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v4 matlab/doc: removed obsolete directory 

git-svn-id: https://www.dynare.org/svn/dynare/dynare_v4@1953 ac1d8469-bf42-47a9-8791-bf33cf982152
time-shift
sebastien 2008-07-11 14:13:28 +00:00
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matlab/doc/CheckPath.html
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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"
"http://www.w3.org/TR/REC-html40/loose.dtd">
<html>
<head>
<title>Description of CheckPath</title>
<meta name="keywords" content="CheckPath">
<meta name="description" content="06-03-2005">
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
<meta name="generator" content="m2html &copy; 2003 Guillaume Flandin">
<meta name="robots" content="index, follow">
<link type="text/css" rel="stylesheet" href="../m2html.css">
</head>
<body>
<a name="_top"></a>
<div><a href="../index.html">Home</a> &gt; <a href="index.html">.</a> &gt; CheckPath.m</div>
<!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png">&nbsp;Master index</a></td>
<td align="right"><a href="index.html">Index for .&nbsp;<img alt=">" border="0" src="../right.png"></a></td></tr></table>-->
<h1>CheckPath
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>06-03-2005</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function DirectoryName = CheckPath(type) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> 06-03-2005</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="CutSample.html" class="code" title="function CutSample()">CutSample</a> stephane.adjemian@ens.fr [09-09-2005]</li><li><a href="GetAllPosteriorDraws.html" class="code" title="function Draws = GetAllPosteriorDraws(column,FirstMhFile,FirstLine,TotalNumberOfMhFile,NumberOfDraws)">GetAllPosteriorDraws</a> stephane.adjemian@ens.fr [09-09-2005]</li><li><a href="GetPosteriorParametersStatistics.html" class="code" title="function GetPosteriorParametersStatistics()">GetPosteriorParametersStatistics</a> stephane.adjemian@ens.fr [09-09-2005]</li><li><a href="McMCDiagnostics.html" class="code" title="function McmcDiagnostic">McMCDiagnostics</a> stephane.adjemian@ens.fr [06-04-2005]</li><li><a href="PlotPosteriorDistributions.html" class="code" title="function PlotPosteriorDistributions()">PlotPosteriorDistributions</a> stephane.adjemian@ens.fr [09-09-2005]</li><li><a href="PosteriorIRF.html" class="code" title="function PosteriorIRF(type)">PosteriorIRF</a> stephane.adjemian@ens.fr [09-25-2005]</li><li><a href="PosteriorSmoother.html" class="code" title="function PosteriorSmoother(Y,gend, type)">PosteriorSmoother</a> stephane.adjemian@ens.fr [09-25-2005]</li><li><a href="ReshapeMatFiles.html" class="code" title="function ReshapeMatFiles(type)">ReshapeMatFiles</a> Reshape and sort (along the mcmc simulations) the mat files generated</li><li><a href="dynare_estimation.html" class="code" title="function dynare_estimation(var_list_)">dynare_estimation</a> </li><li><a href="dynare_sensitivity.html" class="code" title="function x0=dynare_sensitivity()">dynare_sensitivity</a> copyright Marco Ratto 2006</li><li><a href="filt_mc_.html" class="code" title="function [rmse_MC, ixx] = filt_mc_(vvarvecm, loadSA, pfilt, alpha, alpha2, OutDir, istart, alphaPC)">filt_mc_</a> copyright Marco Ratto 2006</li><li><a href="marginal_density.html" class="code" title="function marginal = marginal_density()">marginal_density</a> stephane.adjemian@ens.fr [09-09-2005]</li><li><a href="metropolis.html" class="code" title="function metropolis(xparam1,vv,gend,data,rawdata,mh_bounds)">metropolis</a> stephane.adjemian@ens.fr [09-02-2005]</li><li><a href="metropolis_draw.html" class="code" title="function [xparams, logpost]=metropolis_draw(init)">metropolis_draw</a> </li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function DirectoryName = CheckPath(type)</a>
0002 <span class="comment">% 06-03-2005</span>
0003 <span class="keyword">global</span> M_
0004
0005 DirectoryName = [ M_.dname <span class="string">'/'</span> type ];
0006
0007 <span class="keyword">if</span> ~isdir(DirectoryName)
0008 mkdir(<span class="string">'.'</span>,DirectoryName);
0009 <span class="keyword">end</span></pre></div>
<hr><address>Generated on Fri 16-Jun-2006 09:09:06 by <strong><a href="http://www.artefact.tk/software/matlab/m2html/">m2html</a></strong> &copy; 2003</address>
</body>
</html>

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matlab/doc/CreateBenchmark.html
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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"
"http://www.w3.org/TR/REC-html40/loose.dtd">
<html>
<head>
<title>Description of CreateBenchmark</title>
<meta name="keywords" content="CreateBenchmark">
<meta name="description" content="stephane.adjemian@cepremap.cnrs.fr [12-06-2004]">
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
<meta name="generator" content="m2html &copy; 2003 Guillaume Flandin">
<meta name="robots" content="index, follow">
<link type="text/css" rel="stylesheet" href="../m2html.css">
</head>
<body>
<a name="_top"></a>
<div><a href="../index.html">Home</a> &gt; <a href="index.html">.</a> &gt; CreateBenchmark.m</div>
<!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png">&nbsp;Master index</a></td>
<td align="right"><a href="index.html">Index for .&nbsp;<img alt=">" border="0" src="../right.png"></a></td></tr></table>-->
<h1>CreateBenchmark
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>stephane.adjemian@cepremap.cnrs.fr [12-06-2004]</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function CreateBenchmark </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> stephane.adjemian@cepremap.cnrs.fr [12-06-2004]</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function CreateBenchmark</a>
0002
0003 <span class="comment">% stephane.adjemian@cepremap.cnrs.fr [12-06-2004]</span>
0004
0005 <span class="keyword">global</span> oo_
0006
0007 eval([M_.fname <span class="string">'_oo_ = oo_;'</span>])
0008 eval([<span class="string">'save '</span> M_.fname <span class="string">'_benchmark_oo '</span> M_.fname <span class="string">'_oo_'</span>])</pre></div>
<hr><address>Generated on Fri 16-Jun-2006 09:09:06 by <strong><a href="http://www.artefact.tk/software/matlab/m2html/">m2html</a></strong> &copy; 2003</address>
</body>
</html>

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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"
"http://www.w3.org/TR/REC-html40/loose.dtd">
<html>
<head>
<title>Description of CutSample</title>
<meta name="keywords" content="CutSample">
<meta name="description" content="stephane.adjemian@ens.fr [09-09-2005]">
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
<meta name="generator" content="m2html &copy; 2003 Guillaume Flandin">
<meta name="robots" content="index, follow">
<link type="text/css" rel="stylesheet" href="../m2html.css">
</head>
<body>
<a name="_top"></a>
<div><a href="../index.html">Home</a> &gt; <a href="index.html">.</a> &gt; CutSample.m</div>
<!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png">&nbsp;Master index</a></td>
<td align="right"><a href="index.html">Index for .&nbsp;<img alt=">" border="0" src="../right.png"></a></td></tr></table>-->
<h1>CutSample
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>stephane.adjemian@ens.fr [09-09-2005]</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function CutSample() </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> stephane.adjemian@ens.fr [09-09-2005]</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="CheckPath.html" class="code" title="function DirectoryName = CheckPath(type)">CheckPath</a> 06-03-2005</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="dynare_estimation.html" class="code" title="function dynare_estimation(var_list_)">dynare_estimation</a> </li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function CutSample()</a>
0002 <span class="comment">% stephane.adjemian@ens.fr [09-09-2005]</span>
0003 <span class="keyword">global</span> M_ options_ estim_params_
0004
0005 npar = estim_params_.np+estim_params_.nvn+estim_params_.ncx+estim_params_.ncn+estim_params_.nvx;
0006
0007 DirectoryName = <a href="CheckPath.html" class="code" title="function DirectoryName = CheckPath(type)">CheckPath</a>(<span class="string">'metropolis'</span>);
0008 file = dir([ DirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_mh_history.mat'</span>]);
0009 files = dir([ DirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_mh*.mat'</span> ]);
0010 <span class="keyword">if</span> ~length(files)
0011 disp(<span class="string">'MH:: FAILURE! there is no MH file to load here!'</span>)
0012 <span class="keyword">return</span>
0013 <span class="keyword">end</span>
0014 <span class="keyword">if</span> ~length(file)
0015 disp(<span class="string">'MH:: FAILURE! there is no MH-history file!'</span>)
0016 <span class="keyword">return</span>
0017 <span class="keyword">else</span>
0018 load([ DirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_mh_history'</span>])
0019 <span class="keyword">end</span>
0020 TotalNumberOfMhFiles = sum(record.MhDraws(:,2));
0021 TotalNumberOfMhDraws = sum(record.MhDraws(:,1));
0022 MAX_nruns = ceil(options_.MaxNumberOfBytes/(npar+2)/8)
0023 FirstDraw = floor(options_.mh_drop*TotalNumberOfMhDraws);
0024 FirstMhFile = ceil(FirstDraw/MAX_nruns);
0025 FirstLine = FirstDraw-(FirstMhFile-1)*MAX_nruns+1;
0026 record.KeepedDraws.FirstMhFile = FirstMhFile;
0027 record.KeepedDraws.FirstLine = FirstLine;
0028 <span class="keyword">if</span> (TotalNumberOfMhFiles-1)-(FirstMhFile+1)+1 &gt; 0
0029 record.KeepedDraws.Distribution = [ MAX_nruns-FirstLine+1 ; <span class="keyword">...</span>
0030 ones((TotalNumberOfMhFiles-1)-(FirstMhFile+1)+1,1)*MAX_nruns ; <span class="keyword">...</span>
0031 record.MhDraws(<span class="keyword">end</span>,3) ];
0032 <span class="keyword">elseif</span> TotalNumberOfMhFiles == 1
0033 record.KeepedDraws.Distribution = [];
0034 <span class="keyword">elseif</span> TotalNumberOfMhFiles == 2 &amp; FirstMhFile &gt; 1
0035 record.KeepedDraws.Distribution = [MAX_nruns-FirstLine+1 ; record.MhDraws(<span class="keyword">end</span>,3)];
0036 <span class="keyword">end</span>
0037 save([DirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_mh_history'</span>],<span class="string">'record'</span>);
0038 fprintf(<span class="string">'MH: Total number of Mh draws: %d.\n'</span>,TotalNumberOfMhDraws);
0039 fprintf(<span class="string">'MH: Total number of generated Mh files: %d.\n'</span>,TotalNumberOfMhFiles);
0040 fprintf(<span class="string">'MH: I''ll use mh-files %d to %d.\n'</span>,FirstMhFile,TotalNumberOfMhFiles);
0041 fprintf(<span class="string">'MH: In mh-file number %d i''ll start at line %d.\n'</span>,FirstMhFile,FirstLine);
0042 fprintf(<span class="string">'MH: Finally I keep %d draws.\n'</span>,TotalNumberOfMhDraws-FirstDraw);
0043 disp(<span class="string">' '</span>);</pre></div>
<hr><address>Generated on Fri 16-Jun-2006 09:09:06 by <strong><a href="http://www.artefact.tk/software/matlab/m2html/">m2html</a></strong> &copy; 2003</address>
</body>
</html>

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matlab/doc/DiffuseKalmanSmoother1.html
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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"
"http://www.w3.org/TR/REC-html40/loose.dtd">
<html>
<head>
<title>Description of DiffuseKalmanSmoother1</title>
<meta name="keywords" content="DiffuseKalmanSmoother1">
<meta name="description" content="modified by M. Ratto:">
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
<meta name="generator" content="m2html &copy; 2003 Guillaume Flandin">
<meta name="robots" content="index, follow">
<link type="text/css" rel="stylesheet" href="../m2html.css">
</head>
<body>
<a name="_top"></a>
<div><a href="../index.html">Home</a> &gt; <a href="index.html">.</a> &gt; DiffuseKalmanSmoother1.m</div>
<!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png">&nbsp;Master index</a></td>
<td align="right"><a href="index.html">Index for .&nbsp;<img alt=">" border="0" src="../right.png"></a></td></tr></table>-->
<h1>DiffuseKalmanSmoother1
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>modified by M. Ratto:</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function [alphahat,etahat,a, aK] = DiffuseKalmanSmoother1(T,R,Q,Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> modified by M. Ratto:
new output argument aK (1-step to k-step predictions)
new options_.nk: the max step ahed prediction in aK (default is 4)
new crit1 value for rank of Pinf
it is assured that P is symmetric
stephane.adjemian@cepremap.cnrs.fr [09-16-2004]
See &quot;Filtering and Smoothing of State Vector for Diffuse State Space
Models&quot;, S.J. Koopman and J. Durbin (2003, in Journal of Time Series
Analysis, vol. 24(1), pp. 85-98).</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="steady.html" class="code" title="function steady(linear)">steady</a> Copyright (C) 2001 Michel Juillard</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="DsgeSmoother.html" class="code" title="function [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK] = DsgeSmoother(xparam1,gend,Y)">DsgeSmoother</a> stephane.adjemian@cepremap.cnrs.fr [09-07-2004]</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function [alphahat,etahat,a, aK] = DiffuseKalmanSmoother1(T,R,Q,Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf)</a>
0002 <span class="comment">% modified by M. Ratto:</span>
0003 <span class="comment">% new output argument aK (1-step to k-step predictions)</span>
0004 <span class="comment">% new options_.nk: the max step ahed prediction in aK (default is 4)</span>
0005 <span class="comment">% new crit1 value for rank of Pinf</span>
0006 <span class="comment">% it is assured that P is symmetric</span>
0007 <span class="comment">%</span>
0008 <span class="comment">% stephane.adjemian@cepremap.cnrs.fr [09-16-2004]</span>
0009 <span class="comment">%</span>
0010 <span class="comment">% See &quot;Filtering and Smoothing of State Vector for Diffuse State Space</span>
0011 <span class="comment">% Models&quot;, S.J. Koopman and J. Durbin (2003, in Journal of Time Series</span>
0012 <span class="comment">% Analysis, vol. 24(1), pp. 85-98).</span>
0013
0014 <span class="keyword">global</span> options_
0015
0016 nk = options_.nk;
0017 spinf = size(Pinf1);
0018 spstar = size(Pstar1);
0019 v = zeros(pp,smpl);
0020 a = zeros(mm,smpl+1);
0021 aK = zeros(nk,mm,smpl+1);
0022 iF = zeros(pp,pp,smpl);
0023 Fstar = zeros(pp,pp,smpl);
0024 iFinf = zeros(pp,pp,smpl);
0025 K = zeros(mm,pp,smpl);
0026 L = zeros(mm,mm,smpl);
0027 Linf = zeros(mm,mm,smpl);
0028 Kstar = zeros(mm,pp,smpl);
0029 P = zeros(mm,mm,smpl+1);
0030 Pstar = zeros(spstar(1),spstar(2),smpl+1); Pstar(:,:,1) = Pstar1;
0031 Pinf = zeros(spinf(1),spinf(2),smpl+1); Pinf(:,:,1) = Pinf1;
0032 crit = options_.kalman_tol;
0033 crit1 = 1.e-8;
0034 <a href="steady.html" class="code" title="function steady(linear)">steady</a> = smpl;
0035 rr = size(Q,1);
0036 QQ = R*Q*transpose(R);
0037 QRt = Q*transpose(R);
0038 alphahat = zeros(mm,smpl);
0039 etahat = zeros(rr,smpl);
0040 r = zeros(mm,smpl);
0041
0042 Z = zeros(pp,mm);
0043 <span class="keyword">for</span> i=1:pp;
0044 Z(i,mf(i)) = 1;
0045 <span class="keyword">end</span>
0046
0047 t = 0;
0048 <span class="keyword">while</span> rank(Pinf(:,:,t+1),crit1) &amp; t&lt;smpl
0049 t = t+1;
0050 v(:,t) = Y(:,t) - a(mf,t) - trend(:,t);
0051 <span class="keyword">if</span> rcond(Pinf(mf,mf,t)) &lt; crit
0052 <span class="keyword">return</span>
0053 <span class="keyword">end</span>
0054 iFinf(:,:,t) = inv(Pinf(mf,mf,t));
0055 Kinf(:,:,t) = T*Pinf(:,mf,t)*iFinf(:,:,t);
0056 a(:,t+1) = T*a(:,t) + Kinf(:,:,t)*v(:,t);
0057 aK(1,:,t+1) = a(:,t+1);
0058 <span class="keyword">for</span> jnk=2:nk,
0059 aK(jnk,:,t+jnk) = T^(jnk-1)*a(:,t+1);
0060 <span class="keyword">end</span>
0061 Linf(:,:,t) = T - Kinf(:,:,t)*Z;
0062 Fstar(:,:,t) = Pstar(mf,mf,t);
0063 Kstar(:,:,t) = (T*Pstar(:,mf,t)-Kinf(:,:,t)*Fstar(:,:,t))*iFinf(:,:,t);
0064 Pstar(:,:,t+1) = T*Pstar(:,:,t)*transpose(T)-T*Pstar(:,mf,t)*transpose(Kinf(:,:,t))-Kinf(:,:,t)*Pinf(mf,mf,t)*transpose(Kstar(:,:,t)) + QQ;
0065 Pinf(:,:,t+1) = T*Pinf(:,:,t)*transpose(T)-T*Pinf(:,mf,t)*transpose(Kinf(:,:,t));
0066 <span class="keyword">end</span>
0067 d = t;
0068 P(:,:,d+1) = Pstar(:,:,d+1);
0069 iFinf = iFinf(:,:,1:d);
0070 Linf = Linf(:,:,1:d);
0071 Fstar = Fstar(:,:,1:d);
0072 Kstar = Kstar(:,:,1:d);
0073 Pstar = Pstar(:,:,1:d);
0074 Pinf = Pinf(:,:,1:d);
0075 notsteady = 1;
0076 <span class="keyword">while</span> notsteady &amp; t&lt;smpl
0077 t = t+1;
0078 v(:,t) = Y(:,t) - a(mf,t) - trend(:,t);
0079 P(:,:,t)=tril(P(:,:,t))+transpose(tril(P(:,:,t),-1));
0080 <span class="keyword">if</span> rcond(P(mf,mf,t)) &lt; crit
0081 <span class="keyword">return</span>
0082 <span class="keyword">end</span>
0083 iF(:,:,t) = inv(P(mf,mf,t));
0084 K(:,:,t) = T*P(:,mf,t)*iF(:,:,t);
0085 L(:,:,t) = T-K(:,:,t)*Z;
0086 a(:,t+1) = T*a(:,t) + K(:,:,t)*v(:,t);
0087 aK(1,:,t+1) = a(:,t+1);
0088 <span class="keyword">for</span> jnk=2:nk,
0089 aK(jnk,:,t+jnk) = T^(jnk-1)*a(:,t+1);
0090 <span class="keyword">end</span>
0091 P(:,:,t+1) = T*P(:,:,t)*transpose(T)-T*P(:,mf,t)*transpose(K(:,:,t)) + QQ;
0092 notsteady = ~(max(max(abs(P(:,:,t+1)-P(:,:,t))))&lt;crit);
0093 <span class="keyword">end</span>
0094 K_s = K(:,:,t);
0095 iF_s = iF(:,:,t);
0096 P_s = P(:,:,t+1);
0097 <span class="keyword">if</span> t&lt;smpl
0098 t_steady = t+1;
0099 P = cat(3,P(:,:,1:t),repmat(P(:,:,t),[1 1 smpl-t_steady+1]));
0100 iF = cat(3,iF(:,:,1:t),repmat(inv(P_s(mf,mf)),[1 1 smpl-t_steady+1]));
0101 L = cat(3,L(:,:,1:t),repmat(T-K_s*Z,[1 1 smpl-t_steady+1]));
0102 K = cat(3,K(:,:,1:t),repmat(T*P_s(:,mf)*iF_s,[1 1 smpl-t_steady+1]));
0103 <span class="keyword">end</span>
0104 <span class="keyword">while</span> t&lt;smpl
0105 t=t+1;
0106 v(:,t) = Y(:,t) - a(mf,t) - trend(:,t);
0107 a(:,t+1) = T*a(:,t) + K_s*v(:,t);
0108 aK(1,:,t+1) = a(:,t+1);
0109 <span class="keyword">for</span> jnk=2:nk,
0110 aK(jnk,:,t+jnk) = T^(jnk-1)*a(:,t+1);
0111 <span class="keyword">end</span>
0112 <span class="keyword">end</span>
0113 t = smpl+1;
0114 <span class="keyword">while</span> t&gt;d+1 &amp; t&gt;2
0115 t = t-1;
0116 r(:,t-1) = transpose(Z)*iF(:,:,t)*v(:,t) + transpose(L(:,:,t))*r(:,t);
0117 alphahat(:,t) = a(:,t) + P(:,:,t)*r(:,t-1);
0118 etahat(:,t) = QRt*r(:,t);
0119 <span class="keyword">end</span>
0120 <span class="keyword">if</span> d
0121 r0 = zeros(mm,d); r0(:,d) = r(:,d);
0122 r1 = zeros(mm,d);
0123 <span class="keyword">for</span> t = d:-1:2
0124 r0(:,t-1) = transpose(Linf(:,:,t))*r0(:,t);
0125 r1(:,t-1) = transpose(Z)*(iFinf(:,:,t)*v(:,t)-transpose(Kstar(:,:,t))*r0(:,t)) + transpose(Linf(:,:,t))*r1(:,t);
0126 alphahat(:,t) = a(:,t) + Pstar(:,:,t)*r0(:,t-1) + Pinf(:,:,t)*r1(:,t-1);
0127 etahat(:,t) = QRt*r0(:,t);
0128 <span class="keyword">end</span>
0129 r0_0 = transpose(Linf(:,:,1))*r0(:,1);
0130 r1_0 = transpose(Z)*(iFinf(:,:,1)*v(:,1)-transpose(Kstar(:,:,1))*r0(:,1)) + transpose(Linf(:,:,1))*r1(:,1);
0131 alphahat(:,1) = a(:,1) + Pstar(:,:,1)*r0_0 + Pinf(:,:,1)*r1_0;
0132 etahat(:,1) = QRt*r0(:,1);
0133 <span class="keyword">else</span>
0134 r0 = transpose(Z)*iF(:,:,1)*v(:,1) + transpose(L(:,:,1))*r(:,1);
0135 alphahat(:,1) = a(:,1) + P(:,:,1)*r0;
0136 etahat(:,1) = QRt*r(:,1);
0137 <span class="keyword">end</span></pre></div>
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<h1>DiffuseKalmanSmoother3
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function [a,etahat,a1, aK] = DiffuseKalmanSmoother3(T,R,Q,Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf)</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function [alphahat,etahat,a1, aK] = DiffuseKalmanSmoother3(T,R,Q,Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment">function [a,etahat,a1, aK] = DiffuseKalmanSmoother3(T,R,Q,Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf)
Modified by M. Ratto
New output argument aK: 1-step to nk-stpe ahed predictions)
New input argument nk: max order of predictions in aK
New option options_.diffuse_d where the DKF stops (common with
diffuselikelihood3)
New icc variable to count number of iterations for Finf steps
Pstar % Pinf simmetric
New termination of DKF iterations based on options_.diffuse_d
Li also stored during DKF iterations !!
some bugs corrected in the DKF part of the smoother (Z matrix and
alphahat)
stephane.adjemian@cepremap.cnrs.fr [09-16-2004]
See &quot;Fast Filtering and Smoothing for Multivariate State Space
Models&quot;, S.J. Koopman and J. Durbin (2000, in Journal of Time Series
Analysis, vol. 21(3), pp. 281-296).</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="steady.html" class="code" title="function steady(linear)">steady</a> Copyright (C) 2001 Michel Juillard</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="DsgeSmoother.html" class="code" title="function [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK] = DsgeSmoother(xparam1,gend,Y)">DsgeSmoother</a> stephane.adjemian@cepremap.cnrs.fr [09-07-2004]</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function [alphahat,etahat,a1, aK] = DiffuseKalmanSmoother3(T,R,Q,Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf)</a>
0002 <span class="comment">%function [a,etahat,a1, aK] = DiffuseKalmanSmoother3(T,R,Q,Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf)</span>
0003 <span class="comment">% Modified by M. Ratto</span>
0004 <span class="comment">% New output argument aK: 1-step to nk-stpe ahed predictions)</span>
0005 <span class="comment">% New input argument nk: max order of predictions in aK</span>
0006 <span class="comment">% New option options_.diffuse_d where the DKF stops (common with</span>
0007 <span class="comment">% diffuselikelihood3)</span>
0008 <span class="comment">% New icc variable to count number of iterations for Finf steps</span>
0009 <span class="comment">% Pstar % Pinf simmetric</span>
0010 <span class="comment">% New termination of DKF iterations based on options_.diffuse_d</span>
0011 <span class="comment">% Li also stored during DKF iterations !!</span>
0012 <span class="comment">% some bugs corrected in the DKF part of the smoother (Z matrix and</span>
0013 <span class="comment">% alphahat)</span>
0014 <span class="comment">%</span>
0015 <span class="comment">% stephane.adjemian@cepremap.cnrs.fr [09-16-2004]</span>
0016 <span class="comment">%</span>
0017 <span class="comment">% See &quot;Fast Filtering and Smoothing for Multivariate State Space</span>
0018 <span class="comment">% Models&quot;, S.J. Koopman and J. Durbin (2000, in Journal of Time Series</span>
0019 <span class="comment">% Analysis, vol. 21(3), pp. 281-296).</span>
0020
0021 <span class="keyword">global</span> options_
0022
0023 nk = options_.nk;
0024 spinf = size(Pinf1);
0025 spstar = size(Pstar1);
0026 v = zeros(pp,smpl);
0027 a = zeros(mm,smpl+1);
0028 a1 = a;
0029 aK = zeros(nk,mm,smpl+nk);
0030 Fstar = zeros(pp,smpl);
0031 Finf = zeros(pp,smpl);
0032 Ki = zeros(mm,pp,smpl);
0033 Li = zeros(mm,mm,pp,smpl);
0034 Linf = zeros(mm,mm,pp,smpl);
0035 L0 = zeros(mm,mm,pp,smpl);
0036 Kstar = zeros(mm,pp,smpl);
0037 P = zeros(mm,mm,smpl+1);
0038 P1 = P;
0039 Pstar = zeros(spstar(1),spstar(2),smpl+1); Pstar(:,:,1) = Pstar1;
0040 Pinf = zeros(spinf(1),spinf(2),smpl+1); Pinf(:,:,1) = Pinf1;
0041 Pstar1 = Pstar;
0042 Pinf1 = Pinf;
0043 crit = options_.kalman_tol;
0044 crit1 = 1.e-6;
0045 <a href="steady.html" class="code" title="function steady(linear)">steady</a> = smpl;
0046 rr = size(Q,1);
0047 QQ = R*Q*transpose(R);
0048 QRt = Q*transpose(R);
0049 alphahat = zeros(mm,smpl);
0050 etahat = zeros(rr,smpl);
0051 r = zeros(mm,smpl);
0052
0053 Z = zeros(pp,mm);
0054 <span class="keyword">for</span> i=1:pp;
0055 Z(i,mf(i)) = 1;
0056 <span class="keyword">end</span>
0057
0058 t = 0;
0059 icc=0;
0060 newRank = rank(Pinf(:,:,1),crit1);
0061 <span class="keyword">while</span> newRank &amp; t &lt; smpl
0062 t = t+1;
0063 a1(:,t) = a(:,t);
0064 Pstar(:,:,t)=tril(Pstar(:,:,t))+transpose(tril(Pstar(:,:,t),-1));
0065 Pinf(:,:,t)=tril(Pinf(:,:,t))+transpose(tril(Pinf(:,:,t),-1));
0066 Pstar1(:,:,t) = Pstar(:,:,t);
0067 Pinf1(:,:,t) = Pinf(:,:,t);
0068 <span class="keyword">for</span> i=1:pp
0069 v(i,t) = Y(i,t)-a(mf(i),t)-trend(i,t);
0070 Fstar(i,t) = Pstar(mf(i),mf(i),t);
0071 Finf(i,t) = Pinf(mf(i),mf(i),t);
0072 Kstar(:,i,t) = Pstar(:,mf(i),t);
0073 <span class="keyword">if</span> Finf(i,t) &gt; crit &amp; newRank, <span class="comment">% original MJ: if Finf(i,t) &gt; crit</span>
0074 icc=icc+1;
0075 Kinf(:,i,t) = Pinf(:,mf(i),t);
0076 Linf(:,:,i,t) = eye(mm) - Kinf(:,i,t)*Z(i,:)/Finf(i,t);
0077 L0(:,:,i,t) = (Kinf(:,i,t)*Fstar(i,t)/Finf(i,t) - Kstar(:,i,t))*Z(i,:)/Finf(i,t);
0078 a(:,t) = a(:,t) + Kinf(:,i,t)*v(i,t)/Finf(i,t);
0079 Pstar(:,:,t) = Pstar(:,:,t) + <span class="keyword">...</span>
0080 Kinf(:,i,t)*transpose(Kinf(:,i,t))*Fstar(i,t)/(Finf(i,t)*Finf(i,t)) - <span class="keyword">...</span>
0081 (Kstar(:,i,t)*transpose(Kinf(:,i,t)) +<span class="keyword">...</span>
0082 Kinf(:,i,t)*transpose(Kstar(:,i,t)))/Finf(i,t);
0083 Pinf(:,:,t) = Pinf(:,:,t) - Kinf(:,i,t)*transpose(Kinf(:,i,t))/Finf(i,t);
0084 Pstar(:,:,t)=tril(Pstar(:,:,t))+transpose(tril(Pstar(:,:,t),-1));
0085 Pinf(:,:,t)=tril(Pinf(:,:,t))+transpose(tril(Pinf(:,:,t),-1));
0086 <span class="comment">% new terminiation criteria by M. Ratto</span>
0087 P0=Pinf(:,:,t);
0088 <span class="comment">% newRank = any(diag(P0(mf,mf))&gt;crit);</span>
0089 <span class="comment">% if newRank==0, id = i; end,</span>
0090 <span class="keyword">if</span> ~isempty(options_.diffuse_d),
0091 newRank = (icc&lt;options_.diffuse_d);
0092 <span class="comment">%if newRank &amp; any(diag(P0(mf,mf))&gt;crit)==0;</span>
0093 <span class="keyword">if</span> newRank &amp; (any(diag(P0(mf,mf))&gt;crit)==0 &amp; rank(P0,crit1)==0);
0094 disp(<span class="string">'WARNING!! Change in OPTIONS_.DIFFUSE_D in univariate DKF'</span>)
0095 options_.diffuse_d = icc;
0096 newRank=0;
0097 <span class="keyword">end</span>
0098 <span class="keyword">else</span>
0099 <span class="comment">%newRank = any(diag(P0(mf,mf))&gt;crit);</span>
0100 newRank = (any(diag(P0(mf,mf))&gt;crit) | rank(P0,crit1));
0101 <span class="keyword">if</span> newRank==0,
0102 options_.diffuse_d = icc;
0103 <span class="keyword">end</span>
0104 <span class="keyword">end</span>,
0105 <span class="comment">% if newRank==0,</span>
0106 <span class="comment">% options_.diffuse_d=i; %this is buggy</span>
0107 <span class="comment">% end</span>
0108 <span class="comment">% end new terminiation criteria by M. Ratto</span>
0109 <span class="keyword">else</span>
0110 <span class="comment">%% Note that : (1) rank(Pinf)=0 implies that Finf = 0, (2) outside this loop (when for some i and t the condition</span>
0111 <span class="comment">%% rank(Pinf)=0 is satisfied we have P = Pstar and F = Fstar and (3) Finf = 0 does not imply that</span>
0112 <span class="comment">%% rank(Pinf)=0. [stphane,11-03-2004].</span>
0113 Li(:,:,i,t) = eye(mm)-Kstar(:,i,t)*Z(i,:)/Fstar(i,t); <span class="comment">% we need to store Li for DKF smoother</span>
0114 a(:,t) = a(:,t) + Kstar(:,i,t)*v(i,t)/Fstar(i,t);
0115 Pstar(:,:,t) = Pstar(:,:,t) - Kstar(:,i,t)*transpose(Kstar(:,i,t))/Fstar(i,t);
0116 Pstar(:,:,t)=tril(Pstar(:,:,t))+transpose(tril(Pstar(:,:,t),-1));
0117 <span class="keyword">end</span>
0118 <span class="keyword">end</span>
0119 a(:,t+1) = T*a(:,t);
0120 <span class="keyword">for</span> jnk=1:nk,
0121 aK(jnk,:,t+jnk) = T^jnk*a(:,t);
0122 <span class="keyword">end</span>
0123 Pstar(:,:,t+1) = T*Pstar(:,:,t)*transpose(T)+ QQ;
0124 Pinf(:,:,t+1) = T*Pinf(:,:,t)*transpose(T);
0125 P0=Pinf(:,:,t+1);
0126 <span class="keyword">if</span> newRank,
0127 <span class="comment">%newRank = any(diag(P0(mf,mf))&gt;crit);</span>
0128 newRank = rank(P0,crit1);
0129 <span class="keyword">end</span>
0130 <span class="keyword">end</span>
0131
0132
0133 d = t;
0134 P(:,:,d+1) = Pstar(:,:,d+1);
0135 Linf = Linf(:,:,:,1:d);
0136 L0 = L0(:,:,:,1:d);
0137 Fstar = Fstar(:,1:d);
0138 Finf = Finf(:,1:d);
0139 Kstar = Kstar(:,:,1:d);
0140 Pstar = Pstar(:,:,1:d);
0141 Pinf = Pinf(:,:,1:d);
0142 Pstar1 = Pstar1(:,:,1:d);
0143 Pinf1 = Pinf1(:,:,1:d);
0144 notsteady = 1;
0145 <span class="keyword">while</span> notsteady &amp; t&lt;smpl
0146 t = t+1;
0147 a1(:,t) = a(:,t);
0148 P(:,:,t)=tril(P(:,:,t))+transpose(tril(P(:,:,t),-1));
0149 P1(:,:,t) = P(:,:,t);
0150 <span class="keyword">for</span> i=1:pp
0151 v(i,t) = Y(i,t) - a(mf(i),t) - trend(i,t);
0152 Fi(i,t) = P(mf(i),mf(i),t);
0153 Ki(:,i,t) = P(:,mf(i),t);
0154 <span class="keyword">if</span> Fi(i,t) &gt; crit
0155 Li(:,:,i,t) = eye(mm)-Ki(:,i,t)*Z(i,:)/Fi(i,t);
0156 a(:,t) = a(:,t) + Ki(:,i,t)*v(i,t)/Fi(i,t);
0157 P(:,:,t) = P(:,:,t) - Ki(:,i,t)*transpose(Ki(:,i,t))/Fi(i,t);
0158 P(:,:,t)=tril(P(:,:,t))+transpose(tril(P(:,:,t),-1));
0159 <span class="keyword">end</span>
0160 <span class="keyword">end</span>
0161 a(:,t+1) = T*a(:,t);
0162 <span class="keyword">for</span> jnk=1:nk,
0163 aK(jnk,:,t+jnk) = T^jnk*a(:,t);
0164 <span class="keyword">end</span>
0165 P(:,:,t+1) = T*P(:,:,t)*transpose(T) + QQ;
0166 notsteady = ~(max(max(abs(P(:,:,t+1)-P(:,:,t))))&lt;crit);
0167 <span class="keyword">end</span>
0168 P_s=tril(P(:,:,t))+transpose(tril(P(:,:,t),-1));
0169 Fi_s = Fi(:,t);
0170 Ki_s = Ki(:,:,t);
0171 L_s =Li(:,:,:,t);
0172 <span class="keyword">if</span> t&lt;smpl
0173 t_steady = t+1;
0174 P = cat(3,P(:,:,1:t),repmat(P(:,:,t),[1 1 smpl-t_steady+1]));
0175 Fi = cat(2,Fi(:,1:t),repmat(Fi_s,[1 1 smpl-t_steady+1]));
0176 Li = cat(4,Li(:,:,:,1:t),repmat(L_s,[1 1 smpl-t_steady+1]));
0177 Ki = cat(3,Ki(:,:,1:t),repmat(Ki_s,[1 1 smpl-t_steady+1]));
0178 <span class="keyword">end</span>
0179 <span class="keyword">while</span> t&lt;smpl
0180 t=t+1;
0181 a1(:,t) = a(:,t);
0182 <span class="keyword">for</span> i=1:pp
0183 v(i,t) = Y(i,t) - a(mf(i),t) - trend(i,t);
0184 <span class="keyword">if</span> Fi_s(i) &gt; crit
0185 a(:,t) = a(:,t) + Ki_s(:,i)*v(i,t)/Fi_s(i);
0186 <span class="keyword">end</span>
0187 <span class="keyword">end</span>
0188 a(:,t+1) = T*a(:,t);
0189 <span class="keyword">for</span> jnk=1:nk,
0190 aK(jnk,:,t+jnk) = T^jnk*a(:,t);
0191 <span class="keyword">end</span>
0192 <span class="keyword">end</span>
0193 a1(:,t+1) = a(:,t+1);
0194 ri=r;
0195 t = smpl+1;
0196 <span class="keyword">while</span> t&gt;d+1 &amp; t&gt;2,
0197 t = t-1;
0198 <span class="keyword">for</span> i=pp:-1:1
0199 <span class="keyword">if</span> Fi(i,t) &gt; crit
0200 ri(:,t)=transpose(Z(i,:))/Fi(i,t)*v(i,t)+transpose(Li(:,:,i,t))*ri(:,t);
0201 <span class="keyword">end</span>
0202 <span class="keyword">end</span>
0203 r(:,t-1) = ri(:,t);
0204 alphahat(:,t) = a1(:,t) + P1(:,:,t)*r(:,t-1);
0205 etahat(:,t) = QRt*r(:,t);
0206 ri(:,t-1) = transpose(T)*ri(:,t);
0207 <span class="keyword">end</span>
0208 <span class="keyword">if</span> d
0209 r0 = zeros(mm,d); r0(:,d) = ri(:,d);
0210 r1 = zeros(mm,d);
0211 <span class="keyword">for</span> t = d:-1:2
0212 <span class="keyword">for</span> i=pp:-1:1
0213 <span class="keyword">if</span> Finf(i,t) &gt; crit &amp; ~(t==d &amp; i&gt;options_.diffuse_d), <span class="comment">% use of options_.diffuse_d to be sure of DKF termination</span>
0214 <span class="comment">%r1(:,t) = transpose(Z)*v(:,t)/Finf(i,t) + ... BUG HERE in transpose(Z)</span>
0215 r1(:,t) = transpose(Z(i,:))*v(i,t)/Finf(i,t) + <span class="keyword">...</span>
0216 transpose(L0(:,:,i,t))*r0(:,t) + transpose(Linf(:,:,i,t))*r1(:,t);
0217 r0(:,t) = transpose(Linf(:,:,i,t))*r0(:,t);
0218 <span class="keyword">elseif</span> Fstar(i,t) &gt; crit <span class="comment">% step needed whe Finf == 0</span>
0219 r0(:,t)=transpose(Z(i,:))/Fstar(i,t)*v(i,t)+Li(:,:,i,t)'*r0(:,t);
0220 <span class="keyword">end</span>
0221 <span class="keyword">end</span>
0222 alphahat(:,t) = a1(:,t) + Pstar1(:,:,t)*r0(:,t) + Pinf1(:,:,t)*r1(:,t);
0223 r(:,t-1) = r0(:,t);
0224 etahat(:,t) = QRt*r(:,t);
0225 r0(:,t-1) = transpose(T)*r0(:,t);
0226 r1(:,t-1) = transpose(T)*r1(:,t);
0227 <span class="keyword">end</span>
0228 r0_0 = r0(:,1);
0229 r1_0 = r1(:,1);
0230 <span class="keyword">for</span> i=pp:-1:1
0231 <span class="keyword">if</span> Finf(i,1) &gt; crit,
0232 <span class="comment">%r1_0 = transpose(Z)*v(:,1)/Finf(i,1) + ... %bug with Z here</span>
0233 r1_0 = transpose(Z(i,:))*v(i,1)/Finf(i,1) + <span class="keyword">...</span>
0234 transpose(L0(:,:,i,1))*r0_0 + transpose(Linf(:,:,i,1))*r1_0;
0235 r0_0 = transpose(Linf(:,:,i,1))*r0_0;
0236 <span class="keyword">elseif</span> Fstar(i,1) &gt; crit, <span class="comment">% step needed when Finf=0</span>
0237 r0_0=transpose(Z(i,:))/Fstar(i,1)*v(i,1)+Li(:,:,i,1)'*r0_0;
0238 <span class="keyword">end</span>
0239 <span class="keyword">end</span>
0240 <span class="comment">%alphahat(:,1) = a(:,1) + Pstar(:,:,1)*r0_0 + Pinf(:,:,1)*r1_0; %this line is buggy</span>
0241 alphahat(:,1) = a1(:,1) + Pstar1(:,:,1)*r0_0 + Pinf1(:,:,1)*r1_0;
0242 etahat(:,1) = QRt*r(:,1);
0243 <span class="keyword">else</span>
0244 r0 = ri(:,1);
0245 <span class="keyword">for</span> i=pp:-1:1
0246 <span class="keyword">if</span> Fi(i,1) &gt; crit
0247 r0=transpose(Z(i,:))/Fi(i,1)*v(i,1)+transpose(Li(:,:,i,1))*r0;
0248 <span class="keyword">end</span>
0249 <span class="keyword">end</span>
0250 <span class="comment">%alphahat(:,1) = a(:,1) + P(:,:,1)*r0; % this line is buggy</span>
0251 alphahat(:,1) = a1(:,1) + P1(:,:,1)*r0;
0252 etahat(:,1) = QRt*r(:,1);
0253 <span class="keyword">end</span>
0254
0255 a=a(:,1:end-1);</pre></div>
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<h1>DiffuseKalmanSmootherH1
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>modified by M. Ratto:</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function [alphahat,epsilonhat,etahat,a, aK] = DiffuseKalmanSmootherH1(T,R,Q,H,Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> modified by M. Ratto:
new output argument aK (1-step to k-step predictions)
new options_.nk: the max step ahed prediction in aK (default is 4)
new crit1 value for rank of Pinf
it is assured that P is symmetric
stephane.adjemian@cepremap.cnrs.fr [09-16-2004]
See &quot;Filtering and Smoothing of State Vector for Diffuse State Space
Models&quot;, S.J. Koopman and J. Durbin (2003, in Journal of Time Series
Analysis, vol. 24(1), pp. 85-98).</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="steady.html" class="code" title="function steady(linear)">steady</a> Copyright (C) 2001 Michel Juillard</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="DsgeSmoother.html" class="code" title="function [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK] = DsgeSmoother(xparam1,gend,Y)">DsgeSmoother</a> stephane.adjemian@cepremap.cnrs.fr [09-07-2004]</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function [alphahat,epsilonhat,etahat,a, aK] = DiffuseKalmanSmootherH1(T,R,Q,H,Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf)</a>
0002 <span class="comment">% modified by M. Ratto:</span>
0003 <span class="comment">% new output argument aK (1-step to k-step predictions)</span>
0004 <span class="comment">% new options_.nk: the max step ahed prediction in aK (default is 4)</span>
0005 <span class="comment">% new crit1 value for rank of Pinf</span>
0006 <span class="comment">% it is assured that P is symmetric</span>
0007 <span class="comment">%</span>
0008 <span class="comment">% stephane.adjemian@cepremap.cnrs.fr [09-16-2004]</span>
0009 <span class="comment">%</span>
0010 <span class="comment">% See &quot;Filtering and Smoothing of State Vector for Diffuse State Space</span>
0011 <span class="comment">% Models&quot;, S.J. Koopman and J. Durbin (2003, in Journal of Time Series</span>
0012 <span class="comment">% Analysis, vol. 24(1), pp. 85-98).</span>
0013
0014 <span class="keyword">global</span> options_
0015
0016 nk = options_.nk;
0017 spinf = size(Pinf1);
0018 spstar = size(Pstar1);
0019 v = zeros(pp,smpl);
0020 a = zeros(mm,smpl+1);
0021 iF = zeros(pp,pp,smpl);
0022 Fstar = zeros(pp,pp,smpl);
0023 iFinf = zeros(pp,pp,smpl);
0024 K = zeros(mm,pp,smpl);
0025 L = zeros(mm,mm,smpl);
0026 Linf = zeros(mm,mm,smpl);
0027 Kstar = zeros(mm,pp,smpl);
0028 P = zeros(mm,mm,smpl+1);
0029 Pstar = zeros(spstar(1),spstar(2),smpl+1); Pstar(:,:,1) = Pstar1;
0030 Pinf = zeros(spinf(1),spinf(2),smpl+1); Pinf(:,:,1) = Pinf1;
0031 crit = options_.kalman_tol;
0032 crit1 = 1.e-8;
0033 <a href="steady.html" class="code" title="function steady(linear)">steady</a> = smpl;
0034 rr = size(Q,1);
0035 QQ = R*Q*transpose(R);
0036 QRt = Q*transpose(R);
0037 alphahat = zeros(mm,smpl);
0038 etahat = zeros(rr,smpl);
0039 epsilonhat = zeros(size(Y));
0040 r = zeros(mm,smpl);
0041
0042 Z = zeros(pp,mm);
0043 <span class="keyword">for</span> i=1:pp;
0044 Z(i,mf(i)) = 1;
0045 <span class="keyword">end</span>
0046
0047 t = 0;
0048 <span class="keyword">while</span> rank(Pinf(:,:,t+1),crit1) &amp; t&lt;smpl
0049 t = t+1;
0050 v(:,t) = Y(:,t) - a(mf,t) - trend(:,t);
0051 <span class="keyword">if</span> rcond(Pinf(mf,mf,t)) &lt; crit
0052 <span class="keyword">return</span>
0053 <span class="keyword">end</span>
0054 iFinf(:,:,t) = inv(Pinf(mf,mf,t));
0055 Kinf(:,:,t) = T*Pinf(:,mf,t)*iFinf(:,:,t);
0056 a(:,t+1) = T*a(:,t) + Kinf(:,:,t)*v(:,t);
0057 aK(1,:,t+1) = a(:,t+1);
0058 <span class="keyword">for</span> jnk=2:nk,
0059 aK(jnk,:,t+jnk) = T^(jnk-1)*a(:,t+1);
0060 <span class="keyword">end</span>
0061 Linf(:,:,t) = T - Kinf(:,:,t)*Z;
0062 Fstar(:,:,t) = Pstar(mf,mf,t) + H;
0063 Kstar(:,:,t) = (T*Pstar(:,mf,t)-Kinf(:,:,t)*Fstar(:,:,t))*iFinf(:,:,t);
0064 Pstar(:,:,t+1) = T*Pstar(:,:,t)*transpose(T)-T*Pstar(:,mf,t)*transpose(Kinf(:,:,t))-Kinf(:,:,t)*Pinf(mf,mf,t)*transpose(Kstar(:,:,t)) + QQ;
0065 Pinf(:,:,t+1) = T*Pinf(:,:,t)*transpose(T)-T*Pinf(:,mf,t)*transpose(Kinf(:,:,t));
0066 <span class="keyword">end</span>
0067 d = t;
0068 P(:,:,d+1) = Pstar(:,:,d+1);
0069 iFinf = iFinf(:,:,1:d);
0070 Linf = Linf(:,:,1:d);
0071 Fstar = Fstar(:,:,1:d);
0072 Kstar = Kstar(:,:,1:d);
0073 Pstar = Pstar(:,:,1:d);
0074 Pinf = Pinf(:,:,1:d);
0075 notsteady = 1;
0076 <span class="keyword">while</span> notsteady &amp; t&lt;smpl
0077 t = t+1;
0078 v(:,t) = Y(:,t) - a(mf,t) - trend(:,t);
0079 P(:,:,t)=tril(P(:,:,t))+transpose(tril(P(:,:,t),-1));
0080 <span class="keyword">if</span> rcond(P(mf,mf,t) + H) &lt; crit
0081 <span class="keyword">return</span>
0082 <span class="keyword">end</span>
0083 iF(:,:,t) = inv(P(mf,mf,t) + H);
0084 K(:,:,t) = T*P(:,mf,t)*iF(:,:,t);
0085 L(:,:,t) = T-K(:,:,t)*Z;
0086 a(:,t+1) = T*a(:,t) + K(:,:,t)*v(:,t);
0087 aK(1,:,t+1) = a(:,t+1);
0088 <span class="keyword">for</span> jnk=2:nk,
0089 aK(jnk,:,t+jnk) = T^(jnk-1)*a(:,t+1);
0090 <span class="keyword">end</span>
0091 P(:,:,t+1) = T*P(:,:,t)*transpose(T)-T*P(:,mf,t)*transpose(K(:,:,t)) + QQ;
0092 notsteady = ~(max(max(abs(P(:,:,t+1)-P(:,:,t))))&lt;crit);
0093 <span class="keyword">end</span>
0094 K_s = K(:,:,t);
0095 iF_s = iF(:,:,t);
0096 P_s = P(:,:,t+1);
0097 <span class="keyword">if</span> t&lt;smpl
0098 t_steady = t+1;
0099 P = cat(3,P(:,:,1:t),repmat(P(:,:,t),[1 1 smpl-t_steady+1]));
0100 iF = cat(3,iF(:,:,1:t),repmat(inv(P_s(mf,mf)+H),[1 1 smpl-t_steady+1]));
0101 L = cat(3,L(:,:,1:t),repmat(T-K_s*Z,[1 1 smpl-t_steady+1]));
0102 K = cat(3,K(:,:,1:t),repmat(T*P_s(:,mf)*iF_s,[1 1 smpl-t_steady+1]));
0103 <span class="keyword">end</span>
0104 <span class="keyword">while</span> t&lt;smpl
0105 t=t+1;
0106 v(:,t) = Y(:,t) - a(mf,t) - trend(:,t);
0107 a(:,t+1) = T*a(:,t) + K_s*v(:,t);
0108 aK(1,:,t+1) = a(:,t+1);
0109 <span class="keyword">for</span> jnk=2:nk,
0110 aK(jnk,:,t+jnk) = T^(jnk-1)*a(:,t+1);
0111 <span class="keyword">end</span>
0112 <span class="keyword">end</span>
0113 t = smpl+1;
0114 <span class="keyword">while</span> t&gt;d+1 &amp; t&gt;2
0115 t = t-1;
0116 r(:,t-1) = transpose(Z)*iF(:,:,t)*v(:,t) + transpose(L(:,:,t))*r(:,t);
0117 alphahat(:,t) = a(:,t) + P(:,:,t)*r(:,t-1);
0118 etahat(:,t) = QRt*r(:,t);
0119 <span class="keyword">end</span>
0120 <span class="keyword">if</span> d
0121 r0 = zeros(mm,d); r0(:,d) = r(:,d);
0122 r1 = zeros(mm,d);
0123 <span class="keyword">for</span> t = d:-1:2
0124 r0(:,t-1) = transpose(Linf(:,:,t))*r0(:,t);
0125 r1(:,t-1) = transpose(Z)*(iFinf(:,:,t)*v(:,t)-transpose(Kstar(:,:,t))*r0(:,t)) + transpose(Linf(:,:,t))*r1(:,t);
0126 alphahat(:,t) = a(:,t) + Pstar(:,:,t)*r0(:,t-1) + Pinf(:,:,t)*r1(:,t-1);
0127 etahat(:,t) = QRt*r0(:,t);
0128 <span class="keyword">end</span>
0129 r0_0 = transpose(Linf(:,:,1))*r0(:,1);
0130 r1_0 = transpose(Z)*(iFinf(:,:,1)*v(:,1)-transpose(Kstar(:,:,1))*r0(:,1)) + transpose(Linf(:,:,1))*r1(:,1);
0131 alphahat(:,1) = a(:,1) + Pstar(:,:,1)*r0_0 + Pinf(:,:,1)*r1_0;
0132 etahat(:,1) = QRt*r0(:,1);
0133 <span class="keyword">else</span>
0134 r0 = transpose(Z)*iF(:,:,1)*v(:,1) + transpose(L(:,:,1))*r(:,1);
0135 alphahat(:,1) = a(:,1) + P(:,:,1)*r0;
0136 etahat(:,1) = QRt*r(:,1);
0137 <span class="keyword">end</span>
0138 epsilonhat = Y-alphahat(mf,:)-trend;</pre></div>
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<meta name="description" content="Modified by M. Ratto">
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<h1>DiffuseKalmanSmootherH3
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>Modified by M. Ratto</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function [alphahat,epsilonhat,etahat,a1, aK] = DiffuseKalmanSmootherH3(T,R,Q,H,Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> Modified by M. Ratto
New output argument aK: 1-step to nk-stpe ahed predictions)
New input argument nk: max order of predictions in aK
New global variable id_ where the DKF stops (common with
diffuselikelihood3)
New icc variable to count number of iterations for Finf steps
Pstar % Pinf simmetric
New termination of DKF iterations based on id_
Li also stored during DKF iterations !!
some bugs corrected in the DKF part of the smoother (Z matrix and
alphahat)
stephane.adjemian@cepremap.cnrs.fr [09-16-2004]
See &quot;Fast Filtering and Smoothing for Multivariate State Space
Models&quot;, S.J. Koopman and J. Durbin (2000, in Journal of Time Series
Analysis, vol. 21(3), pp. 281-296).</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="steady.html" class="code" title="function steady(linear)">steady</a> Copyright (C) 2001 Michel Juillard</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="DsgeSmoother.html" class="code" title="function [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK] = DsgeSmoother(xparam1,gend,Y)">DsgeSmoother</a> stephane.adjemian@cepremap.cnrs.fr [09-07-2004]</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function [alphahat,epsilonhat,etahat,a1, aK] = DiffuseKalmanSmootherH3(T,R,Q,H,Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf)</a>
0002 <span class="comment">% Modified by M. Ratto</span>
0003 <span class="comment">% New output argument aK: 1-step to nk-stpe ahed predictions)</span>
0004 <span class="comment">% New input argument nk: max order of predictions in aK</span>
0005 <span class="comment">% New global variable id_ where the DKF stops (common with</span>
0006 <span class="comment">% diffuselikelihood3)</span>
0007 <span class="comment">% New icc variable to count number of iterations for Finf steps</span>
0008 <span class="comment">% Pstar % Pinf simmetric</span>
0009 <span class="comment">% New termination of DKF iterations based on id_</span>
0010 <span class="comment">% Li also stored during DKF iterations !!</span>
0011 <span class="comment">% some bugs corrected in the DKF part of the smoother (Z matrix and</span>
0012 <span class="comment">% alphahat)</span>
0013 <span class="comment">%</span>
0014 <span class="comment">% stephane.adjemian@cepremap.cnrs.fr [09-16-2004]</span>
0015 <span class="comment">%</span>
0016 <span class="comment">% See &quot;Fast Filtering and Smoothing for Multivariate State Space</span>
0017 <span class="comment">% Models&quot;, S.J. Koopman and J. Durbin (2000, in Journal of Time Series</span>
0018 <span class="comment">% Analysis, vol. 21(3), pp. 281-296).</span>
0019
0020 <span class="keyword">global</span> options_
0021
0022 nk = options_.nk;
0023 spinf = size(Pinf1);
0024 spstar = size(Pstar1);
0025 v = zeros(pp,smpl);
0026 a = zeros(mm,smpl+1);
0027 a1 = a;
0028 aK = zeros(nk,mm,smpl+nk);
0029 Fstar = zeros(pp,smpl);
0030 Finf = zeros(pp,smpl);
0031 Ki = zeros(mm,pp,smpl);
0032 Li = zeros(mm,mm,pp,smpl);
0033 Linf = zeros(mm,mm,pp,smpl);
0034 L0 = zeros(mm,mm,pp,smpl);
0035 Kstar = zeros(mm,pp,smpl);
0036 P = zeros(mm,mm,smpl+1);
0037 P1 = P;
0038 Pstar = zeros(spstar(1),spstar(2),smpl+1); Pstar(:,:,1) = Pstar1;
0039 Pinf = zeros(spinf(1),spinf(2),smpl+1); Pinf(:,:,1) = Pinf1;
0040 Pstar1 = Pstar;
0041 Pinf1 = Pinf;
0042 crit = options_.kalman_tol;
0043 crit1 = 1.e-6;
0044 <a href="steady.html" class="code" title="function steady(linear)">steady</a> = smpl;
0045 rr = size(Q,1);
0046 QQ = R*Q*transpose(R);
0047 QRt = Q*transpose(R);
0048 alphahat = zeros(mm,smpl);
0049 etahat = zeros(rr,smpl);
0050 epsilonhat = zeros(size(Y));
0051 r = zeros(mm,smpl);
0052
0053 Z = zeros(pp,mm);
0054 <span class="keyword">for</span> i=1:pp;
0055 Z(i,mf(i)) = 1;
0056 <span class="keyword">end</span>
0057
0058 t = 0;
0059 icc=0;
0060 newRank = rank(Pinf(:,:,1),crit1);
0061 <span class="keyword">while</span> newRank &amp; t &lt; smpl
0062 t = t+1;
0063 a1(:,t) = a(:,t);
0064 Pstar(:,:,t)=tril(Pstar(:,:,t))+transpose(tril(Pstar(:,:,t),-1));
0065 Pinf(:,:,t)=tril(Pinf(:,:,t))+transpose(tril(Pinf(:,:,t),-1));
0066 Pstar1(:,:,t) = Pstar(:,:,t);
0067 Pinf1(:,:,t) = Pinf(:,:,t);
0068 <span class="keyword">for</span> i=1:pp
0069 v(i,t) = Y(i,t)-a(mf(i),t)-trend(i,t);
0070 Fstar(i,t) = Pstar(mf(i),mf(i),t) + H(i,i);
0071 Finf(i,t) = Pinf(mf(i),mf(i),t);
0072 Kstar(:,i,t) = Pstar(:,mf(i),t);
0073 <span class="keyword">if</span> Finf(i,t) &gt; crit &amp; newRank, <span class="comment">% original MJ: if Finf(i,t) &gt; crit</span>
0074 icc=icc+1;
0075 Kinf(:,i,t) = Pinf(:,mf(i),t);
0076 Linf(:,:,i,t) = eye(mm) - Kinf(:,i,t)*Z(i,:)/Finf(i,t);
0077 L0(:,:,i,t) = (Kinf(:,i,t)*Fstar(i,t)/Finf(i,t) - Kstar(:,i,t))*Z(i,:)/Finf(i,t);
0078 a(:,t) = a(:,t) + Kinf(:,i,t)*v(i,t)/Finf(i,t);
0079 Pstar(:,:,t) = Pstar(:,:,t) + <span class="keyword">...</span>
0080 Kinf(:,i,t)*transpose(Kinf(:,i,t))*Fstar(i,t)/(Finf(i,t)*Finf(i,t)) - <span class="keyword">...</span>
0081 (Kstar(:,i,t)*transpose(Kinf(:,i,t)) +<span class="keyword">...</span>
0082 Kinf(:,i,t)*transpose(Kstar(:,i,t)))/Finf(i,t);
0083 Pinf(:,:,t) = Pinf(:,:,t) - Kinf(:,i,t)*transpose(Kinf(:,i,t))/Finf(i,t);
0084 Pstar(:,:,t)=tril(Pstar(:,:,t))+transpose(tril(Pstar(:,:,t),-1));
0085 Pinf(:,:,t)=tril(Pinf(:,:,t))+transpose(tril(Pinf(:,:,t),-1));
0086 <span class="comment">% new terminiation criteria by M. Ratto</span>
0087 P0=Pinf(:,:,t);
0088 <span class="comment">% newRank = any(diag(P0(mf,mf))&gt;crit);</span>
0089 <span class="comment">% if newRank==0, options_.diffuse_d = i; end,</span>
0090 <span class="keyword">if</span> ~isempty(options_.diffuse_d),
0091 newRank = (icc&lt;options_.diffuse_d);
0092 <span class="comment">%if newRank &amp; any(diag(P0(mf,mf))&gt;crit)==0;</span>
0093 <span class="keyword">if</span> newRank &amp; (any(diag(P0(mf,mf))&gt;crit)==0 &amp; rank(P0,crit1)==0);
0094 disp(<span class="string">'WARNING!! Change in OPTIONS_.DIFFUSE_D in univariate DKF'</span>)
0095 options_.diffuse_d = icc;
0096 newRank=0;
0097 <span class="keyword">end</span>
0098 <span class="keyword">else</span>
0099 <span class="comment">%newRank = any(diag(P0(mf,mf))&gt;crit);</span>
0100 newRank = (any(diag(P0(mf,mf))&gt;crit) | rank(P0,crit1));
0101 <span class="keyword">if</span> newRank==0,
0102 options_.diffuse_d = icc;
0103 <span class="keyword">end</span>
0104 <span class="keyword">end</span>,
0105 <span class="comment">% if newRank==0,</span>
0106 <span class="comment">% options_.diffuse_d=i; % this line is buggy!</span>
0107 <span class="comment">% end</span>
0108 <span class="comment">% end new terminiation criteria by M. Ratto</span>
0109 <span class="keyword">else</span>
0110 <span class="comment">%% Note that : (1) rank(Pinf)=0 implies that Finf = 0, (2) outside this loop (when for some i and t the condition</span>
0111 <span class="comment">%% rank(Pinf)=0 is satisfied we have P = Pstar and F = Fstar and (3) Finf = 0 does not imply that</span>
0112 <span class="comment">%% rank(Pinf)=0. [stphane,11-03-2004].</span>
0113 Li(:,:,i,t) = eye(mm)-Kstar(:,i,t)*Z(i,:)/Fstar(i,t); <span class="comment">% we need to store Li for DKF smoother</span>
0114 a(:,t) = a(:,t) + Kstar(:,i,t)*v(i,t)/Fstar(i,t);
0115 Pstar(:,:,t) = Pstar(:,:,t) - Kstar(:,i,t)*transpose(Kstar(:,i,t))/Fstar(i,t);
0116 Pstar(:,:,t)=tril(Pstar(:,:,t))+transpose(tril(Pstar(:,:,t),-1));
0117 <span class="keyword">end</span>
0118 <span class="keyword">end</span>
0119 a(:,t+1) = T*a(:,t);
0120 <span class="keyword">for</span> jnk=1:nk,
0121 aK(jnk,:,t+jnk) = T^jnk*a(:,t);
0122 <span class="keyword">end</span>
0123 Pstar(:,:,t+1) = T*Pstar(:,:,t)*transpose(T)+ QQ;
0124 Pinf(:,:,t+1) = T*Pinf(:,:,t)*transpose(T);
0125 P0=Pinf(:,:,t+1);
0126 <span class="keyword">if</span> newRank,
0127 <span class="comment">%newRank = any(diag(P0(mf,mf))&gt;crit);</span>
0128 newRank = rank(P0,crit1);
0129 <span class="keyword">end</span>
0130 <span class="keyword">end</span>
0131
0132
0133 d = t;
0134 P(:,:,d+1) = Pstar(:,:,d+1);
0135 Linf = Linf(:,:,:,1:d);
0136 L0 = L0(:,:,:,1:d);
0137 Fstar = Fstar(:,1:d);
0138 Finf = Finf(:,1:d);
0139 Kstar = Kstar(:,:,1:d);
0140 Pstar = Pstar(:,:,1:d);
0141 Pinf = Pinf(:,:,1:d);
0142 Pstar1 = Pstar1(:,:,1:d);
0143 Pinf1 = Pinf1(:,:,1:d);
0144 notsteady = 1;
0145 <span class="keyword">while</span> notsteady &amp; t&lt;smpl
0146 t = t+1;
0147 a1(:,t) = a(:,t);
0148 P(:,:,t)=tril(P(:,:,t))+transpose(tril(P(:,:,t),-1));
0149 P1(:,:,t) = P(:,:,t);
0150 <span class="keyword">for</span> i=1:pp
0151 v(i,t) = Y(i,t) - a(mf(i),t) - trend(i,t);
0152 Fi(i,t) = P(mf(i),mf(i),t);
0153 Ki(:,i,t) = P(:,mf(i),t) + H(i,i);
0154 <span class="keyword">if</span> Fi(i,t) &gt; crit
0155 Li(:,:,i,t) = eye(mm)-Ki(:,i,t)*Z(i,:)/Fi(i,t);
0156 a(:,t) = a(:,t) + Ki(:,i,t)*v(i,t)/Fi(i,t);
0157 P(:,:,t) = P(:,:,t) - Ki(:,i,t)*transpose(Ki(:,i,t))/Fi(i,t);
0158 P(:,:,t)=tril(P(:,:,t))+transpose(tril(P(:,:,t),-1));
0159 <span class="keyword">end</span>
0160 <span class="keyword">end</span>
0161 a(:,t+1) = T*a(:,t);
0162 <span class="keyword">for</span> jnk=1:nk,
0163 aK(jnk,:,t+jnk) = T^jnk*a(:,t);
0164 <span class="keyword">end</span>
0165 P(:,:,t+1) = T*P(:,:,t)*transpose(T) + QQ;
0166 notsteady = ~(max(max(abs(P(:,:,t+1)-P(:,:,t))))&lt;crit);
0167 <span class="keyword">end</span>
0168 P_s=tril(P(:,:,t))+transpose(tril(P(:,:,t),-1));
0169 Fi_s = Fi(:,t);
0170 Ki_s = Ki(:,:,t);
0171 L_s =Li(:,:,:,t);
0172 <span class="keyword">if</span> t&lt;smpl
0173 t_steady = t+1;
0174 P = cat(3,P(:,:,1:t),repmat(P(:,:,t),[1 1 smpl-t_steady+1]));
0175 Fi = cat(2,Fi(:,1:t),repmat(Fi_s,[1 1 smpl-t_steady+1]));
0176 Li = cat(4,Li(:,:,:,1:t),repmat(L_s,[1 1 smpl-t_steady+1]));
0177 Ki = cat(3,Ki(:,:,1:t),repmat(Ki_s,[1 1 smpl-t_steady+1]));
0178 <span class="keyword">end</span>
0179 <span class="keyword">while</span> t&lt;smpl
0180 t=t+1;
0181 a1(:,t) = a(:,t);
0182 <span class="keyword">for</span> i=1:pp
0183 v(i,t) = Y(i,t) - a(mf(i),t) - trend(i,t);
0184 <span class="keyword">if</span> Fi_s(i) &gt; crit
0185 a(:,t) = a(:,t) + Ki_s(:,i)*v(i,t)/Fi_s(i);
0186 <span class="keyword">end</span>
0187 <span class="keyword">end</span>
0188 a(:,t+1) = T*a(:,t);
0189 <span class="keyword">for</span> jnk=1:nk,
0190 aK(jnk,:,t+jnk) = T^jnk*a(:,t);
0191 <span class="keyword">end</span>
0192 <span class="keyword">end</span>
0193 a1(:,t+1) = a(:,t+1);
0194 ri=r;
0195 t = smpl+1;
0196 <span class="keyword">while</span> t&gt;d+1 &amp; t&gt;2,
0197 t = t-1;
0198 <span class="keyword">for</span> i=pp:-1:1
0199 <span class="keyword">if</span> Fi(i,t) &gt; crit
0200 ri(:,t)=transpose(Z(i,:))/Fi(i,t)*v(i,t)+transpose(Li(:,:,i,t))*ri(:,t);
0201 <span class="keyword">end</span>
0202 <span class="keyword">end</span>
0203 r(:,t-1) = ri(:,t);
0204 alphahat(:,t) = a1(:,t) + P1(:,:,t)*r(:,t-1);
0205 etahat(:,t) = QRt*r(:,t);
0206 ri(:,t-1) = transpose(T)*ri(:,t);
0207 <span class="keyword">end</span>
0208 <span class="keyword">if</span> d
0209 r0 = zeros(mm,d); r0(:,d) = ri(:,d);
0210 r1 = zeros(mm,d);
0211 <span class="keyword">for</span> t = d:-1:2
0212 <span class="keyword">for</span> i=pp:-1:1
0213 <span class="keyword">if</span> Finf(i,t) &gt; crit &amp; ~(t==d &amp; i&gt;options_.diffuse_d), <span class="comment">% use of options_.diffuse_d to be sure of DKF termination</span>
0214 <span class="comment">%r1(:,t) = transpose(Z)*v(:,t)/Finf(i,t) + ... BUG HERE in transpose(Z)</span>
0215 r1(:,t) = transpose(Z(i,:))*v(i,t)/Finf(i,t) + <span class="keyword">...</span>
0216 transpose(L0(:,:,i,t))*r0(:,t) + transpose(Linf(:,:,i,t))*r1(:,t);
0217 r0(:,t) = transpose(Linf(:,:,i,t))*r0(:,t);
0218 <span class="keyword">elseif</span> Fstar(i,t) &gt; crit <span class="comment">% step needed whe Finf == 0</span>
0219 r0(:,t)=transpose(Z(i,:))/Fstar(i,t)*v(i,t)+Li(:,:,i,t)'*r0(:,t);
0220 <span class="keyword">end</span>
0221 <span class="keyword">end</span>
0222 alphahat(:,t) = a1(:,t) + Pstar1(:,:,t)*r0(:,t) + Pinf1(:,:,t)*r1(:,t);
0223 r(:,t-1) = r0(:,t);
0224 etahat(:,t) = QRt*r(:,t);
0225 r0(:,t-1) = transpose(T)*r0(:,t);
0226 r1(:,t-1) = transpose(T)*r1(:,t);
0227 <span class="keyword">end</span>
0228 r0_0 = r0(:,1);
0229 r1_0 = r1(:,1);
0230 <span class="keyword">for</span> i=pp:-1:1
0231 <span class="keyword">if</span> Finf(i,1) &gt; crit,
0232 <span class="comment">%r1_0 = transpose(Z)*v(:,1)/Finf(i,1) + ... %bug with Z here</span>
0233 r1_0 = transpose(Z(i,:))*v(i,1)/Finf(i,1) + <span class="keyword">...</span>
0234 transpose(L0(:,:,i,1))*r0_0 + transpose(Linf(:,:,i,1))*r1_0;
0235 r0_0 = transpose(Linf(:,:,i,1))*r0_0;
0236 <span class="keyword">elseif</span> Fstar(i,1) &gt; crit, <span class="comment">% step needed when Finf=0</span>
0237 r0_0=transpose(Z(i,:))/Fstar(i,1)*v(i,1)+Li(:,:,i,1)'*r0_0;
0238 <span class="keyword">end</span>
0239 <span class="keyword">end</span>
0240 <span class="comment">%alphahat(:,1) = a(:,1) + Pstar(:,:,1)*r0_0 + Pinf(:,:,1)*r1_0; %this line is buggy</span>
0241 alphahat(:,1) = a1(:,1) + Pstar1(:,:,1)*r0_0 + Pinf1(:,:,1)*r1_0;
0242 etahat(:,1) = QRt*r(:,1);
0243 <span class="keyword">else</span>
0244 r0 = ri(:,1);
0245 <span class="keyword">for</span> i=pp:-1:1
0246 <span class="keyword">if</span> Fi(i,1) &gt; crit
0247 r0=transpose(Z(i,:))/Fi(i,1)*v(i,1)+transpose(Li(:,:,i,1))*r0;
0248 <span class="keyword">end</span>
0249 <span class="keyword">end</span>
0250 <span class="comment">%alphahat(:,1) = a(:,1) + P(:,:,1)*r0; % this line is buggy</span>
0251 alphahat(:,1) = a1(:,1) + P1(:,:,1)*r0;
0252 etahat(:,1) = QRt*r(:,1);
0253 <span class="keyword">end</span>
0254 epsilonhat = Y-alphahat(mf,:)-trend;
0255</pre></div>
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<h1>DiffuseKalmanSmootherH3corr
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>stephane.adjemian@cepremap.cnrs.fr [09-16-2004]</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function [alphahat,epsilonhat,etahat,a1] = DiffuseKalmanSmootherH3corr(T,R,Q,H,Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> stephane.adjemian@cepremap.cnrs.fr [09-16-2004]
See &quot;Fast Filtering and Smoothing for Multivariate State Space
Models&quot;, S.J. Koopman and J. Durbin (2000, in Journal of Time Series
Analysis, vol. 21(3), pp. 281-296).</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="steady.html" class="code" title="function steady(linear)">steady</a> Copyright (C) 2001 Michel Juillard</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function [alphahat,epsilonhat,etahat,a1] = DiffuseKalmanSmootherH3corr(T,R,Q,H,Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf)</a>
0002 <span class="comment">% stephane.adjemian@cepremap.cnrs.fr [09-16-2004]</span>
0003 <span class="comment">%</span>
0004 <span class="comment">% See &quot;Fast Filtering and Smoothing for Multivariate State Space</span>
0005 <span class="comment">% Models&quot;, S.J. Koopman and J. Durbin (2000, in Journal of Time Series</span>
0006 <span class="comment">% Analysis, vol. 21(3), pp. 281-296).</span>
0007
0008 <span class="keyword">global</span> options_;
0009
0010 rr = size(Q,1);
0011 T = cat(1,cat(2,T,zeros(mm,pp)),zeros(pp,mm+pp));
0012 R = cat(1,cat(2,R,zeros(mm,pp)),cat(2,zeros(pp,rr),eye(pp)));
0013 Q = cat(1,cat(2,Q,zeros(rr,pp)),cat(2,zeros(pp,rr),H));
0014 <span class="keyword">if</span> size(Pinf1,1) <span class="comment">% Otherwise Pinf = 0 (no unit root)</span>
0015 Pinf1 = cat(1,cat(2,Pinf1,zeros(mm,pp)),zeros(pp,mm+pp));
0016 <span class="keyword">end</span>
0017 Pstar1 = cat(1,cat(2,Pstar1,zeros(mm,pp)),cat(2,zeros(pp,mm),H));
0018 spinf = size(Pinf1);
0019 spstar = size(Pstar1);
0020 Pstar = zeros(spstar(1),spstar(2),smpl+1); Pstar(:,:,1) = Pstar1;
0021 Pinf = zeros(spinf(1),spinf(2),smpl+1); Pinf(:,:,1) = Pinf1;
0022 Pstar1 = Pstar;
0023 Pinf1 = Pinf;
0024 v = zeros(pp,smpl);
0025 a = zeros(mm+pp,smpl+1);
0026 a1 = a;
0027 Fstar = zeros(pp,smpl);
0028 Finf = zeros(pp,smpl);
0029 Fi = zeros(pp,smpl);
0030 Ki = zeros(mm+pp,pp,smpl);
0031 Li = zeros(mm+pp,mm+pp,pp,smpl);
0032 Linf = zeros(mm+pp,mm+pp,pp,smpl);
0033 L0 = zeros(mm+pp,mm+pp,pp,smpl);
0034 Kstar = zeros(mm+pp,pp,smpl);
0035 Kinf = zeros(mm+pp,pp,smpl);
0036 P = zeros(mm+pp,mm+pp,smpl+1);
0037 P1 = zeros(mm+pp,mm+pp,smpl+1);
0038 crit = options_.kalman_tol;
0039 <a href="steady.html" class="code" title="function steady(linear)">steady</a> = smpl;
0040 QQ = R*Q*transpose(R);
0041 QRt = Q*transpose(R);
0042 alphahat = zeros(mm+pp,smpl);
0043 etahat = zeros(rr,smpl);
0044 epsilonhat = zeros(pp,smpl);
0045 r = zeros(mm+pp,smpl);
0046 Z = zeros(pp,mm+pp);
0047 <span class="keyword">for</span> i=1:pp;
0048 Z(i,mf(i)) = 1;
0049 Z(i,mm+i) = 1;
0050 <span class="keyword">end</span>
0051 <span class="comment">%% [1] Kalman filter...</span>
0052 t = 0;
0053 newRank = rank(Pinf(:,:,1),crit);
0054 <span class="keyword">while</span> newRank &amp; t &lt; smpl
0055 t = t+1;
0056 a1(:,t) = a(:,t);
0057 Pstar1(:,:,t) = Pstar(:,:,t);
0058 Pinf1(:,:,t) = Pinf(:,:,t);
0059 <span class="keyword">for</span> i=1:pp
0060 v(i,t) = Y(i,t)-a(mf(i),t)-a(mm+i,t)-trend(i,t);
0061 Fstar(i,t) = Pstar(mf(i),mf(i),t)+Pstar(mm+i,mm+i,t);
0062 Finf(i,t) = Pinf(mf(i),mf(i),t);
0063 Kstar(:,i,t) = Pstar(:,mf(i),t)+Pstar(:,mm+i,t);
0064 <span class="keyword">if</span> Finf(i,t) &gt; crit
0065 Kinf(:,i,t) = Pinf(:,mf(i),t);
0066 Linf(:,:,i,t) = eye(mm+pp) - Kinf(:,i,t)*Z(i,:)/Finf(i,t);
0067 L0(:,:,i,t) = (Kinf(:,i,t)*Fstar(i,t)/Finf(i,t) - Kstar(:,i,t))*Z(i,:)/Finf(i,t);
0068 a(:,t) = a(:,t) + Kinf(:,i,t)*v(i,t)/Finf(i,t);
0069 Pstar(:,:,t) = Pstar(:,:,t) + <span class="keyword">...</span>
0070 Kinf(:,i,t)*transpose(Kinf(:,i,t))*Fstar(i,t)/(Finf(i,t)*Finf(i,t)) - <span class="keyword">...</span>
0071 (Kstar(:,i,t)*transpose(Kinf(:,i,t)) +<span class="keyword">...</span>
0072 Kinf(:,i,t)*transpose(Kstar(:,i,t)))/Finf(i,t);
0073 Pinf(:,:,t) = Pinf(:,:,t) - Kinf(:,i,t)*transpose(Kinf(:,i,t))/Finf(i,t);
0074 <span class="keyword">else</span> <span class="comment">%% Note that : (1) rank(Pinf)=0 implies that Finf = 0, (2) outside this loop (when for some i and t the condition</span>
0075 <span class="comment">%% rank(Pinf)=0 is satisfied we have P = Pstar and F = Fstar and (3) Finf = 0 does not imply that</span>
0076 <span class="comment">%% rank(Pinf)=0. [stphane,11-03-2004].</span>
0077 a(:,t) = a(:,t) + Kstar(:,i,t)*v(i,t)/Fstar(i,t);
0078 Pstar(:,:,t) = Pstar(:,:,t) - Kstar(:,i,t)*transpose(Kstar(:,i,t))/Fstar(i,t);
0079 <span class="keyword">end</span>
0080 <span class="keyword">end</span>
0081 a(:,t+1) = T*a(:,t);
0082 Pstar(:,:,t+1) = T*Pstar(:,:,t)*transpose(T)+ QQ;
0083 Pinf(:,:,t+1) = T*Pinf(:,:,t)*transpose(T);
0084 P0=Pinf(:,:,t+1);
0085 newRank = ~all(abs(P0(:))&lt;crit);
0086 <span class="keyword">end</span>
0087 d = t;
0088 P(:,:,d+1) = Pstar(:,:,d+1);
0089 Linf = Linf(:,:,:,1:d);
0090 L0 = L0(:,:,:,1:d);
0091 Fstar = Fstar(:,1:d);
0092 Finf = Finf(:,1:d);
0093 Kstar = Kstar(:,:,1:d);
0094 Pstar = Pstar(:,:,1:d);
0095 Pinf = Pinf(:,:,1:d);
0096 Pstar1 = Pstar1(:,:,1:d);
0097 Pinf1 = Pinf1(:,:,1:d);
0098 notsteady = 1;
0099 <span class="keyword">while</span> notsteady &amp; t&lt;smpl
0100 t = t+1;
0101 a1(:,t) = a(:,t);
0102 P(:,:,t)=tril(P(:,:,t))+transpose(tril(P(:,:,t),-1));
0103 P1(:,:,t) = P(:,:,t);
0104 <span class="keyword">for</span> i=1:pp
0105 v(i,t) = Y(i,t) - a(mf(i),t) - a(mm+i,t) - trend(i,t);
0106 Fi(i,t) = P(mf(i),mf(i),t)+P(mm+i,mm+i,t);
0107 Ki(:,i,t) = P(:,mf(i),t)+P(:,mm+i,t);
0108 <span class="keyword">if</span> Fi(i,t) &gt; crit
0109 Li(:,:,i,t) = eye(mm+pp)-Ki(:,i,t)*Z(i,:)/Fi(i,t);
0110 a(:,t) = a(:,t) + Ki(:,i,t)*v(i,t)/Fi(i,t);
0111 P(:,:,t) = P(:,:,t) - Ki(:,i,t)*transpose(Ki(:,i,t))/Fi(i,t);
0112 P(:,:,t)=tril(P(:,:,t))+transpose(tril(P(:,:,t),-1));
0113 <span class="keyword">end</span>
0114 <span class="keyword">end</span>
0115 a(:,t+1) = T*a(:,t);
0116 P(:,:,t+1) = T*P(:,:,t)*transpose(T) + QQ;
0117 notsteady = ~(max(max(abs(P(:,:,t+1)-P(:,:,t))))&lt;crit);
0118 <span class="keyword">end</span>
0119 P_s=tril(P(:,:,t))+transpose(tril(P(:,:,t),-1));
0120 Fi_s = Fi(:,t);
0121 Ki_s = Ki(:,:,t);
0122 L_s =Li(:,:,:,t);
0123 <span class="keyword">if</span> t&lt;smpl
0124 t_steady = t+1;
0125 P = cat(3,P(:,:,1:t),repmat(P(:,:,t),[1 1 smpl-t_steady+1]));
0126 Fi = cat(2,Fi(:,1:t),repmat(Fi_s,[1 1 smpl-t_steady+1]));
0127 Li = cat(4,Li(:,:,:,1:t),repmat(L_s,[1 1 smpl-t_steady+1]));
0128 Ki = cat(3,Ki(:,:,1:t),repmat(Ki_s,[1 1 smpl-t_steady+1]));
0129 <span class="keyword">end</span>
0130 <span class="keyword">while</span> t&lt;smpl
0131 t=t+1;
0132 a1(:,t) = a(:,t);
0133 <span class="keyword">for</span> i=1:pp
0134 v(i,t) = Y(i,t) - a(mf(i),t) - a(mm+i,t) - trend(i,t);
0135 <span class="keyword">if</span> Fi_s(i) &gt; crit
0136 a(:,t) = a(:,t) + Ki_s(:,i)*v(i,t)/Fi_s(i);
0137 <span class="keyword">end</span>
0138 <span class="keyword">end</span>
0139 a(:,t+1) = T*a(:,t);
0140 <span class="keyword">end</span>
0141 a1(:,t+1) = a(:,t+1);
0142 <span class="comment">%% [2] Kalman smoother...</span>
0143 ri=r;
0144 t = smpl+1;
0145 <span class="keyword">while</span> t&gt;d+1 &amp; t&gt;2,
0146 t = t-1;
0147 <span class="keyword">for</span> i=pp:-1:1
0148 <span class="keyword">if</span> Fi(i,t) &gt; crit
0149 ri(:,t)=transpose(Z(i,:))/Fi(i,t)*v(i,t)+transpose(Li(:,:,i,t))*ri(:,t);
0150 <span class="keyword">end</span>
0151 <span class="keyword">end</span>
0152 r(:,t-1) = ri(:,t);
0153 alphahat(:,t) = a1(:,t) + P1(:,:,t)*r(:,t-1);
0154 tmp = QRt*r(:,t);
0155 etahat(:,t) = tmp(1:rr);
0156 epsilonhat(:,t) = tmp(rr+1:rr+pp);
0157 ri(:,t-1) = transpose(T)*ri(:,t);
0158 <span class="keyword">end</span>
0159 <span class="keyword">if</span> d
0160 r0 = zeros(mm+pp,d); r0(:,d) = ri(:,d);
0161 r1 = zeros(mm+pp,d);
0162 <span class="keyword">for</span> t = d:-1:2
0163 <span class="keyword">for</span> i=pp:-1:1
0164 <span class="keyword">if</span> Finf(i,t) &gt; crit
0165 r1(:,t) = transpose(Z)*v(:,t)/Finf(i,t) + <span class="keyword">...</span>
0166 transpose(L0(:,:,i,t))*r0(:,t) + transpose(Linf(:,:,i,t))*r1(:,t);
0167 r0(:,t) = transpose(Linf(:,:,i,t))*r0(:,t);
0168 <span class="keyword">end</span>
0169 <span class="keyword">end</span>
0170 alphahat(:,t) = a1(:,t) + Pstar1(:,:,t)*r0(:,t) + Pinf1(:,:,t)*r1(:,t);
0171 r(:,t-1) = r0(:,t);
0172 tmp = QRt*r(:,t);
0173 etahat(:,t) = tmp(1:rr);
0174 epsilonhat(:,t) = tmp(rr+1:rr+pp);
0175 r0(:,t-1) = transpose(T)*r0(:,t);
0176 r1(:,t-1) = transpose(T)*r1(:,t);
0177 <span class="keyword">end</span>
0178 r0_0 = r0(:,1);
0179 r1_0 = r1(:,1);
0180 <span class="keyword">for</span> i=pp:-1:1
0181 <span class="keyword">if</span> Finf(i,1) &gt; crit,
0182 r1_0 = transpose(Z)*v(:,1)/Finf(i,1) + <span class="keyword">...</span>
0183 transpose(L0(:,:,i,1))*r0_0 + transpose(Linf(:,:,i,1))*r1_0;
0184 r0_0 = transpose(Linf(:,:,i,1))*r0_0;
0185 <span class="keyword">end</span>
0186 <span class="keyword">end</span>
0187 alphahat(:,1) = a(:,1) + Pstar(:,:,1)*r0_0 + Pinf(:,:,1)*r1_0;
0188 tmp = QRt*r(:,1);
0189 etahat(:,1) = tmp(1:rr);
0190 epsilonhat(:,1) = tmp(rr+1:rr+pp);
0191 <span class="keyword">else</span>
0192 r0 = ri(:,1);
0193 <span class="keyword">for</span> i=pp:-1:1
0194 <span class="keyword">if</span> Fi(i,1) &gt; crit
0195 r0=transpose(Z(i,:))/Fi(i,1)*v(i,1)+transpose(Li(:,:,i,1))*r0;
0196 <span class="keyword">end</span>
0197 <span class="keyword">end</span>
0198 alphahat(:,1) = a(:,1) + P(:,:,1)*r0;
0199 tmp = QRt*r(:,1);
0200 etahat(:,1) = tmp(1:rr);
0201 epsilonhat(:,1) = tmp(rr+1:rr+pp);
0202 <span class="keyword">end</span>
0203 alphahat = alphahat(1:mm,:);</pre></div>
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<h1>DiffuseLikelihood1
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>M. Ratto added lik in output</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function [LIK, lik] = DiffuseLikelihood1(T,R,Q,Pinf,Pstar,Y,trend,start) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> M. Ratto added lik in output
stephane.adjemian@cepremap.cnrs.fr [07-19-2004]
Same as DiffuseLikelihoodH1 without measurement error.</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function [LIK, lik] = DiffuseLikelihood1(T,R,Q,Pinf,Pstar,Y,trend,start)</a>
0002 <span class="comment">% M. Ratto added lik in output</span>
0003 <span class="comment">% stephane.adjemian@cepremap.cnrs.fr [07-19-2004]</span>
0004 <span class="comment">%</span>
0005 <span class="comment">% Same as DiffuseLikelihoodH1 without measurement error.</span>
0006 <span class="keyword">global</span> bayestopt_ options_
0007
0008 mf = bayestopt_.mf;
0009 smpl = size(Y,2);
0010 mm = size(T,2);
0011 pp = size(Y,1);
0012 a = zeros(mm,1);
0013 dF = 1;
0014 QQ = R*Q*transpose(R);
0015 t = 0;
0016 lik = zeros(smpl+1,1);
0017 LIK = Inf;
0018 lik(smpl+1) = smpl*pp*log(2*pi);
0019 notsteady = 1;
0020 crit = options_.kalman_tol;
0021 reste = 0;
0022 <span class="keyword">while</span> rank(Pinf,crit) &amp; t &lt; smpl
0023 t = t+1;
0024 v = Y(:,t)-a(mf)-trend(:,t);
0025 Finf = Pinf(mf,mf);
0026 <span class="keyword">if</span> rcond(Finf) &lt; crit
0027 <span class="keyword">if</span> ~all(abs(Finf(:)) &lt; crit)
0028 <span class="keyword">return</span>
0029 <span class="keyword">else</span>
0030 iFstar = inv(Pstar(mf,mf));
0031 dFstar = det(Pstar(mf,mf));
0032 Kstar = Pstar(:,mf)*iFstar;
0033 lik(t) = log(dFstar) + transpose(v)*iFstar*v;
0034 Pinf = T*Pinf*transpose(T);
0035 Pstar = T*(Pstar-Pstar(:,mf)*transpose(Kstar))*transpose(T)+QQ;
0036 a = T*(a+Kstar*v);
0037 <span class="keyword">end</span>
0038 <span class="keyword">else</span>
0039 lik(t) = log(det(Finf));
0040 iFinf = inv(Finf);
0041 Kinf = Pinf(:,mf)*iFinf; <span class="comment">%% premultiplication by the transition matrix T is removed (stephane)</span>
0042 Fstar = Pstar(mf,mf);
0043 Kstar = (Pstar(:,mf)-Kinf*Fstar)*iFinf; <span class="comment">%% premultiplication by the transition matrix T is removed (stephane)</span>
0044 Pstar = T*(Pstar-Pstar(:,mf)*transpose(Kinf)-Pinf(:,mf)*transpose(Kstar))*transpose(T)+QQ;
0045 Pinf = T*(Pinf-Pinf(:,mf)*transpose(Kinf))*transpose(T);
0046 a = T*(a+Kinf*v);
0047 <span class="keyword">end</span>
0048 <span class="keyword">end</span>
0049 <span class="keyword">if</span> t == smpl
0050 error([<span class="string">'There isn''t enough information to estimate the initial'</span> <span class="keyword">...</span><span class="comment"> </span>
0051 <span class="string">' conditions of the nonstationary variables'</span>]);
0052 <span class="keyword">end</span>
0053 F_singular = 1;
0054 <span class="keyword">while</span> notsteady &amp; t &lt; smpl
0055 t = t+1;
0056 v = Y(:,t)-a(mf)-trend(:,t);
0057 F = Pstar(mf,mf);
0058 oldPstar = Pstar;
0059 dF = det(F);
0060 <span class="keyword">if</span> rcond(F) &lt; crit
0061 <span class="keyword">if</span> ~all(abs(F(:))&lt;crit)
0062 <span class="keyword">return</span>
0063 <span class="keyword">else</span>
0064 a = T*a;
0065 Pstar = T*Pstar*transpose(T)+QQ;
0066 <span class="keyword">end</span>
0067 <span class="keyword">else</span>
0068 F_singular = 0;
0069 iF = inv(F);
0070 lik(t) = log(dF)+transpose(v)*iF*v;
0071 K = Pstar(:,mf)*iF; <span class="comment">%% premultiplication by the transition matrix T is removed (stephane)</span>
0072 a = T*(a+K*v); <span class="comment">%% --&gt; factorization of the transition matrix...</span>
0073 Pstar = T*(Pstar-K*Pstar(mf,:))*transpose(T)+QQ; <span class="comment">%% ... idem (stephane)</span>
0074 <span class="keyword">end</span>
0075 notsteady = ~(max(max(abs(Pstar-oldPstar)))&lt;crit);
0076 <span class="keyword">end</span>
0077 <span class="keyword">if</span> F_singular == 1
0078 error([<span class="string">'The variance of the forecast error remains singular until the'</span> <span class="keyword">...</span>
0079 <span class="string">'end of the sample'</span>])
0080 <span class="keyword">end</span>
0081 reste = smpl-t;
0082 <span class="keyword">while</span> t &lt; smpl
0083 t = t+1;
0084 v = Y(:,t)-a(mf)-trend(:,t);
0085 a = T*(a+K*v);
0086 lik(t) = transpose(v)*iF*v;
0087 <span class="keyword">end</span>
0088 lik(t) = lik(t) + reste*log(dF);
0089
0090
0091 LIK = .5*(sum(lik(start:end))-(start-1)*lik(smpl+1)/smpl);<span class="comment">% Minus the log-likelihood.</span></pre></div>
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<h1>DiffuseLikelihood3
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>M. Ratto added lik in output [October 2005]</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function [LIK, lik] = DiffuseLikelihood3(T,R,Q,Pinf,Pstar,Y,trend,start)%//Z,T,R,Q,Pinf,Pstar,Y) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> M. Ratto added lik in output [October 2005]
changes by M. Ratto [April 2005]
introduced new options options_.diffuse_d for termination of DKF
new icc counter for Finf steps in DKF
new termination for DKF
likelihood terms for Fstar must be cumulated in DKF also when Pinf is non
zero.
[4/5/2005] correctyed bug in the modified verson of Ratto for rank of Pinf
introduced a specific crit1 for the DKF termination
stepane.adjemian@cepremap.cnrs.fr [07-19-2004]
See &quot;Filtering and Smoothing of State Vector for Diffuse State Space
Models&quot;, S.J. Koopman and J. Durbin (2003, in Journal of Time Series
Analysis, vol. 24(1), pp. 85-98).
Case where F_{\infty,t} is singular ==&gt; Univariate treatment of multivariate
time series.
THE PROBLEM:
y_t = Z_t * \alpha_t + \varepsilon_t
\alpha_{t+1} = T_t * \alpha_t + R_t * \eta_t
with:
\alpha_1 = a + A*\delta + R_0*\eta_0
m*q matrix A and m*(m-q) matrix R_0 are selection matrices (their
columns constitue all the columns of the m*m identity matrix) so that
A'*R_0 = 0 and A'*\alpha_1 = \delta
We assume that the vector \delta is distributed as a N(0,\kappa*I_q)
for a given \kappa &gt; 0. So that the expectation of \alpha_1 is a and
its variance is P, with
P = \kappa*P_{\infty} + P_{\star}
P_{\infty} = A*A'
P_{\star} = R_0*Q_0*R_0'
P_{\infty} is a m*m diagonal matrix with q ones and m-q zeros.
and where:
y_t is a pp*1 vector
\alpha_t is a mm*1 vector
\varepsilon_t is a pp*1 multivariate random variable (iid N(0,H_t))
\eta_t is a rr*1 multivariate random variable (iid N(0,Q_t))
a_1 is a mm*1 vector
Z_t is a pp*mm matrix
T_t is a mm*mm matrix
H_t is a pp*pp matrix
R_t is a mm*rr matrix
Q_t is a rr*rr matrix
P_1 is a mm*mm matrix
FILTERING EQUATIONS:
v_t = y_t - Z_t* a_t
F_t = Z_t * P_t * Z_t' + H_t
K_t = T_t * P_t * Z_t' * F_t^{-1}
L_t = T_t - K_t * Z_t
a_{t+1} = T_t * a_t + K_t * v_t
P_{t+1} = T_t * P_t * L_t' + R_t*Q_t*R_t'
DIFFUSE FILTERING EQUATIONS:
a_{t+1} = T_t*a_t + K_{\ast,t}v_t
P_{\infty,t+1} = T_t*P_{\infty,t}*T_t'
P_{\ast,t+1} = T_t*P_{\ast,t}*L_{\ast,t}' + R_t*Q_t*R_t'
K_{\ast,t} = T_t*P_{\ast,t}*Z_t'*F_{\ast,t}^{-1}
v_t = y_t - Z_t*a_t
L_{\ast,t} = T_t - K_{\ast,t}*Z_t
F_{\ast,t} = Z_t*P_{\ast,t}*Z_t' + H_t</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="DsgeLikelihood.html" class="code" title="function [fval,cost_flag,ys,trend_coeff,info] = DsgeLikelihood(xparam1,gend,data)">DsgeLikelihood</a> stephane.adjemian@cepremap.cnrs.fr [09-07-2004]</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function [LIK, lik] = DiffuseLikelihood3(T,R,Q,Pinf,Pstar,Y,trend,start)</a><span class="comment">%//Z,T,R,Q,Pinf,Pstar,Y)</span>
0002 <span class="comment">% M. Ratto added lik in output [October 2005]</span>
0003 <span class="comment">% changes by M. Ratto [April 2005]</span>
0004 <span class="comment">% introduced new options options_.diffuse_d for termination of DKF</span>
0005 <span class="comment">% new icc counter for Finf steps in DKF</span>
0006 <span class="comment">% new termination for DKF</span>
0007 <span class="comment">% likelihood terms for Fstar must be cumulated in DKF also when Pinf is non</span>
0008 <span class="comment">% zero.</span>
0009 <span class="comment">%</span>
0010 <span class="comment">% [4/5/2005] correctyed bug in the modified verson of Ratto for rank of Pinf</span>
0011 <span class="comment">% introduced a specific crit1 for the DKF termination</span>
0012 <span class="comment">%</span>
0013 <span class="comment">% stepane.adjemian@cepremap.cnrs.fr [07-19-2004]</span>
0014 <span class="comment">%</span>
0015 <span class="comment">% See &quot;Filtering and Smoothing of State Vector for Diffuse State Space</span>
0016 <span class="comment">% Models&quot;, S.J. Koopman and J. Durbin (2003, in Journal of Time Series</span>
0017 <span class="comment">% Analysis, vol. 24(1), pp. 85-98).</span>
0018 <span class="comment">%</span>
0019 <span class="comment">% Case where F_{\infty,t} is singular ==&gt; Univariate treatment of multivariate</span>
0020 <span class="comment">% time series.</span>
0021 <span class="comment">%</span>
0022 <span class="comment">% THE PROBLEM:</span>
0023 <span class="comment">%</span>
0024 <span class="comment">% y_t = Z_t * \alpha_t + \varepsilon_t</span>
0025 <span class="comment">% \alpha_{t+1} = T_t * \alpha_t + R_t * \eta_t</span>
0026 <span class="comment">%</span>
0027 <span class="comment">% with:</span>
0028 <span class="comment">%</span>
0029 <span class="comment">% \alpha_1 = a + A*\delta + R_0*\eta_0</span>
0030 <span class="comment">%</span>
0031 <span class="comment">% m*q matrix A and m*(m-q) matrix R_0 are selection matrices (their</span>
0032 <span class="comment">% columns constitue all the columns of the m*m identity matrix) so that</span>
0033 <span class="comment">%</span>
0034 <span class="comment">% A'*R_0 = 0 and A'*\alpha_1 = \delta</span>
0035 <span class="comment">%</span>
0036 <span class="comment">% We assume that the vector \delta is distributed as a N(0,\kappa*I_q)</span>
0037 <span class="comment">% for a given \kappa &gt; 0. So that the expectation of \alpha_1 is a and</span>
0038 <span class="comment">% its variance is P, with</span>
0039 <span class="comment">%</span>
0040 <span class="comment">% P = \kappa*P_{\infty} + P_{\star}</span>
0041 <span class="comment">%</span>
0042 <span class="comment">% P_{\infty} = A*A'</span>
0043 <span class="comment">% P_{\star} = R_0*Q_0*R_0'</span>
0044 <span class="comment">%</span>
0045 <span class="comment">% P_{\infty} is a m*m diagonal matrix with q ones and m-q zeros.</span>
0046 <span class="comment">%</span>
0047 <span class="comment">%</span>
0048 <span class="comment">% and where:</span>
0049 <span class="comment">%</span>
0050 <span class="comment">% y_t is a pp*1 vector</span>
0051 <span class="comment">% \alpha_t is a mm*1 vector</span>
0052 <span class="comment">% \varepsilon_t is a pp*1 multivariate random variable (iid N(0,H_t))</span>
0053 <span class="comment">% \eta_t is a rr*1 multivariate random variable (iid N(0,Q_t))</span>
0054 <span class="comment">% a_1 is a mm*1 vector</span>
0055 <span class="comment">%</span>
0056 <span class="comment">% Z_t is a pp*mm matrix</span>
0057 <span class="comment">% T_t is a mm*mm matrix</span>
0058 <span class="comment">% H_t is a pp*pp matrix</span>
0059 <span class="comment">% R_t is a mm*rr matrix</span>
0060 <span class="comment">% Q_t is a rr*rr matrix</span>
0061 <span class="comment">% P_1 is a mm*mm matrix</span>
0062 <span class="comment">%</span>
0063 <span class="comment">%</span>
0064 <span class="comment">% FILTERING EQUATIONS:</span>
0065 <span class="comment">%</span>
0066 <span class="comment">% v_t = y_t - Z_t* a_t</span>
0067 <span class="comment">% F_t = Z_t * P_t * Z_t' + H_t</span>
0068 <span class="comment">% K_t = T_t * P_t * Z_t' * F_t^{-1}</span>
0069 <span class="comment">% L_t = T_t - K_t * Z_t</span>
0070 <span class="comment">% a_{t+1} = T_t * a_t + K_t * v_t</span>
0071 <span class="comment">% P_{t+1} = T_t * P_t * L_t' + R_t*Q_t*R_t'</span>
0072 <span class="comment">%</span>
0073 <span class="comment">%</span>
0074 <span class="comment">% DIFFUSE FILTERING EQUATIONS:</span>
0075 <span class="comment">%</span>
0076 <span class="comment">% a_{t+1} = T_t*a_t + K_{\ast,t}v_t</span>
0077 <span class="comment">% P_{\infty,t+1} = T_t*P_{\infty,t}*T_t'</span>
0078 <span class="comment">% P_{\ast,t+1} = T_t*P_{\ast,t}*L_{\ast,t}' + R_t*Q_t*R_t'</span>
0079 <span class="comment">% K_{\ast,t} = T_t*P_{\ast,t}*Z_t'*F_{\ast,t}^{-1}</span>
0080 <span class="comment">% v_t = y_t - Z_t*a_t</span>
0081 <span class="comment">% L_{\ast,t} = T_t - K_{\ast,t}*Z_t</span>
0082 <span class="comment">% F_{\ast,t} = Z_t*P_{\ast,t}*Z_t' + H_t</span>
0083 <span class="keyword">global</span> bayestopt_ options_
0084
0085 mf = bayestopt_.mf;
0086 pp = size(Y,1);
0087 mm = size(T,1);
0088 smpl = size(Y,2);
0089 a = zeros(mm,1);
0090 QQ = R*Q*transpose(R);
0091 t = 0;
0092 lik = zeros(smpl+1,1);
0093 lik(smpl+1) = smpl*pp*log(2*pi); <span class="comment">%% the constant of minus two times the log-likelihood</span>
0094 notsteady = 1;
0095 crit = options_.kalman_tol;
0096 crit1 = 1.e-6;
0097 newRank = rank(Pinf,crit1);
0098 icc=0;
0099 <span class="keyword">while</span> newRank &amp; t &lt; smpl
0100 t = t+1;
0101 <span class="keyword">for</span> i=1:pp
0102 v(i) = Y(i,t)-a(mf(i))-trend(i,t);
0103 Fstar = Pstar(mf(i),mf(i));
0104 Finf = Pinf(mf(i),mf(i));
0105 Kstar = Pstar(:,mf(i));
0106 <span class="keyword">if</span> Finf &gt; crit &amp; newRank, <span class="comment">%added newRank criterion</span>
0107 icc=icc+1;
0108 Kinf = Pinf(:,mf(i));
0109 a = a + Kinf*v(i)/Finf;
0110 Pstar = Pstar + Kinf*transpose(Kinf)*Fstar/(Finf*Finf) - <span class="keyword">...</span>
0111 (Kstar*transpose(Kinf)+Kinf*transpose(Kstar))/Finf;
0112 Pinf = Pinf - Kinf*transpose(Kinf)/Finf;
0113 lik(t) = lik(t) + log(Finf);
0114 <span class="comment">% start new termination criterion for DKF</span>
0115 <span class="keyword">if</span> ~isempty(options_.diffuse_d),
0116 newRank = (icc&lt;options_.diffuse_d);
0117 <span class="comment">%if newRank &amp; any(diag(Pinf(mf,mf))&gt;crit)==0; % M. Ratto this line is BUGGY</span>
0118 <span class="keyword">if</span> newRank &amp; (any(diag(Pinf(mf,mf))&gt;crit)==0 &amp; rank(Pinf,crit1)==0);
0119 options_.diffuse_d = icc;
0120 newRank=0;
0121 disp(<span class="string">'WARNING: Change in OPTIONS_.DIFFUSE_D in univariate DKF'</span>)
0122 disp([<span class="string">'new OPTIONS_.DIFFUSE_D = '</span>,int2str(icc)])
0123 disp(<span class="string">'You may have to reset the optimisation'</span>)
0124 <span class="keyword">end</span>
0125 <span class="keyword">else</span>
0126 <span class="comment">%newRank = any(diag(Pinf(mf,mf))&gt;crit); % M. Ratto this line is BUGGY</span>
0127 newRank = (any(diag(Pinf(mf,mf))&gt;crit) | rank(Pinf,crit1));
0128 <span class="keyword">if</span> newRank==0,
0129 P0= T*Pinf*transpose(T);
0130 <span class="comment">%newRank = any(diag(P0(mf,mf))&gt;crit); % M. Ratto this line is BUGGY</span>
0131 newRank = (any(diag(P0(mf,mf))&gt;crit) | rank(P0,crit1)); <span class="comment">% M. Ratto 11/10/2005</span>
0132 <span class="keyword">if</span> newRank==0,
0133 options_.diffuse_d = icc;
0134 <span class="keyword">end</span>
0135 <span class="keyword">end</span>
0136 <span class="keyword">end</span>,
0137 <span class="comment">% end new termination and checks for DKF</span>
0138 <span class="keyword">elseif</span> Fstar &gt; crit
0139 <span class="comment">%% Note that : (1) rank(Pinf)=0 implies that Finf = 0, (2) outside this loop (when for some i and t the condition</span>
0140 <span class="comment">%% rank(Pinf)=0 is satisfied we have P = Pstar and F = Fstar and (3) Finf = 0 does not imply that</span>
0141 <span class="comment">%% rank(Pinf)=0. [stphane,11-03-2004].</span>
0142 <span class="comment">%if rank(Pinf,crit) == 0</span>
0143 <span class="comment">% the likelihood terms should alwasy be cumulated, not only</span>
0144 <span class="comment">% when Pinf=0, otherwise the lik would depend on the ordering</span>
0145 <span class="comment">% of observed variables</span>
0146 <span class="comment">% presample options can be used to ignore initial time points</span>
0147 lik(t) = lik(t) + log(Fstar) + v(i)*v(i)/Fstar;
0148 <span class="comment">%end</span>
0149 a = a + Kstar*v(i)/Fstar;
0150 Pstar = Pstar - Kstar*transpose(Kstar)/Fstar;
0151 <span class="keyword">else</span>
0152 <span class="comment">%disp(['zero F term in DKF for observed ',int2str(i),' ',num2str(Fstar)])</span>
0153 <span class="keyword">end</span>
0154 <span class="keyword">end</span>
0155 <span class="comment">% if all(abs(Pinf(:))&lt;crit),</span>
0156 <span class="comment">% oldRank = 0;</span>
0157 <span class="comment">% else</span>
0158 <span class="comment">% oldRank = rank(Pinf,crit);</span>
0159 <span class="comment">% end</span>
0160 <span class="keyword">if</span> newRank,
0161 oldRank = rank(Pinf,crit1);
0162 <span class="keyword">else</span>
0163 oldRank = 0;
0164 <span class="keyword">end</span>
0165 a = T*a;
0166 Pstar = T*Pstar*transpose(T)+QQ;
0167 Pinf = T*Pinf*transpose(T);
0168 <span class="comment">% if all(abs(Pinf(:))&lt;crit),</span>
0169 <span class="comment">% newRank = 0;</span>
0170 <span class="comment">% else</span>
0171 <span class="comment">% newRank = rank(Pinf,crit);</span>
0172 <span class="comment">% end</span>
0173 <span class="keyword">if</span> newRank,
0174 newRank = rank(Pinf,crit1); <span class="comment">% new crit1 is used</span>
0175 <span class="keyword">end</span>
0176 <span class="keyword">if</span> oldRank ~= newRank
0177 disp(<span class="string">'DiffuseLiklihood3 :: T does influence the rank of Pinf!'</span>)
0178 <span class="keyword">end</span>
0179 <span class="keyword">end</span>
0180 <span class="keyword">if</span> t == smpl
0181 error([<span class="string">'There isn''t enough information to estimate the initial'</span> <span class="keyword">...</span><span class="comment"> </span>
0182 <span class="string">' conditions of the nonstationary variables'</span>]);
0183 <span class="keyword">end</span>
0184 <span class="keyword">while</span> notsteady &amp; t &lt; smpl
0185 t = t+1;
0186 oldP = Pstar;
0187 <span class="keyword">for</span> i=1:pp
0188 v(i) = Y(i,t) - a(mf(i)) - trend(i,t);
0189 Fi = Pstar(mf(i),mf(i));
0190 <span class="keyword">if</span> Fi &gt; crit
0191 Ki = Pstar(:,mf(i));
0192 a = a + Ki*v(i)/Fi;
0193 Pstar = Pstar - Ki*transpose(Ki)/Fi;
0194 lik(t) = lik(t) + log(Fi) + v(i)*v(i)/Fi;
0195 <span class="keyword">else</span>
0196 <span class="comment">%disp(['zero F term for observed ',int2str(i),' ',num2str(Fi)])</span>
0197 <span class="keyword">end</span>
0198 <span class="keyword">end</span>
0199 a = T*a;
0200 Pstar = T*Pstar*transpose(T) + QQ;
0201 notsteady = ~(max(max(abs(Pstar-oldP)))&lt;crit);
0202 <span class="keyword">end</span>
0203 <span class="keyword">while</span> t &lt; smpl
0204 t = t+1;
0205 Pstar = oldP;
0206 <span class="keyword">for</span> i=1:pp
0207 v(i) = Y(i,t) - a(mf(i)) - trend(i,t);
0208 Fi = Pstar(mf(i),mf(i));
0209 <span class="keyword">if</span> Fi &gt; crit
0210 Ki = Pstar(:,mf(i));
0211 a = a + Ki*v(i)/Fi;
0212 Pstar = Pstar - Ki*transpose(Ki)/Fi;
0213 lik(t) = lik(t) + log(Fi) + v(i)*v(i)/Fi;
0214 <span class="keyword">else</span>
0215 <span class="comment">%disp(['zero F term for observed ',int2str(i),' ',num2str(Fi)])</span>
0216 <span class="keyword">end</span>
0217 <span class="keyword">end</span>
0218 a = T*a;
0219 <span class="keyword">end</span>
0220
0221 LIK = .5*(sum(lik(start:end))-(start-1)*lik(smpl+1)/smpl);
0222</pre></div>
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<h1>DiffuseLikelihoodH1
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>M. Ratto added lik in output</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function [LIK, lik] = DiffuseLikelihoodH1(T,R,Q,H,Pinf,Pstar,Y,trend,start) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> M. Ratto added lik in output
stephane.adjemian@cepremap.cnrs.fr [07-19-2004]
See &quot;Filtering and Smoothing of State Vector for Diffuse State Space
Models&quot;, S.J. Koopman and J. Durbin (2003, in Journal of Time Series
Analysis, vol. 24(1), pp. 85-98).
THE PROBLEM:
y_t = Z_t * \alpha_t + \varepsilon_t
\alpha_{t+1} = T_t * \alpha_t + R_t * \eta_t
with:
\alpha_1 = a + A*\delta + R_0*\eta_0
m*q matrix A and m*(m-q) matrix R_0 are selection matrices (their
columns constitue all the columns of the m*m identity matrix) so that
A'*R_0 = 0 and A'*\alpha_1 = \delta
We assume that the vector \delta is distributed as a N(0,\kappa*I_q)
for a given \kappa &gt; 0. So that the expectation of \alpha_1 is a and
its variance is P, with
P = \kappa*P_{\infty} + P_{\star}
P_{\infty} = A*A'
P_{\star} = R_0*Q_0*R_0'
P_{\infty} is a m*m diagonal matrix with q ones and m-q zeros.
and where:
y_t is a pp*1 vector
\alpha_t is a mm*1 vector
\varepsilon_t is a pp*1 multivariate random variable (iid N(0,H_t))
\eta_t is a rr*1 multivariate random variable (iid N(0,Q_t))
a_1 is a mm*1 vector
Z_t is a pp*mm matrix
T_t is a mm*mm matrix
H_t is a pp*pp matrix
R_t is a mm*rr matrix
Q_t is a rr*rr matrix
P_1 is a mm*mm matrix
FILTERING EQUATIONS:
v_t = y_t - Z_t* a_t
F_t = Z_t * P_t * Z_t' + H_t
K_t = T_t * P_t * Z_t' * F_t^{-1}
L_t = T_t - K_t * Z_t
a_{t+1} = T_t * a_t + K_t * v_t
P_{t+1} = T_t * P_t * L_t' + R_t*Q_t*R_t'
DIFFUSE FILTERING EQUATIONS:
a_{t+1} = T_t*a_t + K_{\infty,t}v_t
P_{\infty,t+1} = T_t*P_{\infty,t}*L_{\infty,t}'
P_{\ast,t+1} = T_t*P_{\ast,t}*L_{\infty,t}' - K_{\infty,t}*F_{\infty,t}*K_{\ast,t}' + R_t*Q_t*R_t'
K_{\infty,t} = T_t*P_{\infty,t}*Z_t'*F_{\infty,t}^{-1}
v_t = y_t - Z_t*a_t
L_{\infty,t} = T_t - K_{\infty,t}*Z_t
F_{\infty,t} = Z_t*P_{\infty,t}*Z_t'
K_{\ast,t} = (T_t*P_{\ast,t}*Z_t' + K_{\infty,t}*F_{\ast,t})*F_{\infty,t}^{-1}
F_{\ast,t} = Z_t*P_{\ast,t}*Z_t' + H_t
Matrix Finf is assumed to be non singular. If this is not the case we have
to switch to another algorithm (NewAlg=3).
start = options_.presample</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="DsgeLikelihood.html" class="code" title="function [fval,cost_flag,ys,trend_coeff,info] = DsgeLikelihood(xparam1,gend,data)">DsgeLikelihood</a> stephane.adjemian@cepremap.cnrs.fr [09-07-2004]</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function [LIK, lik] = DiffuseLikelihoodH1(T,R,Q,H,Pinf,Pstar,Y,trend,start)</a>
0002 <span class="comment">% M. Ratto added lik in output</span>
0003 <span class="comment">% stephane.adjemian@cepremap.cnrs.fr [07-19-2004]</span>
0004 <span class="comment">%</span>
0005 <span class="comment">% See &quot;Filtering and Smoothing of State Vector for Diffuse State Space</span>
0006 <span class="comment">% Models&quot;, S.J. Koopman and J. Durbin (2003, in Journal of Time Series</span>
0007 <span class="comment">% Analysis, vol. 24(1), pp. 85-98).</span>
0008 <span class="comment">%</span>
0009 <span class="comment">% THE PROBLEM:</span>
0010 <span class="comment">%</span>
0011 <span class="comment">% y_t = Z_t * \alpha_t + \varepsilon_t</span>
0012 <span class="comment">% \alpha_{t+1} = T_t * \alpha_t + R_t * \eta_t</span>
0013 <span class="comment">%</span>
0014 <span class="comment">% with:</span>
0015 <span class="comment">%</span>
0016 <span class="comment">% \alpha_1 = a + A*\delta + R_0*\eta_0</span>
0017 <span class="comment">%</span>
0018 <span class="comment">% m*q matrix A and m*(m-q) matrix R_0 are selection matrices (their</span>
0019 <span class="comment">% columns constitue all the columns of the m*m identity matrix) so that</span>
0020 <span class="comment">%</span>
0021 <span class="comment">% A'*R_0 = 0 and A'*\alpha_1 = \delta</span>
0022 <span class="comment">%</span>
0023 <span class="comment">% We assume that the vector \delta is distributed as a N(0,\kappa*I_q)</span>
0024 <span class="comment">% for a given \kappa &gt; 0. So that the expectation of \alpha_1 is a and</span>
0025 <span class="comment">% its variance is P, with</span>
0026 <span class="comment">%</span>
0027 <span class="comment">% P = \kappa*P_{\infty} + P_{\star}</span>
0028 <span class="comment">%</span>
0029 <span class="comment">% P_{\infty} = A*A'</span>
0030 <span class="comment">% P_{\star} = R_0*Q_0*R_0'</span>
0031 <span class="comment">%</span>
0032 <span class="comment">% P_{\infty} is a m*m diagonal matrix with q ones and m-q zeros.</span>
0033 <span class="comment">%</span>
0034 <span class="comment">%</span>
0035 <span class="comment">% and where:</span>
0036 <span class="comment">%</span>
0037 <span class="comment">% y_t is a pp*1 vector</span>
0038 <span class="comment">% \alpha_t is a mm*1 vector</span>
0039 <span class="comment">% \varepsilon_t is a pp*1 multivariate random variable (iid N(0,H_t))</span>
0040 <span class="comment">% \eta_t is a rr*1 multivariate random variable (iid N(0,Q_t))</span>
0041 <span class="comment">% a_1 is a mm*1 vector</span>
0042 <span class="comment">%</span>
0043 <span class="comment">% Z_t is a pp*mm matrix</span>
0044 <span class="comment">% T_t is a mm*mm matrix</span>
0045 <span class="comment">% H_t is a pp*pp matrix</span>
0046 <span class="comment">% R_t is a mm*rr matrix</span>
0047 <span class="comment">% Q_t is a rr*rr matrix</span>
0048 <span class="comment">% P_1 is a mm*mm matrix</span>
0049 <span class="comment">%</span>
0050 <span class="comment">%</span>
0051 <span class="comment">% FILTERING EQUATIONS:</span>
0052 <span class="comment">%</span>
0053 <span class="comment">% v_t = y_t - Z_t* a_t</span>
0054 <span class="comment">% F_t = Z_t * P_t * Z_t' + H_t</span>
0055 <span class="comment">% K_t = T_t * P_t * Z_t' * F_t^{-1}</span>
0056 <span class="comment">% L_t = T_t - K_t * Z_t</span>
0057 <span class="comment">% a_{t+1} = T_t * a_t + K_t * v_t</span>
0058 <span class="comment">% P_{t+1} = T_t * P_t * L_t' + R_t*Q_t*R_t'</span>
0059 <span class="comment">%</span>
0060 <span class="comment">%</span>
0061 <span class="comment">% DIFFUSE FILTERING EQUATIONS:</span>
0062 <span class="comment">%</span>
0063 <span class="comment">% a_{t+1} = T_t*a_t + K_{\infty,t}v_t</span>
0064 <span class="comment">% P_{\infty,t+1} = T_t*P_{\infty,t}*L_{\infty,t}'</span>
0065 <span class="comment">% P_{\ast,t+1} = T_t*P_{\ast,t}*L_{\infty,t}' - K_{\infty,t}*F_{\infty,t}*K_{\ast,t}' + R_t*Q_t*R_t'</span>
0066 <span class="comment">% K_{\infty,t} = T_t*P_{\infty,t}*Z_t'*F_{\infty,t}^{-1}</span>
0067 <span class="comment">% v_t = y_t - Z_t*a_t</span>
0068 <span class="comment">% L_{\infty,t} = T_t - K_{\infty,t}*Z_t</span>
0069 <span class="comment">% F_{\infty,t} = Z_t*P_{\infty,t}*Z_t'</span>
0070 <span class="comment">% K_{\ast,t} = (T_t*P_{\ast,t}*Z_t' + K_{\infty,t}*F_{\ast,t})*F_{\infty,t}^{-1}</span>
0071 <span class="comment">% F_{\ast,t} = Z_t*P_{\ast,t}*Z_t' + H_t</span>
0072 <span class="comment">%</span>
0073 <span class="comment">% Matrix Finf is assumed to be non singular. If this is not the case we have</span>
0074 <span class="comment">% to switch to another algorithm (NewAlg=3).</span>
0075 <span class="comment">%</span>
0076 <span class="comment">% start = options_.presample</span>
0077 <span class="keyword">global</span> bayestopt_ options_
0078
0079 mf = bayestopt_.mf;
0080 smpl = size(Y,2);
0081 mm = size(T,2);
0082 pp = size(Y,1);
0083 a = zeros(mm,1);
0084 dF = 1;
0085 QQ = R*Q*transpose(R);
0086 t = 0;
0087 lik = zeros(smpl+1,1);
0088 LIK = Inf;
0089 lik(smpl+1) = smpl*pp*log(2*pi);
0090 notsteady = 1;
0091 crit = options_.kalman_tol;
0092 reste = 0;
0093 <span class="keyword">while</span> rank(Pinf,crit) &amp; t &lt; smpl
0094 t = t+1;
0095 v = Y(:,t)-a(mf)-trend(:,t);
0096 Finf = Pinf(mf,mf);
0097 <span class="keyword">if</span> rcond(Finf) &lt; crit
0098 <span class="keyword">if</span> ~all(abs(Finf(:))&lt;crit)
0099 <span class="keyword">return</span>
0100 <span class="keyword">else</span>
0101 iFstar = inv(Pstar(mf,mf)+H);
0102 dFstar = det(Pstar(mf,mf)+H);
0103 Kstar = Pstar(:,mf)*iFstar;
0104 lik(t) = log(dFstar) + transpose(v)*iFstar*v;
0105 Pinf = T*Pinf*transpose(T);
0106 Pstar = T*(Pstar-Pstar(:,mf)*transpose(Kstar))*transpose(T)+QQ;
0107 a = T*(a+Kstar*v);
0108 <span class="keyword">end</span>
0109 <span class="keyword">else</span>
0110 lik(t) = log(det(Finf));
0111 iFinf = inv(Finf);
0112 Kinf = Pinf(:,mf)*iFinf; <span class="comment">%% premultiplication by the transition matrix T is removed (stephane)</span>
0113 Fstar = Pstar(mf,mf)+H;
0114 Kstar = (Pstar(:,mf)-Kinf*Fstar)*iFinf; <span class="comment">%% premultiplication by the transition matrix T is removed (stephane)</span>
0115 Pstar = T*(Pstar-Pstar(:,mf)*transpose(Kinf)-Pinf(:,mf)*transpose(Kstar))*transpose(T)+QQ;
0116 Pinf = T*(Pinf-Pinf(:,mf)*transpose(Kinf))*transpose(T);
0117 a = T*(a+Kinf*v);
0118 <span class="keyword">end</span>
0119 <span class="keyword">end</span>
0120 <span class="keyword">if</span> t == smpl
0121 error([<span class="string">'There isn''t enough information to estimate the initial'</span> <span class="keyword">...</span><span class="comment"> </span>
0122 <span class="string">' conditions of the nonstationary variables'</span>]);
0123 <span class="keyword">end</span>
0124 F_singular = 1;
0125 <span class="keyword">while</span> notsteady &amp; t &lt; smpl
0126 t = t+1;
0127 v = Y(:,t)-a(mf)-trend(:,t);
0128 F = Pstar(mf,mf)+H;
0129 oldPstar = Pstar;
0130 dF = det(F);
0131 <span class="keyword">if</span> rcond(F) &lt; crit
0132 <span class="keyword">if</span> ~all(abs(F(:))&lt;crit)
0133 <span class="keyword">return</span>
0134 <span class="keyword">else</span>
0135 a = T*a;
0136 Pstar = T*Pstar*transpose(T)+QQ;
0137 <span class="keyword">end</span>
0138 <span class="keyword">else</span>
0139 F_singular = 0;
0140 iF = inv(F);
0141 lik(t) = log(dF)+transpose(v)*iF*v;
0142 K = Pstar(:,mf)*iF; <span class="comment">%% premultiplication by the transition matrix T is removed (stephane)</span>
0143 a = T*(a+K*v); <span class="comment">%% --&gt; factorization of the transition matrix...</span>
0144 Pstar = T*(Pstar-K*Pstar(mf,:))*transpose(T)+QQ; <span class="comment">%% ... idem (stephane)</span>
0145 <span class="keyword">end</span>
0146 notsteady = ~(max(max(abs(Pstar-oldPstar)))&lt;crit);
0147 <span class="keyword">end</span>
0148 <span class="keyword">if</span> F_singular == 1
0149 error([<span class="string">'The variance of the forecast error remains singular until the'</span> <span class="keyword">...</span>
0150 <span class="string">'end of the sample'</span>])
0151 <span class="keyword">end</span>
0152 reste = smpl-t;
0153 <span class="keyword">while</span> t &lt; smpl
0154 t = t+1;
0155 v = Y(:,t)-a(mf)-trend(:,t);
0156 a = T*(a+K*v);
0157 lik(t) = transpose(v)*iF*v;
0158 <span class="keyword">end</span>
0159 lik(t) = lik(t) + reste*log(dF);
0160 LIK = .5*(sum(lik(start:end))-(start-1)*lik(smpl+1)/smpl);<span class="comment">% Minus the</span>
0161 <span class="comment">% log-likelihood.</span>
0162</pre></div>
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<div><a href="../index.html">Home</a> &gt; <a href="index.html">.</a> &gt; DiffuseLikelihoodH3.m</div>
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<h1>DiffuseLikelihoodH3
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>M. Ratto added lik in output [October 2005]</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function [LIK, lik] = DiffuseLikelihoodH3(T,R,Q,H,Pinf,Pstar,Y,trend,start) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> M. Ratto added lik in output [October 2005]
changes by M. Ratto
introduced new global variable id_ for termination of DKF
introduced a persistent fmax, in order to keep track the max order of
magnitude of the 'zero' values in Pinf at DKF termination
new icc counter for Finf steps in DKF
new termination for DKF
likelihood terms for Fstar must be cumulated in DKF also when Pinf is non
zero. this bug is fixed.
stephane.adjemian@cepremap.cnrs.fr [07-19-2004]
See &quot;Filtering and Smoothing of State Vector for Diffuse State Space
Models&quot;, S.J. Koopman and J. Durbin (2003, in Journal of Time Series
Analysis, vol. 24(1), pp. 85-98).
Case where F_{\infty,t} is singular ==&gt; Univariate treatment of multivariate
time series.
THE PROBLEM:
y_t = Z_t * \alpha_t + \varepsilon_t
\alpha_{t+1} = T_t * \alpha_t + R_t * \eta_t
with:
\alpha_1 = a + A*\delta + R_0*\eta_0
m*q matrix A and m*(m-q) matrix R_0 are selection matrices (their
columns constitue all the columns of the m*m identity matrix) so that
A'*R_0 = 0 and A'*\alpha_1 = \delta
We assume that the vector \delta is distributed as a N(0,\kappa*I_q)
for a given \kappa &gt; 0. So that the expectation of \alpha_1 is a and
its variance is P, with
P = \kappa*P_{\infty} + P_{\star}
P_{\infty} = A*A'
P_{\star} = R_0*Q_0*R_0'
P_{\infty} is a m*m diagonal matrix with q ones and m-q zeros.
and where:
y_t is a pp*1 vector
\alpha_t is a mm*1 vector
\varepsilon_t is a pp*1 multivariate random variable (iid N(0,H_t))
\eta_t is a rr*1 multivariate random variable (iid N(0,Q_t))
a_1 is a mm*1 vector
Z_t is a pp*mm matrix
T_t is a mm*mm matrix
H_t is a pp*pp matrix
R_t is a mm*rr matrix
Q_t is a rr*rr matrix
P_1 is a mm*mm matrix
FILTERING EQUATIONS:
v_t = y_t - Z_t* a_t
F_t = Z_t * P_t * Z_t' + H_t
K_t = T_t * P_t * Z_t' * F_t^{-1}
L_t = T_t - K_t * Z_t
a_{t+1} = T_t * a_t + K_t * v_t
P_{t+1} = T_t * P_t * L_t' + R_t*Q_t*R_t'
DIFFUSE FILTERING EQUATIONS:
a_{t+1} = T_t*a_t + K_{\ast,t}v_t
P_{\infty,t+1} = T_t*P_{\infty,t}*T_t'
P_{\ast,t+1} = T_t*P_{\ast,t}*L_{\ast,t}' + R_t*Q_t*R_t'
K_{\ast,t} = T_t*P_{\ast,t}*Z_t'*F_{\ast,t}^{-1}
v_t = y_t - Z_t*a_t
L_{\ast,t} = T_t - K_{\ast,t}*Z_t
F_{\ast,t} = Z_t*P_{\ast,t}*Z_t' + H_t</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="DsgeLikelihood.html" class="code" title="function [fval,cost_flag,ys,trend_coeff,info] = DsgeLikelihood(xparam1,gend,data)">DsgeLikelihood</a> stephane.adjemian@cepremap.cnrs.fr [09-07-2004]</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function [LIK, lik] = DiffuseLikelihoodH3(T,R,Q,H,Pinf,Pstar,Y,trend,start)</a>
0002 <span class="comment">% M. Ratto added lik in output [October 2005]</span>
0003 <span class="comment">% changes by M. Ratto</span>
0004 <span class="comment">% introduced new global variable id_ for termination of DKF</span>
0005 <span class="comment">% introduced a persistent fmax, in order to keep track the max order of</span>
0006 <span class="comment">% magnitude of the 'zero' values in Pinf at DKF termination</span>
0007 <span class="comment">% new icc counter for Finf steps in DKF</span>
0008 <span class="comment">% new termination for DKF</span>
0009 <span class="comment">% likelihood terms for Fstar must be cumulated in DKF also when Pinf is non</span>
0010 <span class="comment">% zero. this bug is fixed.</span>
0011 <span class="comment">%</span>
0012 <span class="comment">% stephane.adjemian@cepremap.cnrs.fr [07-19-2004]</span>
0013 <span class="comment">%</span>
0014 <span class="comment">% See &quot;Filtering and Smoothing of State Vector for Diffuse State Space</span>
0015 <span class="comment">% Models&quot;, S.J. Koopman and J. Durbin (2003, in Journal of Time Series</span>
0016 <span class="comment">% Analysis, vol. 24(1), pp. 85-98).</span>
0017 <span class="comment">%</span>
0018 <span class="comment">% Case where F_{\infty,t} is singular ==&gt; Univariate treatment of multivariate</span>
0019 <span class="comment">% time series.</span>
0020 <span class="comment">%</span>
0021 <span class="comment">% THE PROBLEM:</span>
0022 <span class="comment">%</span>
0023 <span class="comment">% y_t = Z_t * \alpha_t + \varepsilon_t</span>
0024 <span class="comment">% \alpha_{t+1} = T_t * \alpha_t + R_t * \eta_t</span>
0025 <span class="comment">%</span>
0026 <span class="comment">% with:</span>
0027 <span class="comment">%</span>
0028 <span class="comment">% \alpha_1 = a + A*\delta + R_0*\eta_0</span>
0029 <span class="comment">%</span>
0030 <span class="comment">% m*q matrix A and m*(m-q) matrix R_0 are selection matrices (their</span>
0031 <span class="comment">% columns constitue all the columns of the m*m identity matrix) so that</span>
0032 <span class="comment">%</span>
0033 <span class="comment">% A'*R_0 = 0 and A'*\alpha_1 = \delta</span>
0034 <span class="comment">%</span>
0035 <span class="comment">% We assume that the vector \delta is distributed as a N(0,\kappa*I_q)</span>
0036 <span class="comment">% for a given \kappa &gt; 0. So that the expectation of \alpha_1 is a and</span>
0037 <span class="comment">% its variance is P, with</span>
0038 <span class="comment">%</span>
0039 <span class="comment">% P = \kappa*P_{\infty} + P_{\star}</span>
0040 <span class="comment">%</span>
0041 <span class="comment">% P_{\infty} = A*A'</span>
0042 <span class="comment">% P_{\star} = R_0*Q_0*R_0'</span>
0043 <span class="comment">%</span>
0044 <span class="comment">% P_{\infty} is a m*m diagonal matrix with q ones and m-q zeros.</span>
0045 <span class="comment">%</span>
0046 <span class="comment">%</span>
0047 <span class="comment">% and where:</span>
0048 <span class="comment">%</span>
0049 <span class="comment">% y_t is a pp*1 vector</span>
0050 <span class="comment">% \alpha_t is a mm*1 vector</span>
0051 <span class="comment">% \varepsilon_t is a pp*1 multivariate random variable (iid N(0,H_t))</span>
0052 <span class="comment">% \eta_t is a rr*1 multivariate random variable (iid N(0,Q_t))</span>
0053 <span class="comment">% a_1 is a mm*1 vector</span>
0054 <span class="comment">%</span>
0055 <span class="comment">% Z_t is a pp*mm matrix</span>
0056 <span class="comment">% T_t is a mm*mm matrix</span>
0057 <span class="comment">% H_t is a pp*pp matrix</span>
0058 <span class="comment">% R_t is a mm*rr matrix</span>
0059 <span class="comment">% Q_t is a rr*rr matrix</span>
0060 <span class="comment">% P_1 is a mm*mm matrix</span>
0061 <span class="comment">%</span>
0062 <span class="comment">%</span>
0063 <span class="comment">% FILTERING EQUATIONS:</span>
0064 <span class="comment">%</span>
0065 <span class="comment">% v_t = y_t - Z_t* a_t</span>
0066 <span class="comment">% F_t = Z_t * P_t * Z_t' + H_t</span>
0067 <span class="comment">% K_t = T_t * P_t * Z_t' * F_t^{-1}</span>
0068 <span class="comment">% L_t = T_t - K_t * Z_t</span>
0069 <span class="comment">% a_{t+1} = T_t * a_t + K_t * v_t</span>
0070 <span class="comment">% P_{t+1} = T_t * P_t * L_t' + R_t*Q_t*R_t'</span>
0071 <span class="comment">%</span>
0072 <span class="comment">%</span>
0073 <span class="comment">% DIFFUSE FILTERING EQUATIONS:</span>
0074 <span class="comment">%</span>
0075 <span class="comment">% a_{t+1} = T_t*a_t + K_{\ast,t}v_t</span>
0076 <span class="comment">% P_{\infty,t+1} = T_t*P_{\infty,t}*T_t'</span>
0077 <span class="comment">% P_{\ast,t+1} = T_t*P_{\ast,t}*L_{\ast,t}' + R_t*Q_t*R_t'</span>
0078 <span class="comment">% K_{\ast,t} = T_t*P_{\ast,t}*Z_t'*F_{\ast,t}^{-1}</span>
0079 <span class="comment">% v_t = y_t - Z_t*a_t</span>
0080 <span class="comment">% L_{\ast,t} = T_t - K_{\ast,t}*Z_t</span>
0081 <span class="comment">% F_{\ast,t} = Z_t*P_{\ast,t}*Z_t' + H_t</span>
0082 <span class="keyword">global</span> bayestopt_ options_
0083
0084 mf = bayestopt_.mf;
0085 pp = size(Y,1);
0086 mm = size(T,1);
0087 smpl = size(Y,2);
0088 a = zeros(mm,1);
0089 QQ = R*Q*transpose(R);
0090 t = 0;
0091 lik = zeros(smpl+1,1);
0092 lik(smpl+1) = smpl*pp*log(2*pi); <span class="comment">%% the constant of minus two times the log-likelihood</span>
0093 notsteady = 1;
0094 crit = options_.kalman_tol;
0095 crit1 = 1.e-6;
0096 newRank = rank(Pinf,crit1);
0097 icc = 0;
0098 <span class="keyword">while</span> newRank &amp; t &lt; smpl <span class="comment">%% Matrix Finf is assumed to be zero</span>
0099 t = t+1;
0100 <span class="keyword">for</span> i=1:pp
0101 v(i) = Y(i,t)-a(mf(i))-trend(i,t);
0102 Fstar = Pstar(mf(i),mf(i))+H(i,i);
0103 Finf = Pinf(mf(i),mf(i));
0104 Kstar = Pstar(:,mf(i));
0105 <span class="keyword">if</span> Finf &gt; crit &amp; newRank
0106 icc = icc + 1;
0107 Kinf = Pinf(:,mf(i));
0108 a = a + Kinf*v(i)/Finf;
0109 Pstar = Pstar + Kinf*transpose(Kinf)*Fstar/(Finf*Finf) - <span class="keyword">...</span>
0110 (Kstar*transpose(Kinf)+Kinf*transpose(Kstar))/Finf;
0111 Pinf = Pinf - Kinf*transpose(Kinf)/Finf;
0112 lik(t) = lik(t) + log(Finf);
0113 <span class="comment">% start new termination criterion for DKF</span>
0114 <span class="keyword">if</span> ~isempty(options_.diffuse_d),
0115 newRank = (icc&lt;options_.diffuse_d);
0116 <span class="comment">%if newRank &amp; any(diag(Pinf(mf,mf))&gt;crit)==0; % M. Ratto this line is BUGGY</span>
0117 <span class="keyword">if</span> newRank &amp; (any(diag(Pinf(mf,mf))&gt;crit)==0 &amp; rank(Pinf,crit1)==0);
0118 options_.diffuse_d = icc;
0119 newRank=0;
0120 disp(<span class="string">'WARNING: Change in OPTIONS_.DIFFUSE_D in univariate DKF'</span>)
0121 disp([<span class="string">'new OPTIONS_.DIFFUSE_D = '</span>,int2str(icc)])
0122 disp(<span class="string">'You may have to reset the optimisation'</span>)
0123 <span class="keyword">end</span>
0124 <span class="keyword">else</span>
0125 <span class="comment">%newRank = any(diag(Pinf(mf,mf))&gt;crit); % M. Ratto this line is BUGGY</span>
0126 newRank = (any(diag(Pinf(mf,mf))&gt;crit) | rank(Pinf,crit1));
0127 <span class="keyword">if</span> newRank==0,
0128 P0= T*Pinf*transpose(T);
0129 <span class="comment">%newRank = any(diag(P0(mf,mf))&gt;crit); % M. Ratto this line is BUGGY</span>
0130 newRank = (any(diag(P0(mf,mf))&gt;crit) | rank(P0,crit1)); <span class="comment">% M. Ratto 10 Oct 2005</span>
0131 <span class="keyword">if</span> newRank==0,
0132 options_.diffuse_d = icc;
0133 <span class="keyword">end</span>
0134 <span class="keyword">end</span>
0135 <span class="keyword">end</span>,
0136 <span class="comment">% end new termination and checks for DKF and fmax</span>
0137 <span class="keyword">elseif</span> Finf &gt; crit
0138 <span class="comment">%% Note that : (1) rank(Pinf)=0 implies that Finf = 0, (2) outside this loop (when for some i and t the condition</span>
0139 <span class="comment">%% rank(Pinf)=0 is satisfied we have P = Pstar and F = Fstar and (3) Finf = 0 does not imply that</span>
0140 <span class="comment">%% rank(Pinf)=0. [stphane,11-03-2004].</span>
0141 <span class="comment">%if rank(Pinf) == 0</span>
0142 <span class="comment">% the likelihood terms should alwasy be cumulated, not only</span>
0143 <span class="comment">% when Pinf=0, otherwise the lik would depend on the ordering</span>
0144 <span class="comment">% of observed variables</span>
0145 lik(t) = lik(t) + log(Fstar) + v(i)*v(i)/Fstar;
0146 <span class="comment">%end</span>
0147 a = a + Kstar*v(i)/Fstar;
0148 Pstar = Pstar - Kstar*transpose(Kstar)/Fstar;
0149 <span class="keyword">else</span>
0150 <span class="comment">% disp(['zero F term in DKF for observed ',int2str(i),' ',num2str(Fi)])</span>
0151 <span class="keyword">end</span>
0152 <span class="keyword">end</span>
0153 <span class="keyword">if</span> newRank
0154 oldRank = rank(Pinf,crit1);
0155 <span class="keyword">else</span>
0156 oldRank = 0;
0157 <span class="keyword">end</span>
0158 a = T*a;
0159 Pstar = T*Pstar*transpose(T)+QQ;
0160 Pinf = T*Pinf*transpose(T);
0161 <span class="keyword">if</span> newRank
0162 newRank = rank(Pinf,crit1);
0163 <span class="keyword">end</span>
0164 <span class="keyword">if</span> oldRank ~= newRank
0165 disp(<span class="string">'DiffuseLiklihoodH3 :: T does influence the rank of Pinf!'</span>)
0166 <span class="keyword">end</span>
0167 <span class="keyword">end</span>
0168 <span class="keyword">if</span> t == smpl
0169 error([<span class="string">'There isn''t enough information to estimate the initial'</span> <span class="keyword">...</span><span class="comment"> </span>
0170 <span class="string">' conditions of the nonstationary variables'</span>]);
0171 <span class="keyword">end</span>
0172 <span class="keyword">while</span> notsteady &amp; t &lt; smpl
0173 t = t+1;
0174 <span class="keyword">for</span> i=1:pp
0175 v(i) = Y(i,t) - a(mf(i)) - trend(i,t);
0176 Fi = Pstar(mf(i),mf(i))+H(i,i);
0177 <span class="keyword">if</span> Fi &gt; crit
0178 Ki = Pstar(:,mf(i));
0179 a = a + Ki*v(i)/Fi;
0180 Pstar = Pstar - Ki*transpose(Ki)/Fi;
0181 lik(t) = lik(t) + log(Fi) + v(i)*v(i)/Fi;
0182 <span class="keyword">end</span>
0183 <span class="keyword">end</span>
0184 oldP = Pstar;
0185 a = T*a;
0186 Pstar = T*Pstar*transpose(T) + QQ;
0187 notsteady = ~(max(max(abs(Pstar-oldP)))&lt;crit);
0188 <span class="keyword">end</span>
0189 <span class="keyword">while</span> t &lt; smpl
0190 t = t+1;
0191 <span class="keyword">for</span> i=1:pp
0192 v(i) = Y(i,t) - a(mf(i)) - trend(i,t);
0193 Fi = Pstar(mf(i),mf(i))+H(i,i);
0194 <span class="keyword">if</span> Fi &gt; crit
0195 Ki = Pstar(:,mf(i));
0196 a = a + Ki*v(i)/Fi;
0197 Pstar = Pstar - Ki*transpose(Ki)/Fi;
0198 lik(t) = lik(t) + log(Fi) + v(i)*v(i)/Fi;
0199 <span class="keyword">end</span>
0200 <span class="keyword">end</span>
0201 a = T*a;
0202 <span class="keyword">end</span>
0203 LIK = .5*(sum(lik(start:end))-(start-1)*lik(smpl+1)/smpl);
0204</pre></div>
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<div><a href="../index.html">Home</a> &gt; <a href="index.html">.</a> &gt; DiffuseLikelihoodH3corr.m</div>
<!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png">&nbsp;Master index</a></td>
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<h1>DiffuseLikelihoodH3corr
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>stephane.adjemian@cepremap.cnrs.fr [12-13-2004]</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function LIK = DiffuseLikelihoodH3corr(T,R,Q,H,Pinf,Pstar,Y,trend,start) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> stephane.adjemian@cepremap.cnrs.fr [12-13-2004]
Same as DiffuseLikelihoodH3 but allows correlation between the measurement
errors (this is not a problem with the multivariate approach).</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="DsgeLikelihood.html" class="code" title="function [fval,cost_flag,ys,trend_coeff,info] = DsgeLikelihood(xparam1,gend,data)">DsgeLikelihood</a> stephane.adjemian@cepremap.cnrs.fr [09-07-2004]</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function LIK = DiffuseLikelihoodH3corr(T,R,Q,H,Pinf,Pstar,Y,trend,start)</a>
0002 <span class="comment">% stephane.adjemian@cepremap.cnrs.fr [12-13-2004]</span>
0003 <span class="comment">%</span>
0004 <span class="comment">% Same as DiffuseLikelihoodH3 but allows correlation between the measurement</span>
0005 <span class="comment">% errors (this is not a problem with the multivariate approach).</span>
0006
0007 <span class="keyword">global</span> bayestopt_ options_
0008
0009 mf = bayestopt_.mf;
0010 pp = size(Y,1);
0011 mm = size(T,1);
0012 rr = size(Q,1);
0013 smpl = size(Y,2);
0014 T = cat(1,cat(2,T,zeros(mm,pp)),zeros(pp,mm+pp));
0015 R = cat(1,cat(2,R,zeros(mm,pp)),cat(2,zeros(pp,rr),eye(pp)));
0016 Q = cat(1,cat(2,Q,zeros(rr,pp)),cat(2,zeros(pp,rr),H));
0017 <span class="keyword">if</span> size(Pinf,1) <span class="comment">% Otherwise Pinf = 0 (no unit root)</span>
0018 Pinf = cat(1,cat(2,Pinf,zeros(mm,pp)),zeros(pp,mm+pp));
0019 <span class="keyword">end</span>
0020 Pstar = cat(1,cat(2,Pstar,zeros(mm,pp)),cat(2,zeros(pp,mm),H));
0021 a = zeros(mm+pp,1);
0022 QQ = R*Q*transpose(R);
0023 t = 0;
0024 lik = zeros(smpl+1,1);
0025 lik(smpl+1) = smpl*pp*log(2*pi); <span class="comment">%% the constant of minus two times the log-likelihood</span>
0026 notsteady = 1;
0027 crit = options_.kalman_tol;
0028 newRank = rank(Pinf,crit);
0029
0030 <span class="keyword">while</span> rank(Pinf,crit) &amp; t &lt; smpl <span class="comment">%% Matrix Finf is assumed to be zero</span>
0031 t = t+1;
0032 <span class="keyword">for</span> i=1:pp
0033 v(i) = Y(i,t)-a(mf(i))-a(mm+i)-trend(i,t);
0034 Fstar = Pstar(mf(i),mf(i))+Pstar(mm+i,mm+i);
0035 Finf = Pinf(mf(i),mf(i));
0036 Kstar = Pstar(:,mf(i))+Pstar(:,mm+i);
0037 <span class="keyword">if</span> Finf &gt; crit
0038 Kinf = Pinf(:,mf(i));
0039 a = a + Kinf*v(i)/Finf;
0040 Pstar = Pstar + Kinf*transpose(Kinf)*Fstar/(Finf*Finf) - <span class="keyword">...</span>
0041 (Kstar*transpose(Kinf)+Kinf*transpose(Kstar))/Finf;
0042 Pinf = Pinf - Kinf*transpose(Kinf)/Finf;
0043 lik(t) = lik(t) + log(Finf);
0044 <span class="keyword">else</span> <span class="comment">%% Note that : (1) rank(Pinf)=0 implies that Finf = 0, (2) outside this loop (when for some i and t the condition</span>
0045 <span class="comment">%% rank(Pinf)=0 is satisfied we have P = Pstar and F = Fstar and (3) Finf = 0 does not imply that</span>
0046 <span class="comment">%% rank(Pinf)=0. [stphane,11-03-2004].</span>
0047 <span class="keyword">if</span> rank(Pinf) == 0
0048 lik(t) = lik(t) + log(Fstar) + v(i)*v(i)/Fstar;
0049 <span class="keyword">end</span>
0050 a = a + Kstar*v(i)/Fstar;
0051 Pstar = Pstar - Kstar*transpose(Kstar)/Fstar;
0052 <span class="keyword">end</span>
0053 oldRank = rank(Pinf,crit);
0054 a = T*a;
0055 Pstar = T*Pstar*transpose(T)+QQ;
0056 Pinf = T*Pinf*transpose(T);
0057 newRank = rank(Pinf,crit);
0058 <span class="keyword">if</span> oldRank ~= newRank
0059 disp(<span class="string">'DiffuseLiklihoodH3 :: T does influence the rank of Pinf!'</span>)
0060 <span class="keyword">end</span>
0061 <span class="keyword">end</span>
0062 <span class="keyword">end</span>
0063 <span class="keyword">if</span> t == smpl
0064 error([<span class="string">'There isn''t enough information to estimate the initial'</span> <span class="keyword">...</span><span class="comment"> </span>
0065 <span class="string">' conditions of the nonstationary variables'</span>]);
0066 <span class="keyword">end</span>
0067 <span class="keyword">while</span> notsteady &amp; t &lt; smpl
0068 t = t+1;
0069 <span class="keyword">for</span> i=1:pp
0070 v(i) = Y(i,t) - a(mf(i)) - trend(i,t) -a(mm+i);
0071 Fi = Pstar(mf(i),mf(i))+Pstar(mm+i,mm+i);
0072 <span class="keyword">if</span> Fi &gt; crit
0073 Ki = Pstar(:,mf(i))+Pstar(:,mm+i);
0074 a = a + Ki*v(i)/Fi;
0075 Pstar = Pstar - Ki*transpose(Ki)/Fi;
0076 lik(t) = lik(t) + log(Fi) + v(i)*v(i)/Fi;
0077 <span class="keyword">end</span>
0078 <span class="keyword">end</span>
0079 oldP = Pstar;
0080 a = T*a;
0081 Pstar = T*Pstar*transpose(T) + QQ;
0082 notsteady = ~(max(max(abs(Pstar-oldP)))&lt;crit);
0083 <span class="keyword">end</span>
0084 <span class="keyword">while</span> t &lt; smpl
0085 t = t+1;
0086 <span class="keyword">for</span> i=1:pp
0087 v(i) = Y(i,t) - a(mf(i)) - trend(i,t) - a(mm+i);
0088 Fi = Pstar(mf(i),mf(i))+Pstar(mm+i,mm+i);
0089 <span class="keyword">if</span> Fi &gt; crit
0090 Ki = Pstar(:,mf(i))+Pstar(:,mm+i);
0091 a = a + Ki*v(i)/Fi;
0092 Pstar = Pstar - Ki*transpose(Ki)/Fi;
0093 lik(t) = lik(t) + log(Fi) + v(i)*v(i)/Fi;
0094 <span class="keyword">end</span>
0095 <span class="keyword">end</span>
0096 a = T*a;
0097 <span class="keyword">end</span>
0098 LIK = .5*(sum(lik(start:end))-(start-1)*lik(smpl+1)/smpl);</pre></div>
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<div><a href="../index.html">Home</a> &gt; <a href="index.html">.</a> &gt; DsgeLikelihood.m</div>
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<h1>DsgeLikelihood
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>stephane.adjemian@cepremap.cnrs.fr [09-07-2004]</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function [fval,cost_flag,ys,trend_coeff,info] = DsgeLikelihood(xparam1,gend,data) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> stephane.adjemian@cepremap.cnrs.fr [09-07-2004]
Adapted from mj_optmumlik.m</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="DiffuseLikelihood3.html" class="code" title="function [LIK, lik] = DiffuseLikelihood3(T,R,Q,Pinf,Pstar,Y,trend,start)%//Z,T,R,Q,Pinf,Pstar,Y)">DiffuseLikelihood3</a> M. Ratto added lik in output [October 2005]</li><li><a href="DiffuseLikelihoodH1.html" class="code" title="function [LIK, lik] = DiffuseLikelihoodH1(T,R,Q,H,Pinf,Pstar,Y,trend,start)">DiffuseLikelihoodH1</a> M. Ratto added lik in output</li><li><a href="DiffuseLikelihoodH3.html" class="code" title="function [LIK, lik] = DiffuseLikelihoodH3(T,R,Q,H,Pinf,Pstar,Y,trend,start)">DiffuseLikelihoodH3</a> M. Ratto added lik in output [October 2005]</li><li><a href="DiffuseLikelihoodH3corr.html" class="code" title="function LIK = DiffuseLikelihoodH3corr(T,R,Q,H,Pinf,Pstar,Y,trend,start)">DiffuseLikelihoodH3corr</a> stephane.adjemian@cepremap.cnrs.fr [12-13-2004]</li><li><a href="dynare_resolve.html" class="code" title="function [A,B,ys,info] = dynare_resolve()">dynare_resolve</a> </li><li><a href="kalman_filter.html" class="code" title="function [loglik, per, d] = kalman_filter(varargin)">kalman_filter</a> SYNOPSIS</li><li><a href="lyapunov_symm.html" class="code" title="function [x,ns_var]=lyapunov_symm(a,b)">lyapunov_symm</a> solves x-a*x*a'=b for b (and then x) symmetrical</li><li><a href="priordens.html" class="code" title="function lnprior = priordens(para, pshape, p1, p2, p3, p4)">priordens</a> This procedure computes a prior density for</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="dynare_MC.html" class="code" title="function dynare_MC(var_list_)">dynare_MC</a> </li><li><a href="dynare_estimation.html" class="code" title="function dynare_estimation(var_list_)">dynare_estimation</a> </li><li><a href="initial_estimation_checks.html" class="code" title="function initial_estimation_checks(xparam1,gend,data)">initial_estimation_checks</a> </li><li><a href="metropolis.html" class="code" title="function metropolis(xparam1,vv,gend,data,rawdata,mh_bounds)">metropolis</a> stephane.adjemian@ens.fr [09-02-2005]</li><li><a href="mode_check.html" class="code" title="function mode_check(x,fval,hessian,gend,data,lb,ub)">mode_check</a> </li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function [fval,cost_flag,ys,trend_coeff,info] = DsgeLikelihood(xparam1,gend,data)</a>
0002 <span class="comment">% stephane.adjemian@cepremap.cnrs.fr [09-07-2004]</span>
0003 <span class="comment">%</span>
0004 <span class="comment">% Adapted from mj_optmumlik.m</span>
0005 <span class="keyword">global</span> bayestopt_ estim_params_ options_ trend_coeff_ M_ oo_ xparam1_test
0006
0007 fval = [];
0008 ys = [];
0009 trend_coeff = [];
0010 xparam1_test = xparam1;
0011 cost_flag = 1;
0012 nobs = size(options_.varobs,1);
0013 <span class="comment">%------------------------------------------------------------------------------</span>
0014 <span class="comment">% 1. Get the structural parameters &amp; define penalties</span>
0015 <span class="comment">%------------------------------------------------------------------------------</span>
0016 <span class="keyword">if</span> options_.mode_compute ~= 1 &amp; any(xparam1 &lt; bayestopt_.lb)
0017 k = find(xparam1 &lt; bayestopt_.lb);
0018 fval = bayestopt_.penalty+sum((bayestopt_.lb(k)-xparam1(k)).^2);
0019 cost_flag = 0;
0020 <span class="keyword">return</span>;
0021 <span class="keyword">end</span>
0022 <span class="keyword">if</span> options_.mode_compute ~= 1 &amp; any(xparam1 &gt; bayestopt_.ub)
0023 k = find(xparam1 &gt; bayestopt_.ub);
0024 fval = bayestopt_.penalty+sum((xparam1(k)-bayestopt_.ub(k)).^2);
0025 cost_flag = 0;
0026 <span class="keyword">return</span>;
0027 <span class="keyword">end</span>
0028 Q = M_.Sigma_e;
0029 <span class="keyword">for</span> i=1:estim_params_.nvx
0030 k =estim_params_.var_exo(i,1);
0031 Q(k,k) = xparam1(i)*xparam1(i);
0032 <span class="keyword">end</span>
0033 offset = estim_params_.nvx;
0034 <span class="keyword">if</span> estim_params_.nvn
0035 H = zeros(nobs,nobs);
0036 <span class="keyword">for</span> i=1:estim_params_.nvn
0037 k = estim_params_.var_endo(i,1);
0038 H(k,k) = xparam1(i+offset)*xparam1(i+offset);
0039 <span class="keyword">end</span>
0040 offset = offset+estim_params_.nvn;
0041 <span class="keyword">end</span>
0042 <span class="keyword">if</span> estim_params_.ncx
0043 <span class="keyword">for</span> i=1:estim_params_.ncx
0044 k1 =estim_params_.corrx(i,1);
0045 k2 =estim_params_.corrx(i,2);
0046 Q(k1,k2) = xparam1(i+offset)*sqrt(Q(k1,k1)*Q(k2,k2));
0047 Q(k2,k1) = Q(k1,k2);
0048 <span class="keyword">end</span>
0049 [CholQ,testQ] = chol(Q);
0050 <span class="keyword">if</span> testQ <span class="comment">%% The variance-covariance matrix of the structural innovations is not definite positive.</span>
0051 <span class="comment">%% We have to compute the eigenvalues of this matrix in order to build the penalty.</span>
0052 a = diag(eig(Q));
0053 k = find(a &lt; 0);
0054 <span class="keyword">if</span> k &gt; 0
0055 fval = bayestopt_.penalty+sum(-a(k));
0056 cost_flag = 0;
0057 <span class="keyword">return</span>
0058 <span class="keyword">end</span>
0059 <span class="keyword">end</span>
0060 offset = offset+estim_params_.ncx;
0061 <span class="keyword">end</span>
0062 <span class="keyword">if</span> estim_params_.ncn
0063 <span class="keyword">for</span> i=1:estim_params_.ncn
0064 k1 = options_.lgyidx2varobs(estim_params_.corrn(i,1));
0065 k2 = options_.lgyidx2varobs(estim_params_.corrn(i,2));
0066 H(k1,k2) = xparam1(i+offset)*sqrt(H(k1,k1)*H(k2,k2));
0067 H(k2,k1) = H(k1,k2);
0068 <span class="keyword">end</span>
0069 [CholH,testH] = chol(H);
0070 <span class="keyword">if</span> testH
0071 a = diag(eig(H));
0072 k = find(a &lt; 0);
0073 <span class="keyword">if</span> k &gt; 0
0074 fval = bayestopt_.penalty+sum(-a(k));
0075 cost_flag = 0;
0076 <span class="keyword">return</span>
0077 <span class="keyword">end</span>
0078 <span class="keyword">end</span>
0079 offset = offset+estim_params_.ncn;
0080 <span class="keyword">end</span>
0081 M_.params(estim_params_.param_vals(:,1)) = xparam1(offset+1:end);
0082 <span class="comment">% for i=1:estim_params_.np</span>
0083 <span class="comment">% M_.params(estim_params_.param_vals(i,1)) = xparam1(i+offset);</span>
0084 <span class="comment">%end</span>
0085 M_.Sigma_e = Q;
0086 <span class="comment">%------------------------------------------------------------------------------</span>
0087 <span class="comment">% 2. call model setup &amp; reduction program</span>
0088 <span class="comment">%------------------------------------------------------------------------------</span>
0089 [T,R,SteadyState,info] = <a href="dynare_resolve.html" class="code" title="function [A,B,ys,info] = dynare_resolve()">dynare_resolve</a>;
0090 rs = bayestopt_.restrict_state;
0091 <span class="keyword">if</span> info(1) == 1 | info(1) == 2 | info(1) == 5
0092 fval = bayestopt_.penalty+1;
0093 cost_flag = 0;
0094 <span class="keyword">return</span>
0095 <span class="keyword">elseif</span> info(1) == 3 | info(1) == 4 | info(1) == 20
0096 fval = bayestopt_.penalty+info(2)^2;
0097 cost_flag = 0;
0098 <span class="keyword">return</span>
0099 <span class="keyword">end</span>
0100 T = T(rs,rs);
0101 R = R(rs,:);
0102 bayestopt_.mf = bayestopt_.mf1;
0103 <span class="keyword">if</span> options_.loglinear == 1
0104 constant = log(SteadyState(bayestopt_.mfys));
0105 <span class="keyword">else</span>
0106 constant = SteadyState(bayestopt_.mfys);
0107 <span class="keyword">end</span>
0108 <span class="keyword">if</span> bayestopt_.with_trend == 1
0109 trend_coeff = zeros(nobs,1);
0110 <span class="keyword">for</span> i=1:nobs
0111 trend_coeff(i) = evalin(<span class="string">'base'</span>,bayestopt_.trend_coeff{i});
0112 <span class="keyword">end</span>
0113 trend = repmat(constant,1,gend)+trend_coeff*[1:gend];
0114 <span class="keyword">else</span>
0115 trend = repmat(constant,1,gend);
0116 <span class="keyword">end</span>
0117 start = options_.presample+1;
0118 np = size(T,1);
0119 mf = bayestopt_.mf;
0120 <span class="comment">%------------------------------------------------------------------------------</span>
0121 <span class="comment">% 3. Initial condition of the Kalman filter</span>
0122 <span class="comment">%------------------------------------------------------------------------------</span>
0123 <span class="keyword">if</span> options_.lik_init == 1 <span class="comment">% Kalman filter</span>
0124 Pstar = <a href="lyapunov_symm.html" class="code" title="function [x,ns_var]=lyapunov_symm(a,b)">lyapunov_symm</a>(T,R*Q*transpose(R));
0125 Pinf = [];
0126 <span class="keyword">elseif</span> options_.lik_init == 2 <span class="comment">% Old Diffuse Kalman filter</span>
0127 Pstar = 10*eye(np);
0128 Pinf = [];
0129 <span class="keyword">elseif</span> options_.lik_init == 3 <span class="comment">% Diffuse Kalman filter</span>
0130 Pstar = zeros(np,np);
0131 ivs = bayestopt_.i_T_var_stable;
0132 Pstar(ivs,ivs) = <a href="lyapunov_symm.html" class="code" title="function [x,ns_var]=lyapunov_symm(a,b)">lyapunov_symm</a>(T(ivs,ivs),R(ivs,:)*Q* <span class="keyword">...</span>
0133 transpose(R(ivs,:)));
0134 Pinf = bayestopt_.Pinf;
0135 <span class="comment">% by M. Ratto</span>
0136 RR=T(:,find(~ismember([1:np],ivs)));
0137 i=find(abs(RR)&gt;1.e-10);
0138 R0=zeros(size(RR));
0139 R0(i)=sign(RR(i));
0140 Pinf=R0*R0';
0141 <span class="comment">% by M. Ratto</span>
0142 <span class="keyword">end</span>
0143 <span class="comment">%------------------------------------------------------------------------------</span>
0144 <span class="comment">% 4. Likelihood evaluation</span>
0145 <span class="comment">%------------------------------------------------------------------------------</span>
0146 <span class="keyword">if</span> estim_params_.nvn
0147 <span class="keyword">if</span> options_.kalman_algo == 1
0148 LIK = <a href="DiffuseLikelihoodH1.html" class="code" title="function [LIK, lik] = DiffuseLikelihoodH1(T,R,Q,H,Pinf,Pstar,Y,trend,start)">DiffuseLikelihoodH1</a>(T,R,Q,H,Pinf,Pstar,data,trend,start);
0149 <span class="keyword">if</span> isinf(LIK) &amp; ~estim_params_.ncn <span class="comment">%% The univariate approach considered here doesn't</span>
0150 <span class="comment">%% apply when H has some off-diagonal elements.</span>
0151 LIK = <a href="DiffuseLikelihoodH3.html" class="code" title="function [LIK, lik] = DiffuseLikelihoodH3(T,R,Q,H,Pinf,Pstar,Y,trend,start)">DiffuseLikelihoodH3</a>(T,R,Q,H,Pinf,Pstar,data,trend,start);
0152 <span class="keyword">elseif</span> isinf(LIK) &amp; estim_params_.ncn
0153 LIK = <a href="DiffuseLikelihoodH3corr.html" class="code" title="function LIK = DiffuseLikelihoodH3corr(T,R,Q,H,Pinf,Pstar,Y,trend,start)">DiffuseLikelihoodH3corr</a>(T,R,Q,H,Pinf,Pstar,data,trend,start);
0154 <span class="keyword">end</span>
0155 <span class="keyword">elseif</span> options_.kalman_algo == 3
0156 <span class="keyword">if</span> ~estim_params_.ncn <span class="comment">%% The univariate approach considered here doesn't</span>
0157 <span class="comment">%% apply when H has some off-diagonal elements.</span>
0158 LIK = <a href="DiffuseLikelihoodH3.html" class="code" title="function [LIK, lik] = DiffuseLikelihoodH3(T,R,Q,H,Pinf,Pstar,Y,trend,start)">DiffuseLikelihoodH3</a>(T,R,Q,H,Pinf,Pstar,data,trend,start);
0159 <span class="keyword">else</span>
0160 LIK = <a href="DiffuseLikelihoodH3corr.html" class="code" title="function LIK = DiffuseLikelihoodH3corr(T,R,Q,H,Pinf,Pstar,Y,trend,start)">DiffuseLikelihoodH3corr</a>(T,R,Q,H,Pinf,Pstar,data,trend,start);
0161 <span class="keyword">end</span>
0162 <span class="keyword">end</span>
0163 <span class="keyword">else</span>
0164 <span class="keyword">if</span> options_.kalman_algo == 1
0165 nv = size(bayestopt_.Z,1);
0166 LIK = <a href="kalman_filter.html" class="code" title="function [loglik, per, d] = kalman_filter(varargin)">kalman_filter</a>(bayestopt_.Z,zeros(nv,nv),T,R,Q,data,zeros(size(T,1),1),Pstar,<span class="string">'u'</span>);
0167 <span class="comment">% LIK = DiffuseLikelihood1(T,R,Q,Pinf,Pstar,data,trend,start);</span>
0168 <span class="keyword">if</span> isinf(LIK)
0169 LIK = <a href="DiffuseLikelihood3.html" class="code" title="function [LIK, lik] = DiffuseLikelihood3(T,R,Q,Pinf,Pstar,Y,trend,start)%//Z,T,R,Q,Pinf,Pstar,Y)">DiffuseLikelihood3</a>(T,R,Q,Pinf,Pstar,data,trend,start);
0170 <span class="keyword">end</span>
0171 <span class="keyword">elseif</span> options_.kalman_algo == 3
0172 LIK = <a href="DiffuseLikelihood3.html" class="code" title="function [LIK, lik] = DiffuseLikelihood3(T,R,Q,Pinf,Pstar,Y,trend,start)%//Z,T,R,Q,Pinf,Pstar,Y)">DiffuseLikelihood3</a>(T,R,Q,Pinf,Pstar,data,trend,start);
0173 <span class="keyword">end</span>
0174 <span class="keyword">end</span>
0175 <span class="keyword">if</span> imag(LIK) ~= 0
0176 likelihood = bayestopt_.penalty;
0177 <span class="keyword">else</span>
0178 likelihood = LIK;
0179 <span class="keyword">end</span>
0180 <span class="comment">% ------------------------------------------------------------------------------</span>
0181 <span class="comment">% Adds prior if necessary</span>
0182 <span class="comment">% ------------------------------------------------------------------------------</span>
0183 lnprior = <a href="priordens.html" class="code" title="function lnprior = priordens(para, pshape, p1, p2, p3, p4)">priordens</a>(xparam1,bayestopt_.pshape,bayestopt_.p1,bayestopt_.p2,bayestopt_.p3,bayestopt_.p4);
0184 fval = (likelihood-lnprior);</pre></div>
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<title>Description of DsgeSmoother</title>
<meta name="keywords" content="DsgeSmoother">
<meta name="description" content="stephane.adjemian@cepremap.cnrs.fr [09-07-2004]">
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<div><a href="../index.html">Home</a> &gt; <a href="index.html">.</a> &gt; DsgeSmoother.m</div>
<!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png">&nbsp;Master index</a></td>
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<h1>DsgeSmoother
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>stephane.adjemian@cepremap.cnrs.fr [09-07-2004]</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK] = DsgeSmoother(xparam1,gend,Y) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> stephane.adjemian@cepremap.cnrs.fr [09-07-2004]
Adapted from mj_optmumlik.m</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="DiffuseKalmanSmoother1.html" class="code" title="function [alphahat,etahat,a, aK] = DiffuseKalmanSmoother1(T,R,Q,Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf)">DiffuseKalmanSmoother1</a> modified by M. Ratto:</li><li><a href="DiffuseKalmanSmoother3.html" class="code" title="function [alphahat,etahat,a1, aK] = DiffuseKalmanSmoother3(T,R,Q,Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf)">DiffuseKalmanSmoother3</a> function [a,etahat,a1, aK] = DiffuseKalmanSmoother3(T,R,Q,Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf)</li><li><a href="DiffuseKalmanSmootherH1.html" class="code" title="function [alphahat,epsilonhat,etahat,a, aK] = DiffuseKalmanSmootherH1(T,R,Q,H,Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf)">DiffuseKalmanSmootherH1</a> modified by M. Ratto:</li><li><a href="DiffuseKalmanSmootherH3.html" class="code" title="function [alphahat,epsilonhat,etahat,a1, aK] = DiffuseKalmanSmootherH3(T,R,Q,H,Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf)">DiffuseKalmanSmootherH3</a> Modified by M. Ratto</li><li><a href="dynare_resolve.html" class="code" title="function [A,B,ys,info] = dynare_resolve()">dynare_resolve</a> </li><li><a href="lyapunov_symm.html" class="code" title="function [x,ns_var]=lyapunov_symm(a,b)">lyapunov_symm</a> solves x-a*x*a'=b for b (and then x) symmetrical</li><li><a href="set_all_parameters.html" class="code" title="function set_all_parameters(xparam1)">set_all_parameters</a> </li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="PosteriorSmoother.html" class="code" title="function PosteriorSmoother(Y,gend, type)">PosteriorSmoother</a> stephane.adjemian@ens.fr [09-25-2005]</li><li><a href="dynare_MC.html" class="code" title="function dynare_MC(var_list_)">dynare_MC</a> </li><li><a href="dynare_estimation.html" class="code" title="function dynare_estimation(var_list_)">dynare_estimation</a> </li><li><a href="filt_mc_.html" class="code" title="function [rmse_MC, ixx] = filt_mc_(vvarvecm, loadSA, pfilt, alpha, alpha2, OutDir, istart, alphaPC)">filt_mc_</a> copyright Marco Ratto 2006</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK] = DsgeSmoother(xparam1,gend,Y)</a>
0002 <span class="comment">% stephane.adjemian@cepremap.cnrs.fr [09-07-2004]</span>
0003 <span class="comment">%</span>
0004 <span class="comment">% Adapted from mj_optmumlik.m</span>
0005 <span class="keyword">global</span> bayestopt_ M_ oo_ estim_params_ options_
0006
0007 alphahat = [];
0008 epsilonhat = [];
0009 etahat = [];
0010 nobs = size(options_.varobs,1);
0011 smpl = size(Y,2);
0012
0013 <a href="set_all_parameters.html" class="code" title="function set_all_parameters(xparam1)">set_all_parameters</a>(xparam1);
0014
0015 <span class="comment">%------------------------------------------------------------------------------</span>
0016 <span class="comment">% 2. call model setup &amp; reduction program</span>
0017 <span class="comment">%------------------------------------------------------------------------------</span>
0018 [T,R,SteadyState] = <a href="dynare_resolve.html" class="code" title="function [A,B,ys,info] = dynare_resolve()">dynare_resolve</a>;
0019 bayestopt_.mf = bayestopt_.mf2;
0020 <span class="keyword">if</span> options_.loglinear == 1
0021 constant = log(SteadyState(bayestopt_.mfys));
0022 <span class="keyword">else</span>
0023 constant = SteadyState(bayestopt_.mfys);
0024 <span class="keyword">end</span>
0025 trend_coeff = zeros(nobs,1);
0026 <span class="keyword">if</span> bayestopt_.with_trend == 1
0027 trend_coeff = zeros(nobs,1);
0028 nx1 = estim_params_.nvx+estim_params_.nvn+estim_params_.ncx+estim_params_.ncn;
0029 <span class="keyword">for</span> i=1:nobs
0030 trend_coeff(i) = evalin(<span class="string">'base'</span>,bayestopt_.trend_coeff{i});
0031 <span class="keyword">end</span>
0032 trend = constant*ones(1,gend)+trend_coeff*(1:gend);
0033 <span class="keyword">else</span>
0034 trend = constant*ones(1,gend);
0035 <span class="keyword">end</span>
0036 start = options_.presample+1;
0037 np = size(T,1);
0038 mf = bayestopt_.mf;
0039 <span class="comment">% ------------------------------------------------------------------------------</span>
0040 <span class="comment">% 3. Initial condition of the Kalman filter</span>
0041 <span class="comment">% ------------------------------------------------------------------------------</span>
0042 <span class="comment">%</span>
0043 <span class="comment">% C'est ici qu'il faut dterminer Pinf et Pstar. Si le modle est stationnaire,</span>
0044 <span class="comment">% alors il suffit de poser Pstar comme la solution de l'uation de Lyapounov et</span>
0045 <span class="comment">% Pinf=[].</span>
0046 <span class="comment">%</span>
0047 Q = M_.Sigma_e;
0048 H = M_.H;
0049
0050 <span class="keyword">if</span> options_.lik_init == 1 <span class="comment">% Kalman filter</span>
0051 Pstar = <a href="lyapunov_symm.html" class="code" title="function [x,ns_var]=lyapunov_symm(a,b)">lyapunov_symm</a>(T,R*Q*transpose(R));
0052 Pinf = [];
0053 <span class="keyword">elseif</span> options_.lik_init == 2 <span class="comment">% Old Diffuse Kalman filter</span>
0054 Pstar = 10*eye(np);
0055 Pinf = [];
0056 <span class="keyword">elseif</span> options_.lik_init == 3 <span class="comment">% Diffuse Kalman filter</span>
0057 Pstar = zeros(np,np);
0058 ivs = bayestopt_.i_T_var_stable;
0059 Pstar(ivs,ivs) = <a href="lyapunov_symm.html" class="code" title="function [x,ns_var]=lyapunov_symm(a,b)">lyapunov_symm</a>(T(ivs,ivs),R(ivs,:)*Q* <span class="keyword">...</span>
0060 transpose(R(ivs,:)));
0061 Pinf = bayestopt_.Pinf;
0062 <span class="comment">% by M. Ratto</span>
0063 RR=T(:,find(~ismember([1:np],ivs)));
0064 i=find(abs(RR)&gt;1.e-10);
0065 R0=zeros(size(RR));
0066 R0(i)=sign(RR(i));
0067 Pinf=R0*R0';
0068 <span class="comment">% by M. Ratto</span>
0069 <span class="keyword">end</span>
0070 <span class="comment">% -----------------------------------------------------------------------------</span>
0071 <span class="comment">% 4. Kalman smoother</span>
0072 <span class="comment">% -----------------------------------------------------------------------------</span>
0073 <span class="keyword">if</span> estim_params_.nvn
0074 <span class="keyword">if</span> options_.kalman_algo == 1
0075 [alphahat,epsilonhat,etahat,ahat,aK] = <a href="DiffuseKalmanSmootherH1.html" class="code" title="function [alphahat,epsilonhat,etahat,a, aK] = DiffuseKalmanSmootherH1(T,R,Q,H,Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf)">DiffuseKalmanSmootherH1</a>(T,R,Q,H,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
0076 <span class="keyword">if</span> all(alphahat(:)==0)
0077 [alphahat,epsilonhat,etahat,ahat,aK] = <a href="DiffuseKalmanSmootherH3.html" class="code" title="function [alphahat,epsilonhat,etahat,a1, aK] = DiffuseKalmanSmootherH3(T,R,Q,H,Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf)">DiffuseKalmanSmootherH3</a>(T,R,Q,H,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
0078 <span class="keyword">end</span>
0079 <span class="keyword">elseif</span> options_.kalman_algo == 3
0080 [alphahat,epsilonhat,etahat,ahat,aK] = <a href="DiffuseKalmanSmootherH3.html" class="code" title="function [alphahat,epsilonhat,etahat,a1, aK] = DiffuseKalmanSmootherH3(T,R,Q,H,Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf)">DiffuseKalmanSmootherH3</a>(T,R,Q,H,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
0081 <span class="keyword">end</span>
0082 <span class="keyword">else</span>
0083 <span class="keyword">if</span> options_.kalman_algo == 1
0084 [alphahat,etahat,ahat,aK] = <a href="DiffuseKalmanSmoother1.html" class="code" title="function [alphahat,etahat,a, aK] = DiffuseKalmanSmoother1(T,R,Q,Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf)">DiffuseKalmanSmoother1</a>(T,R,Q,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
0085 <span class="keyword">if</span> all(alphahat(:)==0)
0086 [alphahat,etahat,ahat,aK] = <a href="DiffuseKalmanSmoother3.html" class="code" title="function [alphahat,etahat,a1, aK] = DiffuseKalmanSmoother3(T,R,Q,Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf)">DiffuseKalmanSmoother3</a>(T,R,Q,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
0087 <span class="keyword">end</span>
0088 <span class="keyword">elseif</span> options_.kalman_algo == 3
0089 [alphahat,etahat,ahat,aK] = <a href="DiffuseKalmanSmoother3.html" class="code" title="function [alphahat,etahat,a1, aK] = DiffuseKalmanSmoother3(T,R,Q,Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf)">DiffuseKalmanSmoother3</a>(T,R,Q,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
0090 <span class="keyword">end</span>
0091 <span class="keyword">end</span></pre></div>
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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"
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<title>Description of GetAllPosteriorDraws</title>
<meta name="keywords" content="GetAllPosteriorDraws">
<meta name="description" content="stephane.adjemian@ens.fr [09-09-2005]">
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
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<h1>GetAllPosteriorDraws
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>stephane.adjemian@ens.fr [09-09-2005]</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function Draws = GetAllPosteriorDraws(column,FirstMhFile,FirstLine,TotalNumberOfMhFile,NumberOfDraws) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> stephane.adjemian@ens.fr [09-09-2005]</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="CheckPath.html" class="code" title="function DirectoryName = CheckPath(type)">CheckPath</a> 06-03-2005</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="GetPosteriorParametersStatistics.html" class="code" title="function GetPosteriorParametersStatistics()">GetPosteriorParametersStatistics</a> stephane.adjemian@ens.fr [09-09-2005]</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function Draws = GetAllPosteriorDraws(column,FirstMhFile,FirstLine,TotalNumberOfMhFile,NumberOfDraws)</a>
0002 <span class="comment">% stephane.adjemian@ens.fr [09-09-2005]</span>
0003 <span class="keyword">global</span> M_ options_
0004
0005 nblck = options_.mh_nblck;
0006 iline = FirstLine;
0007 linee = 1;
0008 DirectoryName = <a href="CheckPath.html" class="code" title="function DirectoryName = CheckPath(type)">CheckPath</a>(<span class="string">'metropolis'</span>);
0009 Draws = zeros(NumberOfDraws*nblck,1);
0010 logpo = zeros(NumberOfDraws*nblck,1);
0011 ipost=0;
0012 <span class="keyword">if</span> column&lt;0,
0013 column=1;
0014 ipost=1;
0015 <span class="keyword">end</span>
0016
0017 <span class="keyword">for</span> file = FirstMhFile:TotalNumberOfMhFile
0018 <span class="keyword">for</span> blck = 1:nblck
0019 load([DirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_mh'</span> int2str(file) <span class="string">'_blck'</span> int2str(blck)],<span class="string">'x2'</span>,<span class="string">'logpo2'</span>)
0020 NumberOfLines = size(x2(iline:<span class="keyword">end</span>,:),1);
0021 Draws(linee:linee+NumberOfLines-1) = x2(iline:<span class="keyword">end</span>,column);
0022 logpo(linee:linee+NumberOfLines-1) = logpo2(iline:end);
0023 linee = linee+NumberOfLines;
0024 <span class="keyword">end</span>
0025 iline = 1;
0026 <span class="keyword">end</span>
0027
0028 <span class="keyword">if</span> ipost,
0029 Draws=logpo;
0030 <span class="keyword">end</span></pre></div>
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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"
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<head>
<title>Description of GetOneDraw</title>
<meta name="keywords" content="GetOneDraw">
<meta name="description" content="stephane.adjemian@ens.fr [09-25-2005]">
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<div><a href="../index.html">Home</a> &gt; <a href="index.html">.</a> &gt; GetOneDraw.m</div>
<!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png">&nbsp;Master index</a></td>
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<h1>GetOneDraw
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>stephane.adjemian@ens.fr [09-25-2005]</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function [xparams, logpost] = GetOneDraw(type) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> stephane.adjemian@ens.fr [09-25-2005]</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="metropolis_draw.html" class="code" title="function [xparams, logpost]=metropolis_draw(init)">metropolis_draw</a> </li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="PosteriorIRF.html" class="code" title="function PosteriorIRF(type)">PosteriorIRF</a> stephane.adjemian@ens.fr [09-25-2005]</li><li><a href="PosteriorSmoother.html" class="code" title="function PosteriorSmoother(Y,gend, type)">PosteriorSmoother</a> stephane.adjemian@ens.fr [09-25-2005]</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function [xparams, logpost] = GetOneDraw(type)</a>
0002 <span class="comment">% stephane.adjemian@ens.fr [09-25-2005]</span>
0003
0004 <span class="keyword">switch</span> type
0005 <span class="keyword">case</span> <span class="string">'posterior'</span>
0006 [xparams, logpost] = <a href="metropolis_draw.html" class="code" title="function [xparams, logpost]=metropolis_draw(init)">metropolis_draw</a>(0);
0007 <span class="keyword">case</span> <span class="string">'prior'</span>
0008 xparams = prior_draw(0);
0009 <span class="keyword">end</span></pre></div>
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<title>Description of GetPosteriorParametersStatistics</title>
<meta name="keywords" content="GetPosteriorParametersStatistics">
<meta name="description" content="stephane.adjemian@ens.fr [09-09-2005]">
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<!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png">&nbsp;Master index</a></td>
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<h1>GetPosteriorParametersStatistics
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>stephane.adjemian@ens.fr [09-09-2005]</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function GetPosteriorParametersStatistics() </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> stephane.adjemian@ens.fr [09-09-2005]</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="CheckPath.html" class="code" title="function DirectoryName = CheckPath(type)">CheckPath</a> 06-03-2005</li><li><a href="GetAllPosteriorDraws.html" class="code" title="function Draws = GetAllPosteriorDraws(column,FirstMhFile,FirstLine,TotalNumberOfMhFile,NumberOfDraws)">GetAllPosteriorDraws</a> stephane.adjemian@ens.fr [09-09-2005]</li><li><a href="posterior_moments.html" class="code" title="function [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(xx,info)">posterior_moments</a> stephane.adjemian@ens.fr [09-09-2005]</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="dynare_estimation.html" class="code" title="function dynare_estimation(var_list_)">dynare_estimation</a> </li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function GetPosteriorParametersStatistics()</a>
0002 <span class="comment">% stephane.adjemian@ens.fr [09-09-2005]</span>
0003 <span class="keyword">global</span> estim_params_ M_ options_ bayestopt_ oo_
0004
0005 TeX = options_.TeX;
0006 nblck = options_.mh_nblck;
0007 nvx = estim_params_.nvx;
0008 nvn = estim_params_.nvn;
0009 ncx = estim_params_.ncx;
0010 ncn = estim_params_.ncn;
0011 np = estim_params_.np ;
0012 nx = nvx+nvn+ncx+ncn+np;
0013
0014 DirectoryName = <a href="CheckPath.html" class="code" title="function DirectoryName = CheckPath(type)">CheckPath</a>(<span class="string">'metropolis'</span>);
0015 load([ DirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_mh_history'</span>])
0016 FirstMhFile = record.KeepedDraws.FirstMhFile;
0017 FirstLine = record.KeepedDraws.FirstLine; ifil = FirstLine;
0018 TotalNumberOfMhFiles = sum(record.MhDraws(:,2))
0019 TotalNumberOfMhDraws = sum(record.MhDraws(:,1));
0020 FirstMhFile = record.KeepedDraws.FirstMhFile;
0021 NumberOfDraws = TotalNumberOfMhDraws-floor(options_.mh_drop*TotalNumberOfMhDraws);
0022 clear record;
0023
0024 disp(<span class="string">' '</span>)
0025 disp(<span class="string">' '</span>)
0026 disp(<span class="string">'ESTIMATION RESULTS'</span>)
0027 disp(<span class="string">' '</span>)
0028 disp(sprintf(<span class="string">'Log data density is %f.'</span>,oo_.MarginalDensity.ModifiedHarmonicMean))
0029 pnames=[<span class="string">' '</span>;<span class="string">'beta '</span>;<span class="string">'gamm '</span>;<span class="string">'norm '</span>;<span class="string">'invg '</span>;<span class="string">'unif '</span>;<span class="string">'invg2'</span>];
0030 tit2 = sprintf(<span class="string">'%10s %7s %10s %14s %4s %6s\n'</span>,<span class="string">' '</span>,<span class="string">'prior mean'</span>,<span class="string">'post. mean'</span>,<span class="string">'conf. interval'</span>,<span class="string">'prior'</span>,<span class="string">'pstdev'</span>);
0031 <span class="keyword">if</span> np
0032 disp(<span class="string">' '</span>)
0033 disp(<span class="string">'parameters'</span>)
0034 disp(tit2)
0035 ip = nvx+nvn+ncx+ncn+1;
0036 <span class="keyword">for</span> i=1:np
0037 Draws = <a href="GetAllPosteriorDraws.html" class="code" title="function Draws = GetAllPosteriorDraws(column,FirstMhFile,FirstLine,TotalNumberOfMhFile,NumberOfDraws)">GetAllPosteriorDraws</a>(ip,FirstMhFile,FirstLine,TotalNumberOfMhFiles,NumberOfDraws);
0038 [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = <a href="posterior_moments.html" class="code" title="function [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(xx,info)">posterior_moments</a>(Draws,1);
0039 name = bayestopt_.name{ip};
0040 disp(sprintf(<span class="string">'%12s %7.3f %8.4f %7.4f %7.4f %4s %6.4f'</span>, <span class="keyword">...</span>
0041 name, <span class="keyword">...</span>
0042 bayestopt_.pmean(ip),post_mean,hpd_interval, <span class="keyword">...</span>
0043 pnames(bayestopt_.pshape(ip)+1,:), <span class="keyword">...</span>
0044 bayestopt_.pstdev(ip)));
0045 eval([<span class="string">'oo_.posterior_mean.parameters.'</span> name <span class="string">' = post_mean;'</span>]);
0046 eval([<span class="string">'oo_.posterior_hpdinf.parameters.'</span> name <span class="string">' = hpd_interval(1);'</span>]);
0047 eval([<span class="string">'oo_.posterior_hpdsup.parameters.'</span> name <span class="string">' = hpd_interval(2);'</span>]);
0048 eval([<span class="string">'oo_.posterior_median.'</span> name <span class="string">' = post_median;'</span>]);
0049 eval([<span class="string">'oo_.posterior_variance.'</span> name <span class="string">' = post_var;'</span>]);
0050 eval([<span class="string">'oo_.posterior_deciles.'</span> name <span class="string">' = post_deciles;'</span>]);
0051 eval([<span class="string">'oo_.posterior_density.'</span> name <span class="string">' = density;'</span>]);
0052 ip = ip+1;
0053 <span class="keyword">end</span>
0054 <span class="keyword">end</span>
0055 <span class="keyword">if</span> nvx
0056 ip = 1;
0057 disp(<span class="string">' '</span>)
0058 disp(<span class="string">'standard deviation of shocks'</span>)
0059 disp(tit2)
0060 <span class="keyword">for</span> i=1:nvx
0061 Draws = <a href="GetAllPosteriorDraws.html" class="code" title="function Draws = GetAllPosteriorDraws(column,FirstMhFile,FirstLine,TotalNumberOfMhFile,NumberOfDraws)">GetAllPosteriorDraws</a>(ip,FirstMhFile,FirstLine,TotalNumberOfMhFiles,NumberOfDraws);
0062 [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = <a href="posterior_moments.html" class="code" title="function [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(xx,info)">posterior_moments</a>(Draws,1);
0063 k = estim_params_.var_exo(i,1);
0064 name = deblank(M_.exo_names(k,:));
0065 disp(sprintf(<span class="string">'%12s %7.3f %8.4f %7.4f %7.4f %4s %6.4f'</span>, <span class="keyword">...</span>
0066 name,bayestopt_.pmean(ip),post_mean, <span class="keyword">...</span>
0067 hpd_interval,pnames(bayestopt_.pshape(ip)+1,:), <span class="keyword">...</span>
0068 bayestopt_.pstdev(ip)));
0069 M_.Sigma_e(k,k) = post_mean*post_mean;
0070 eval([<span class="string">'oo_.posterior_mean.shocks_std.'</span> name <span class="string">' = post_mean;'</span>]);
0071 eval([<span class="string">'oo_.posterior_hpdinf.shocks_std.'</span> name <span class="string">' = hpd_interval(1);'</span>]);
0072 eval([<span class="string">'oo_.posterior_hpdsup.shocks_std.'</span> name <span class="string">' = hpd_interval(2);'</span>]);
0073 eval([<span class="string">'oo_.posterior_median.shocks_std.'</span> name <span class="string">' = post_median;'</span>]);
0074 eval([<span class="string">'oo_.posterior_variance.shocks_std.'</span> name <span class="string">' = post_var;'</span>]);
0075 eval([<span class="string">'oo_.posterior_deciles.shocks_std.'</span> name <span class="string">' = post_deciles;'</span>]);
0076 eval([<span class="string">'oo_.posterior_density.shocks_std.'</span> name <span class="string">' = density;'</span>]);
0077 ip = ip+1;
0078 <span class="keyword">end</span>
0079 <span class="keyword">end</span>
0080 <span class="keyword">if</span> nvn
0081 disp(<span class="string">' '</span>)
0082 disp(<span class="string">'standard deviation of measurement errors'</span>)
0083 disp(tit2)
0084 ip = nvx+1;
0085 <span class="keyword">for</span> i=1:nvn
0086 Draws = <a href="GetAllPosteriorDraws.html" class="code" title="function Draws = GetAllPosteriorDraws(column,FirstMhFile,FirstLine,TotalNumberOfMhFile,NumberOfDraws)">GetAllPosteriorDraws</a>(ip,FirstMhFile,FirstLine,TotalNumberOfMhFiles,NumberOfDraws);
0087 [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = <a href="posterior_moments.html" class="code" title="function [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(xx,info)">posterior_moments</a>(Draws,1);
0088 name = deblank(options_.varobs(estim_params_.var_endo(i,1),:));
0089 disp(sprintf(<span class="string">'%12s %7.3f %8.4f %7.4f %7.4f %4s %6.4f'</span>, <span class="keyword">...</span>
0090 name,<span class="keyword">...</span>
0091 bayestopt_.pmean(ip), <span class="keyword">...</span>
0092 post_mean,hpd_interval, <span class="keyword">...</span>
0093 pnames(bayestopt_.pshape(ip)+1,:), <span class="keyword">...</span>
0094 bayestopt_.pstdev(ip)));
0095 eval([<span class="string">'oo_.posterior_mean.measurement_errors_std.'</span> name <span class="string">' = post_mean;'</span>]);
0096 eval([<span class="string">'oo_.posterior_hpdinf.measurement_errors_std.'</span> name <span class="string">' = hpd_interval(1);'</span>]);
0097 eval([<span class="string">'oo_.posterior_hpdsup.measurement_errors_std.'</span> name <span class="string">' = hpd_interval(2);'</span>]);
0098 eval([<span class="string">'oo_.posterior_median.measurement_errors_std.'</span> name <span class="string">' = post_median;'</span>]);
0099 eval([<span class="string">'oo_.posterior_variance.measurement_errors_std.'</span> name <span class="string">' = post_var;'</span>]);
0100 eval([<span class="string">'oo_.posterior_deciles.measurement_errors_std.'</span> name <span class="string">' = post_deciles;'</span>]);
0101 eval([<span class="string">'oo_.posterior_density.measurement_errors_std.'</span> name <span class="string">' = density;'</span>]);
0102 ip = ip+1;
0103 <span class="keyword">end</span>
0104 <span class="keyword">end</span>
0105 <span class="keyword">if</span> ncx
0106 disp(<span class="string">' '</span>)
0107 disp(<span class="string">'correlation of shocks'</span>)
0108 disp(tit2)
0109 ip = nvx+nvn+1;
0110 <span class="keyword">for</span> i=1:ncx
0111 Draws = <a href="GetAllPosteriorDraws.html" class="code" title="function Draws = GetAllPosteriorDraws(column,FirstMhFile,FirstLine,TotalNumberOfMhFile,NumberOfDraws)">GetAllPosteriorDraws</a>(ip,FirstMhFile,FirstLine,TotalNumberOfMhFiles,NumberOfDraws);
0112 [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = <a href="posterior_moments.html" class="code" title="function [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(xx,info)">posterior_moments</a>(Draws,1);
0113 k1 = estim_params_.corrx(i,1);
0114 k2 = estim_params_.corrx(i,2);
0115 name = [deblank(M_.exo_names(k1,:)) <span class="string">','</span> deblank(M_.exo_names(k2,:))];
0116 NAME = [deblank(M_.exo_names(k1,:)) <span class="string">'_'</span> deblank(M_.exo_names(k2,:))];
0117 disp(sprintf(<span class="string">'%12s %7.3f %8.4f %7.4f %7.4f %4s %6.4f'</span>, name, <span class="keyword">...</span>
0118 bayestopt_.pmean(ip),post_mean,hpd_interval, <span class="keyword">...</span>
0119 pnames(bayestopt_.pshape(ip)+1,:), <span class="keyword">...</span>
0120 bayestopt_.pstdev(ip)));
0121 eval([<span class="string">'oo_.posterior_mean.shocks_corr.'</span> NAME <span class="string">' = post_mean;'</span>]);
0122 eval([<span class="string">'oo_.posterior_hpdinf.shocks_corr.'</span> NAME <span class="string">' = hpd_interval(1);'</span>]);
0123 eval([<span class="string">'oo_.posterior_hpdsup.shocks_corr.'</span> NAME <span class="string">' = hpd_interval(2);'</span>]);
0124 eval([<span class="string">'oo_.posterior_median.shocks_corr.'</span> NAME <span class="string">' = post_median;'</span>]);
0125 eval([<span class="string">'oo_.posterior_variance.shocks_corr.'</span> NAME <span class="string">' = post_var;'</span>]);
0126 eval([<span class="string">'oo_.posterior_deciles.shocks_corr.'</span> NAME <span class="string">' = post_deciles;'</span>]);
0127 eval([<span class="string">'oo_.posterior_density.shocks_corr.'</span> NAME <span class="string">' = density;'</span>]);
0128 M_.Sigma_e(k1,k2) = post_mean*sqrt(M_.Sigma_e(k1,k1)*M_.Sigma_e(k2,k2));
0129 M_.Sigma_e(k2,k1) = M_.Sigma_e(k1,k2);
0130 ip = ip+1;
0131 <span class="keyword">end</span>
0132 <span class="keyword">end</span>
0133 <span class="keyword">if</span> ncn
0134 disp(<span class="string">' '</span>)
0135 disp(<span class="string">'correlation of measurement errors'</span>)
0136 disp(tit2)
0137 ip = nvx+nvn+ncx+1;
0138 <span class="keyword">for</span> i=1:ncn
0139 Draws = <a href="GetAllPosteriorDraws.html" class="code" title="function Draws = GetAllPosteriorDraws(column,FirstMhFile,FirstLine,TotalNumberOfMhFile,NumberOfDraws)">GetAllPosteriorDraws</a>(ip,FirstMhFile,FirstLine,TotalNumberOfMhFiles,NumberOfDraws);
0140 [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = <a href="posterior_moments.html" class="code" title="function [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(xx,info)">posterior_moments</a>(Draws,1);
0141 k1 = estim_params_.corrn(i,1);
0142 k2 = estim_params_.corrn(i,2);
0143 name = [deblank(M_.endo_names(k1,:)) <span class="string">','</span> deblank(M_.endo_names(k2,:))];
0144 NAME = [deblank(M_.endo_names(k1,:)) <span class="string">'_'</span> deblank(M_.endo_names(k2,:))];
0145 disp(sprintf(<span class="string">'%12s %7.3f %8.4f %7.4f %7.4f %4s %6.4f'</span>, name, <span class="keyword">...</span>
0146 bayestopt_.pmean(ip),post_mean,hpd_interval, <span class="keyword">...</span>
0147 pnames(bayestopt_.pshape(ip)+1,:), <span class="keyword">...</span>
0148 bayestopt_.pstdev(ip)));
0149 eval([<span class="string">'oo_.posterior_mean.measurement_errors_corr.'</span> NAME <span class="string">' = post_mean;'</span>]);
0150 eval([<span class="string">'oo_.posterior_hpdinf.measurement_errors_corr.'</span> NAME <span class="string">' = hpd_interval(1);'</span>]);
0151 eval([<span class="string">'oo_.posterior_hpdsup.measurement_errors_corr.'</span> NAME <span class="string">' = hpd_interval(2);'</span>]);
0152 eval([<span class="string">'oo_.posterior_median.measurement_errors_corr.'</span> NAME <span class="string">' = post_median;'</span>]);
0153 eval([<span class="string">'oo_.posterior_variance.measurement_errors_corr.'</span> NAME <span class="string">' = post_var;'</span>]);
0154 eval([<span class="string">'oo_.posterior_deciles.measurement_errors_corr.'</span> NAME <span class="string">' = post_decile;'</span>]);
0155 eval([<span class="string">'oo_.posterior_density.measurement_errors_corr.'</span> NAME <span class="string">' = density;'</span>]);
0156 ip = ip+1;
0157 <span class="keyword">end</span>
0158 <span class="keyword">end</span></pre></div>
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<head>
<title>Description of InitSeed</title>
<meta name="keywords" content="InitSeed">
<meta name="description" content="stephane.adjemian@cepremap.cnrs.fr [12-02-2004]">
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<div><a href="../index.html">Home</a> &gt; <a href="index.html">.</a> &gt; InitSeed.m</div>
<!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png">&nbsp;Master index</a></td>
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<h1>InitSeed
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>stephane.adjemian@cepremap.cnrs.fr [12-02-2004]</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function InitSeed </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> stephane.adjemian@cepremap.cnrs.fr [12-02-2004]</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function InitSeed</a>
0002
0003 <span class="comment">% stephane.adjemian@cepremap.cnrs.fr [12-02-2004]</span>
0004
0005 normalseed = [ 220271 ;
0006 220378];
0007
0008 uniformseed = [ 0.6776 ; <span class="keyword">...</span>
0009 0.8239 ; <span class="keyword">...</span>
0010 0.6219 ; <span class="keyword">...</span>
0011 0.8169 ; <span class="keyword">...</span>
0012 0.2796 ; <span class="keyword">...</span>
0013 0.4337 ; <span class="keyword">...</span>
0014 0.6205 ; <span class="keyword">...</span>
0015 0.8994 ; <span class="keyword">...</span>
0016 0.9718 ; <span class="keyword">...</span>
0017 0.7993 ; <span class="keyword">...</span>
0018 0.4444 ; <span class="keyword">...</span>
0019 0.8896 ; <span class="keyword">...</span>
0020 0.9231 ; <span class="keyword">...</span>
0021 0.5838 ; <span class="keyword">...</span>
0022 0.1788 ; <span class="keyword">...</span>
0023 0.6556 ; <span class="keyword">...</span>
0024 0.7464 ; <span class="keyword">...</span>
0025 0.7545 ; <span class="keyword">...</span>
0026 0.2400 ; <span class="keyword">...</span>
0027 0.4431 ; <span class="keyword">...</span>
0028 0.3453 ; <span class="keyword">...</span>
0029 0.6687 ; <span class="keyword">...</span>
0030 0.6287 ; <span class="keyword">...</span>
0031 0.8371 ; <span class="keyword">...</span>
0032 0.8041 ; <span class="keyword">...</span>
0033 0.6802 ; <span class="keyword">...</span>
0034 0.5864 ; <span class="keyword">...</span>
0035 0.7713 ; <span class="keyword">...</span>
0036 0.8282 ; <span class="keyword">...</span>
0037 0.5748 ; <span class="keyword">...</span>
0038 0.0999 ; <span class="keyword">...</span>
0039 0.6360 ; <span class="keyword">...</span>
0040 0 ; <span class="keyword">...</span>
0041 0.0000 ; <span class="keyword">...</span>
0042 0.0000];
0043
0044 randn(<span class="string">'state'</span>,normalseed);
0045 rand(<span class="string">'state'</span>,uniformseed);</pre></div>
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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"
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<title>Description of LPTAU</title>
<meta name="keywords" content="LPTAU">
<meta name="description" content="">
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<meta name="generator" content="m2html &copy; 2003 Guillaume Flandin">
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<!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png">&nbsp;Master index</a></td>
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<h1>LPTAU
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong></strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function VECTOR = LPTAU(I, N) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment">
I.M. SOBOL', V.I. TURCHANINOV, Yu.L. LEVITAN, B.V. SHUKHMAN
KELDYSH INSTITUTE OF APPLIED MATHEMATICS
RUSSIAN ACADEMY OF SCIENCES
QUASIRANDOM SEQUENCE GENERATORS
-------------------------------
28.11.1991
NOTE TO THE USER BY the NEA Data Bank:
This quasi random number generator has been made available to
you on condition that its identity is preserved when used
in computer programs. If its use leads to scientific publication
of results you should cite it in the references, in addition
no commercial use should be made unless agreed upon with the
main author (Prof. I.M. Sobol')
ABSTRACT
........
POINTS BELONGING TO LP-TAU SEQUENCES UNIFORMLY DISTRIBUTED IN THE
N-DIMENSIONAL UNIT CUBE ARE OFTEN USED IN NUMERICAL MATHEMATICS:
- AS NODES FOR MULTIDIMENSIONAL INTEGRATION;
- AS SEARCHING POINTS IN GLOBAL OPTIMIZATION;
- AS TRIAL POINTS IN MULTI-CRITERIA DECISION MAKING;
- AS QUASIRANDOM POINTS FOR QUASI-MONTECARLO ALGORITHMS;
- ETC.
THIS SUBROUTINE CONTAINS THE ALGORITHM FOR FAST GENERATION OF
LP-TAU SEQUENCES THAT ARE SUITABLE FOR MULTI-PROCESSOR COMPUTATIONS.
THE DIMENSIONS N.LE.51, THE NUMBER OF POINTS N.LT.2**30.
THE PROGRAMMING LANGUAGE IS FORTRAN-77. THIS SUBROUTINE IS AVAILABLE
ALSO IN %-LANGUAGE.
THE REPORT DESCRIBING THE ALGORITHM CONTAINS THE DESCRIPTION OF THE
ALGORITHM AND CERTAIN IMPORTANT PROPERTIES OF LP-TAU SEQUENCES AND
THEIR GENERALIZATIONS ARE DISCUSSED.
REFERENCE:
I.M. SOBOL', V.I. TURCHANINOV, Yu.L. LEVITAN, B.V. SHUKHMAN
KELDYSH INSTITUTE OF APPLIED MATHEMATICS
RUSSIAN ACADEMY OF SCIENCES
QUASIRANDOM SEQUENCE GENERATORS
MOSCOW 1992, IPM ZAK. NO.30 (100 COPIES)
------------------------------------------------------------------------
INPUT PARAMETERS:
I - NUMBER OF THE POINT (I=(0,2**30-1)),
N - DIMENSION OF THE POINT (0&lt;N&lt;52);
OUTPUT PARAMETER:
VECTOR(N) - N-VECTOR CONTAINING THE CARTESIAN CO-ORDINATES OF
THE I-TH POINT.
TO CALL THE SUBROUTINE WRITE:
CALL LPTAU(I,N,VECTOR)
WHERE I, N: INTEGER CAPABLE OF STORING 2**30 (INTEGER*4 ON IBM
OR OTHER 32 BIT/WORD MACHINES)
VECTOR: DOUBLE PRECISION ARRAY WHOSE LENGTH &lt; 52.
INTEGER QP
QP = QUANTITY POWER
PARAMETER (MAXDIM=51, QP=30, MAXNUM=2**30-1)</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="lptauSEQ.html" class="code" title="function [lpmat] = lptauSEQ(Nsam,Nvar)">lptauSEQ</a> [lpmat] = lptauSEQ(Nsam,Nvar)</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function VECTOR = LPTAU(I, N)</a>
0002 <span class="comment">%</span>
0003 <span class="comment">% I.M. SOBOL', V.I. TURCHANINOV, Yu.L. LEVITAN, B.V. SHUKHMAN</span>
0004 <span class="comment">% KELDYSH INSTITUTE OF APPLIED MATHEMATICS</span>
0005 <span class="comment">% RUSSIAN ACADEMY OF SCIENCES</span>
0006 <span class="comment">%</span>
0007 <span class="comment">% QUASIRANDOM SEQUENCE GENERATORS</span>
0008 <span class="comment">% -------------------------------</span>
0009 <span class="comment">%</span>
0010 <span class="comment">% 28.11.1991</span>
0011 <span class="comment">%</span>
0012 <span class="comment">% NOTE TO THE USER BY the NEA Data Bank:</span>
0013 <span class="comment">% This quasi random number generator has been made available to</span>
0014 <span class="comment">% you on condition that its identity is preserved when used</span>
0015 <span class="comment">% in computer programs. If its use leads to scientific publication</span>
0016 <span class="comment">% of results you should cite it in the references, in addition</span>
0017 <span class="comment">% no commercial use should be made unless agreed upon with the</span>
0018 <span class="comment">% main author (Prof. I.M. Sobol')</span>
0019 <span class="comment">%</span>
0020 <span class="comment">% ABSTRACT</span>
0021 <span class="comment">% ........</span>
0022 <span class="comment">%</span>
0023 <span class="comment">% POINTS BELONGING TO LP-TAU SEQUENCES UNIFORMLY DISTRIBUTED IN THE</span>
0024 <span class="comment">% N-DIMENSIONAL UNIT CUBE ARE OFTEN USED IN NUMERICAL MATHEMATICS:</span>
0025 <span class="comment">%</span>
0026 <span class="comment">% - AS NODES FOR MULTIDIMENSIONAL INTEGRATION;</span>
0027 <span class="comment">% - AS SEARCHING POINTS IN GLOBAL OPTIMIZATION;</span>
0028 <span class="comment">% - AS TRIAL POINTS IN MULTI-CRITERIA DECISION MAKING;</span>
0029 <span class="comment">% - AS QUASIRANDOM POINTS FOR QUASI-MONTECARLO ALGORITHMS;</span>
0030 <span class="comment">% - ETC.</span>
0031 <span class="comment">%</span>
0032 <span class="comment">% THIS SUBROUTINE CONTAINS THE ALGORITHM FOR FAST GENERATION OF</span>
0033 <span class="comment">% LP-TAU SEQUENCES THAT ARE SUITABLE FOR MULTI-PROCESSOR COMPUTATIONS.</span>
0034 <span class="comment">% THE DIMENSIONS N.LE.51, THE NUMBER OF POINTS N.LT.2**30.</span>
0035 <span class="comment">% THE PROGRAMMING LANGUAGE IS FORTRAN-77. THIS SUBROUTINE IS AVAILABLE</span>
0036 <span class="comment">% ALSO IN %-LANGUAGE.</span>
0037 <span class="comment">% THE REPORT DESCRIBING THE ALGORITHM CONTAINS THE DESCRIPTION OF THE</span>
0038 <span class="comment">% ALGORITHM AND CERTAIN IMPORTANT PROPERTIES OF LP-TAU SEQUENCES AND</span>
0039 <span class="comment">% THEIR GENERALIZATIONS ARE DISCUSSED.</span>
0040 <span class="comment">%</span>
0041 <span class="comment">% REFERENCE:</span>
0042 <span class="comment">% I.M. SOBOL', V.I. TURCHANINOV, Yu.L. LEVITAN, B.V. SHUKHMAN</span>
0043 <span class="comment">% KELDYSH INSTITUTE OF APPLIED MATHEMATICS</span>
0044 <span class="comment">% RUSSIAN ACADEMY OF SCIENCES</span>
0045 <span class="comment">%</span>
0046 <span class="comment">% QUASIRANDOM SEQUENCE GENERATORS</span>
0047 <span class="comment">% MOSCOW 1992, IPM ZAK. NO.30 (100 COPIES)</span>
0048 <span class="comment">%</span>
0049 <span class="comment">% ------------------------------------------------------------------------</span>
0050 <span class="comment">%</span>
0051 <span class="comment">% INPUT PARAMETERS:</span>
0052 <span class="comment">%</span>
0053 <span class="comment">% I - NUMBER OF THE POINT (I=(0,2**30-1)),</span>
0054 <span class="comment">% N - DIMENSION OF THE POINT (0&lt;N&lt;52);</span>
0055 <span class="comment">%</span>
0056 <span class="comment">% OUTPUT PARAMETER:</span>
0057 <span class="comment">%</span>
0058 <span class="comment">% VECTOR(N) - N-VECTOR CONTAINING THE CARTESIAN CO-ORDINATES OF</span>
0059 <span class="comment">% THE I-TH POINT.</span>
0060 <span class="comment">%</span>
0061 <span class="comment">%</span>
0062 <span class="comment">% TO CALL THE SUBROUTINE WRITE:</span>
0063 <span class="comment">%</span>
0064 <span class="comment">% CALL LPTAU(I,N,VECTOR)</span>
0065 <span class="comment">% WHERE I, N: INTEGER CAPABLE OF STORING 2**30 (INTEGER*4 ON IBM</span>
0066 <span class="comment">% OR OTHER 32 BIT/WORD MACHINES)</span>
0067 <span class="comment">% VECTOR: DOUBLE PRECISION ARRAY WHOSE LENGTH &lt; 52.</span>
0068 <span class="comment">%</span>
0069 <span class="comment">% INTEGER QP</span>
0070 <span class="comment">% QP = QUANTITY POWER</span>
0071 <span class="comment">%</span>
0072 <span class="comment">% PARAMETER (MAXDIM=51, QP=30, MAXNUM=2**30-1)</span>
0073 MAXDIM=51; QP=30; MAXNUM=2^30-1;
0074 <span class="comment">%</span>
0075 <span class="comment">% THE DIMENSION OF THE POINT CANNOT EXCEED MAXDIM</span>
0076 <span class="comment">% THE TOTAL NUMBER OF GENERATED POINTS CANNOT EXCEED 2**QP</span>
0077 <span class="comment">% MAXNUM=2**30-1 // 1073741823</span>
0078 <span class="comment">%</span>
0079 <span class="comment">% DOUBLE PRECISION VECTOR(N)</span>
0080 <span class="comment">% INTEGER I,N</span>
0081 <span class="comment">%</span>
0082 <span class="comment">% INTEGER PRVNUM,PRVDIM</span>
0083 <span class="comment">% INTEGER DIRECT(MAXDIM,QP), MASKV(MAXDIM)</span>
0084 <span class="comment">% DOUBLE PRECISION SCALE</span>
0085 <span class="comment">%</span>
0086 <span class="comment">% Translated into MATLAB by M. Ratto</span>
0087 VECTOR=zeros(1,N);
0088 SCALE =9.31322574615478516E-10;
0089
0090 <span class="keyword">persistent</span> PRVNUM PRVDIM MASKV DIRECT
0091 <span class="keyword">if</span> isempty(PRVNUM), PRVNUM=-2; <span class="keyword">end</span>,
0092 <span class="keyword">if</span> isempty(PRVDIM), PRVDIM=0; <span class="keyword">end</span>,
0093
0094 <span class="keyword">if</span> isempty(DIRECT), <span class="keyword">...</span>
0095 DIRECT(1:MAXDIM,1)=[
0096 536870912, 536870912, 536870912, 536870912, 536870912, <span class="keyword">...</span>
0097 536870912, 536870912, 536870912, 536870912, 536870912, <span class="keyword">...</span>
0098 536870912, 536870912, 536870912, 536870912, 536870912, <span class="keyword">...</span>
0099 536870912, 536870912, 536870912, 536870912, 536870912, <span class="keyword">...</span>
0100 536870912, 536870912, 536870912, 536870912, 536870912, <span class="keyword">...</span>
0101 536870912, 536870912, 536870912, 536870912, 536870912, <span class="keyword">...</span>
0102 536870912, 536870912, 536870912, 536870912, 536870912, <span class="keyword">...</span>
0103 536870912, 536870912, 536870912, 536870912, 536870912, <span class="keyword">...</span>
0104 536870912, 536870912, 536870912, 536870912, 536870912, <span class="keyword">...</span>
0105 536870912, 536870912, 536870912, 536870912, 536870912, <span class="keyword">...</span>
0106 536870912]';
0107 DIRECT(1:MAXDIM,2)=[
0108 805306368, 268435456, 805306368, 268435456, 805306368, <span class="keyword">...</span>
0109 268435456, 805306368, 268435456, 268435456, 805306368, <span class="keyword">...</span>
0110 268435456, 805306368, 268435456, 805306368, 268435456, <span class="keyword">...</span>
0111 805306368, 805306368, 268435456, 805306368, 268435456, <span class="keyword">...</span>
0112 805306368, 268435456, 805306368, 268435456, 268435456, <span class="keyword">...</span>
0113 805306368, 268435456, 805306368, 268435456, 805306368, <span class="keyword">...</span>
0114 268435456, 805306368, 805306368, 268435456, 805306368, <span class="keyword">...</span>
0115 268435456, 805306368, 268435456, 805306368, 268435456, <span class="keyword">...</span>
0116 268435456, 805306368, 268435456, 805306368, 268435456, <span class="keyword">...</span>
0117 805306368, 268435456, 805306368, 805306368, 268435456, <span class="keyword">...</span>
0118 805306368]';
0119 DIRECT(1:MAXDIM,3)=[
0120 939524096, 939524096, 134217728, 671088640, 402653184, <span class="keyword">...</span>
0121 402653184, 671088640, 134217728, 671088640, 402653184, <span class="keyword">...</span>
0122 939524096, 134217728, 671088640, 939524096, 134217728, <span class="keyword">...</span>
0123 671088640, 134217728, 939524096, 939524096, 402653184, <span class="keyword">...</span>
0124 402653184, 134217728, 671088640, 402653184, 671088640, <span class="keyword">...</span>
0125 402653184, 402653184, 671088640, 134217728, 134217728, <span class="keyword">...</span>
0126 939524096, 939524096, 939524096, 939524096, 134217728, <span class="keyword">...</span>
0127 671088640, 402653184, 402653184, 671088640, 134217728, <span class="keyword">...</span>
0128 671088640, 402653184, 939524096, 134217728, 671088640, <span class="keyword">...</span>
0129 939524096, 134217728, 671088640, 134217728, 939524096, <span class="keyword">...</span>
0130 939524096]';
0131 DIRECT(1:MAXDIM,4)=[
0132 1006632960, 67108864, 603979776, 1006632960, 335544320, <span class="keyword">...</span>
0133 469762048, 872415232, 738197504, 469762048, 872415232, <span class="keyword">...</span>
0134 1006632960, 67108864, 872415232, 469762048, 469762048, <span class="keyword">...</span>
0135 469762048, 1006632960, 335544320, 872415232, 603979776, <span class="keyword">...</span>
0136 201326592, 67108864, 335544320, 67108864, 201326592, <span class="keyword">...</span>
0137 738197504, 1006632960, 738197504, 603979776, 335544320, <span class="keyword">...</span>
0138 738197504, 67108864, 1006632960, 67108864, 603979776, <span class="keyword">...</span>
0139 1006632960, 335544320, 469762048, 872415232, 738197504, <span class="keyword">...</span>
0140 469762048, 872415232, 1006632960, 67108864, 872415232, <span class="keyword">...</span>
0141 469762048, 469762048, 469762048, 1006632960, 335544320, <span class="keyword">...</span>
0142 872415232]';
0143 DIRECT(1:MAXDIM,5)=[
0144 1040187392, 637534208, 1040187392, 838860800, 167772160, <span class="keyword">...</span>
0145 234881024, 167772160, 1040187392, 436207616, 33554432, <span class="keyword">...</span>
0146 570425344, 1040187392, 503316480, 838860800, 973078528, <span class="keyword">...</span>
0147 167772160, 234881024, 436207616, 771751936, 100663296, <span class="keyword">...</span>
0148 234881024, 905969664, 771751936, 33554432, 838860800, <span class="keyword">...</span>
0149 973078528, 905969664, 33554432, 301989888, 838860800, <span class="keyword">...</span>
0150 100663296, 234881024, 1040187392, 637534208, 1040187392, <span class="keyword">...</span>
0151 838860800, 167772160, 234881024, 167772160, 1040187392, <span class="keyword">...</span>
0152 436207616, 33554432, 570425344, 1040187392, 503316480, <span class="keyword">...</span>
0153 838860800, 973078528, 167772160, 234881024, 436207616, <span class="keyword">...</span>
0154 771751936]';
0155 DIRECT(1:MAXDIM,6)=[
0156 1056964608, 352321536, 50331648, 419430400, 956301312, <span class="keyword">...</span>
0157 889192448, 620756992, 117440512, 956301312, 922746880, <span class="keyword">...</span>
0158 822083584, 218103808, 587202560, 385875968, 452984832, <span class="keyword">...</span>
0159 218103808, 184549376, 285212672, 150994944, 956301312, <span class="keyword">...</span>
0160 352321536, 654311424, 553648128, 352321536, 788529152, <span class="keyword">...</span>
0161 956301312, 587202560, 251658240, 218103808, 721420288, <span class="keyword">...</span>
0162 251658240, 1056964608, 520093696, 889192448, 587202560, <span class="keyword">...</span>
0163 956301312, 419430400, 352321536, 83886080, 654311424, <span class="keyword">...</span>
0164 419430400, 385875968, 285212672, 754974720, 50331648, <span class="keyword">...</span>
0165 922746880, 989855744, 754974720, 721420288, 822083584, <span class="keyword">...</span>
0166 687865856]';
0167 DIRECT(1:MAXDIM,7)=[
0168 1065353216, 1065353216, 578813952, 25165824, 125829120, <span class="keyword">...</span>
0169 964689920, 327155712, 310378496, 360710144, 360710144, <span class="keyword">...</span>
0170 159383552, 444596224, 947912704, 662700032, 444596224, <span class="keyword">...</span>
0171 528482304, 578813952, 578813952, 75497472, 1065353216, <span class="keyword">...</span>
0172 92274688, 511705088, 897581056, 847249408, 662700032, <span class="keyword">...</span>
0173 931135488, 411041792, 713031680, 696254464, 528482304, <span class="keyword">...</span>
0174 209715200, 578813952, 1065353216, 1065353216, 578813952, <span class="keyword">...</span>
0175 25165824, 125829120, 964689920, 327155712, 310378496, <span class="keyword">...</span>
0176 360710144, 360710144, 159383552, 444596224, 947912704, <span class="keyword">...</span>
0177 662700032, 444596224, 528482304, 578813952, 578813952, <span class="keyword">...</span>
0178 75497472]';
0179 DIRECT(1:MAXDIM,8)=[
0180 1069547520, 4194304, 826277888, 624951296, 616562688, <span class="keyword">...</span>
0181 801112064, 952107008, 406847488, 398458880, 331350016, <span class="keyword">...</span>
0182 356515840, 46137344, 1010827264, 1069547520, 734003200, <span class="keyword">...</span>
0183 113246208, 490733568, 633339904, 910163968, 801112064, <span class="keyword">...</span>
0184 893386752, 851443712, 801112064, 918552576, 742391808, <span class="keyword">...</span>
0185 499122176, 62914560, 264241152, 708837376, 717225984, <span class="keyword">...</span>
0186 759169024, 499122176, 272629760, 423624704, 155189248, <span class="keyword">...</span>
0187 239075328, 205520896, 943718400, 373293056, 457179136, <span class="keyword">...</span>
0188 406847488, 71303168, 473956352, 54525952, 624951296, <span class="keyword">...</span>
0189 104857600, 1019215872, 901775360, 213909504, 281018368, <span class="keyword">...</span>
0190 851443712]';
0191 DIRECT(1:MAXDIM,9)=[
0192 1071644672, 543162368, 190840832, 329252864, 853540864, <span class="keyword">...</span>
0193 132120576, 778043392, 73400320, 178257920, 522190848, <span class="keyword">...</span>
0194 639631360, 534773760, 991952896, 333447168, 48234496, <span class="keyword">...</span>
0195 1059061760, 761266176, 673185792, 220200960, 396361728, <span class="keyword">...</span>
0196 362807296, 815792128, 819986432, 346030080, 39845888, <span class="keyword">...</span>
0197 752877568, 387973120, 643825664, 291504128, 274726912, <span class="keyword">...</span>
0198 568328192, 526385152, 673185792, 98566144, 396361728, <span class="keyword">...</span>
0199 727711744, 1042284544, 106954752, 299892736, 912261120, <span class="keyword">...</span>
0200 44040192, 895483904, 333447168, 551550976, 467664896, <span class="keyword">...</span>
0201 618659840, 606076928, 274726912, 245366784, 446693376, <span class="keyword">...</span>
0202 421527552]';
0203 DIRECT(1:MAXDIM,10)=[
0204 1072693248, 273678336, 644874240, 753926144, 495976448, <span class="keyword">...</span>
0205 869269504, 355467264, 57671680, 816840704, 961544192, <span class="keyword">...</span>
0206 804257792, 495976448, 347078656, 426770432, 1066401792, <span class="keyword">...</span>
0207 372244480, 84934656, 208666624, 313524224, 598736896, <span class="keyword">...</span>
0208 487587840, 965738496, 1011875840, 296747008, 393216000, <span class="keyword">...</span>
0209 523239424, 720371712, 823132160, 128974848, 407896064, <span class="keyword">...</span>
0210 747634688, 850395136, 873463808, 504365056, 481296384, <span class="keyword">...</span>
0211 686817280, 592445440, 995098624, 498073600, 969932800, <span class="keyword">...</span>
0212 586153984, 1039138816, 814743552, 523239424, 294649856, <span class="keyword">...</span>
0213 305135616, 506462208, 11534336, 449839104, 619708416, <span class="keyword">...</span>
0214 479199232]';
0215 DIRECT(1:MAXDIM,11)=[
0216 1073217536, 947388416, 1070071808, 977797120, 365428736, <span class="keyword">...</span>
0217 702021632, 461897728, 829947904, 425197568, 634912768, <span class="keyword">...</span>
0218 437780480, 582483968, 792199168, 315097088, 611844096, <span class="keyword">...</span>
0219 667418624, 166199296, 513277952, 187170816, 1036517376, <span class="keyword">...</span>
0220 25690112, 201850880, 443023360, 990380032, 63438848, <span class="keyword">...</span>
0221 211288064, 983040000, 1069023232, 421003264, 742916096, <span class="keyword">...</span>
0222 487063552, 363331584, 973602816, 286785536, 171442176, <span class="keyword">...</span>
0223 669515776, 110624768, 383254528, 289931264, 352845824, <span class="keyword">...</span>
0224 878182400, 655884288, 836239360, 765984768, 549978112, <span class="keyword">...</span>
0225 655884288, 85458944, 591921152, 563609600, 277348352, <span class="keyword">...</span>
0226 919076864]';
0227 DIRECT(1:MAXDIM,12)=[
0228 1073479680, 71565312, 2359296, 891551744, 158597120, <span class="keyword">...</span>
0229 383516672, 1019478016, 947126272, 621019136, 714866688, <span class="keyword">...</span>
0230 738459648, 265027584, 468975616, 131858432, 504627200, <span class="keyword">...</span>
0231 581173248, 266600448, 865861632, 658243584, 546045952, <span class="keyword">...</span>
0232 521404416, 304873472, 1060896768, 163840000, 305922048, <span class="keyword">...</span>
0233 257163264, 50069504, 773062656, 59506688, 779354112, <span class="keyword">...</span>
0234 165937152, 587988992, 486801408, 160694272, 90439680, <span class="keyword">...</span>
0235 423362560, 536608768, 614203392, 56885248, 999030784, <span class="keyword">...</span>
0236 10747904, 764674048, 25952256, 989069312, 352583680, <span class="keyword">...</span>
0237 799801344, 261357568, 873201664, 40108032, 769392640, <span class="keyword">...</span>
0238 254541824]';
0239 DIRECT(1:MAXDIM,13)=[
0240 1073610752, 644218880, 538836992, 455475200, 1062600704, <span class="keyword">...</span>
0241 139329536, 205651968, 905052160, 797048832, 452329472, <span class="keyword">...</span>
0242 973471744, 627703808, 614072320, 803078144, 637403136, <span class="keyword">...</span>
0243 835059712, 949878784, 662044672, 767950848, 426901504, <span class="keyword">...</span>
0244 448659456, 23986176, 1016201216, 524943360, 525991936, <span class="keyword">...</span>
0245 618790912, 781058048, 761659392, 458096640, 226361344, <span class="keyword">...</span>
0246 950665216, 952500224, 516030464, 337510400, 496107520, <span class="keyword">...</span>
0247 830865408, 944111616, 636354560, 978452480, 921567232, <span class="keyword">...</span>
0248 533594112, 7471104, 678035456, 471203840, 1065746432, <span class="keyword">...</span>
0249 575275008, 996540416, 909246464, 879362048, 637927424, <span class="keyword">...</span>
0250 25821184]';
0251 DIRECT(1:MAXDIM,14)=[
0252 1073676288, 357892096, 808648704, 2424832, 2555904, <span class="keyword">...</span>
0253 624230400, 69271552, 456851456, 1052966912, 600637440, <span class="keyword">...</span>
0254 487260160, 794624000, 386727936, 467599360, 798031872, <span class="keyword">...</span>
0255 630652928, 340983808, 493944832, 37945344, 264175616, <span class="keyword">...</span>
0256 263520256, 833421312, 235077632, 464846848, 534839296, <span class="keyword">...</span>
0257 992411648, 10813440, 367067136, 116457472, 115015680, <span class="keyword">...</span>
0258 928710656, 619773952, 813760512, 1043398656, 967770112, <span class="keyword">...</span>
0259 912850944, 72155136, 1009057792, 668532736, 462356480, <span class="keyword">...</span>
0260 267321344, 795803648, 635764736, 574160896, 1003421696, <span class="keyword">...</span>
0261 181075968, 56688640, 388562944, 190906368, 657915904, <span class="keyword">...</span>
0262 474939392]';
0263 DIRECT(1:MAXDIM,15)=[
0264 1073709056, 1073709056, 137003008, 547782656, 545095680, <span class="keyword">...</span>
0265 26836992, 34701312, 354385920, 925663232, 656965632, <span class="keyword">...</span>
0266 327581696, 894795776, 110067712, 1038057472, 209354752, <span class="keyword">...</span>
0267 596541440, 42631168, 471433216, 52527104, 666861568, <span class="keyword">...</span>
0268 706707456, 674070528, 824410112, 305496064, 136282112, <span class="keyword">...</span>
0269 847740928, 531464192, 222920704, 379289600, 507740160, <span class="keyword">...</span>
0270 11894784, 1053392896, 129990656, 557547520, 666468352, <span class="keyword">...</span>
0271 1061912576, 576684032, 1041334272, 380469248, 114196480, <span class="keyword">...</span>
0272 133070848, 517046272, 129990656, 790396928, 563773440, <span class="keyword">...</span>
0273 388333568, 661749760, 446791680, 737378304, 229998592, <span class="keyword">...</span>
0274 348225536]';
0275 DIRECT(1:MAXDIM,16)=[
0276 1073725440, 16384, 605372416, 275234816, 817971200, <span class="keyword">...</span>
0277 603963392, 555335680, 721534976, 997801984, 1028767744, <span class="keyword">...</span>
0278 407060480, 375275520, 256688128, 1021165568, 303349760, <span class="keyword">...</span>
0279 1022476288, 234143744, 106708992, 732971008, 733954048, <span class="keyword">...</span>
0280 789889024, 879575040, 764657664, 762658816, 1010843648, <span class="keyword">...</span>
0281 941080576, 827932672, 98942976, 1051738112, 624934912, <span class="keyword">...</span>
0282 993280000, 134070272, 201375744, 567558144, 882163712, <span class="keyword">...</span>
0283 649084928, 356564992, 489439232, 637091840, 60637184, <span class="keyword">...</span>
0284 199278592, 815677440, 927678464, 94519296, 419184640, <span class="keyword">...</span>
0285 933838848, 426655744, 911130624, 171393024, 561332224, <span class="keyword">...</span>
0286 471613440]';
0287 DIRECT(1:MAXDIM,17)=[
0288 1073733632, 536895488, 1043685376, 679944192, 417505280, <span class="keyword">...</span>
0289 301981696, 832561152, 210542592, 167501824, 1071341568, <span class="keyword">...</span>
0290 229302272, 970661888, 732176384, 576659456, 402464768, <span class="keyword">...</span>
0291 451584000, 368467968, 928260096, 933847040, 29319168, <span class="keyword">...</span>
0292 582934528, 772612096, 330014720, 647323648, 174071808, <span class="keyword">...</span>
0293 1008689152, 295919616, 353869824, 177774592, 580198400, <span class="keyword">...</span>
0294 381837312, 638574592, 637558784, 679370752, 504012800, <span class="keyword">...</span>
0295 747118592, 429973504, 1032609792, 932667392, 583360512, <span class="keyword">...</span>
0296 969498624, 1056333824, 660955136, 247488512, 153509888, <span class="keyword">...</span>
0297 242180096, 205840384, 797499392, 824565760, 234348544, <span class="keyword">...</span>
0298 842326016]';
0299 DIRECT(1:MAXDIM,18)=[
0300 1073737728, 268455936, 52785152, 1020628992, 345018368, <span class="keyword">...</span>
0301 452972544, 704442368, 255987712, 750759936, 697692160, <span class="keyword">...</span>
0302 196677632, 764604416, 485625856, 522022912, 680620032, <span class="keyword">...</span>
0303 362270720, 838103040, 83972096, 629133312, 46108672, <span class="keyword">...</span>
0304 867561472, 725422080, 184504320, 751112192, 191918080, <span class="keyword">...</span>
0305 306425856, 507310080, 30453760, 281858048, 604000256, <span class="keyword">...</span>
0306 208662528, 319557632, 318779392, 476139520, 863719424, <span class="keyword">...</span>
0307 567062528, 521179136, 712790016, 610299904, 293687296, <span class="keyword">...</span>
0308 1023086592, 549089280, 1065242624, 707751936, 363024384, <span class="keyword">...</span>
0309 16674816, 197136384, 1037561856, 195112960, 372707328, <span class="keyword">...</span>
0310 992751616]';
0311 DIRECT(1:MAXDIM,19)=[
0312 1073739776, 939554816, 580732928, 854333440, 172619776, <span class="keyword">...</span>
0313 511694848, 936142848, 518199296, 593348608, 225527808, <span class="keyword">...</span>
0314 900982784, 180279296, 168904704, 62814208, 754485248, <span class="keyword">...</span>
0315 730691584, 1005996032, 411174912, 249866240, 641669120, <span class="keyword">...</span>
0316 1008719872, 749066240, 860993536, 94177280, 432564224, <span class="keyword">...</span>
0317 226355200, 925784064, 995657728, 967731200, 436226048, <span class="keyword">...</span>
0318 913799168, 549894144, 964696064, 843315200, 445863936, <span class="keyword">...</span>
0319 1047422976, 548947968, 492066816, 953870336, 1002653696, <span class="keyword">...</span>
0320 861440000, 385636352, 325253120, 187353088, 653584384, <span class="keyword">...</span>
0321 1008269312, 748693504, 1013016576, 55814144, 255170560, <span class="keyword">...</span>
0322 260708352]';
0323 DIRECT(1:MAXDIM,20)=[
0324 1073740800, 67126272, 829514752, 423777280, 968297472, <span class="keyword">...</span>
0325 205511680, 147076096, 926669824, 202300416, 118395904, <span class="keyword">...</span>
0326 381332480, 1002738688, 743042048, 292551680, 584567808, <span class="keyword">...</span>
0327 284339200, 183936000, 616762368, 435221504, 159376384, <span class="keyword">...</span>
0328 907322368, 595696640, 247497728, 553735168, 826051584, <span class="keyword">...</span>
0329 564454400, 446024704, 214236160, 33661952, 251685888, <span class="keyword">...</span>
0330 660327424, 284244992, 859868160, 722502656, 622844928, <span class="keyword">...</span>
0331 324342784, 682374144, 400579584, 405353472, 605187072, <span class="keyword">...</span>
0332 840682496, 212956160, 157891584, 193201152, 437990400, <span class="keyword">...</span>
0333 573578240, 368053248, 580197376, 937905152, 565527552, <span class="keyword">...</span>
0334 89064448]';
0335 DIRECT(1:MAXDIM,21)=[
0336 1073741312, 637560320, 189496832, 27474432, 129338880, <span class="keyword">...</span>
0337 908054016, 641870336, 186375680, 302677504, 763663872, <span class="keyword">...</span>
0338 103878144, 325187072, 858254848, 922041856, 261924352, <span class="keyword">...</span>
0339 954978816, 292822528, 849512960, 210311680, 933232128, <span class="keyword">...</span>
0340 691981824, 155417088, 627070464, 416795136, 182081024, <span class="keyword">...</span>
0341 513433088, 848658944, 515770880, 627273216, 629169664, <span class="keyword">...</span>
0342 414566912, 147450368, 698353152, 244844032, 226578944, <span class="keyword">...</span>
0343 1020087808, 886978048, 389697024, 1007004160, 839646720, <span class="keyword">...</span>
0344 621924864, 549962240, 609583616, 735976960, 87342592, <span class="keyword">...</span>
0345 1058542080, 163066368, 307997184, 876471808, 794280448, <span class="keyword">...</span>
0346 675386880]';
0347 DIRECT(1:MAXDIM,22)=[
0348 1073741568, 352343296, 644236032, 636735232, 615860480, <span class="keyword">...</span>
0349 959444224, 287380736, 1007410432, 890187008, 399480576, <span class="keyword">...</span>
0350 520092928, 643311360, 816901376, 695310080, 1019229440, <span class="keyword">...</span>
0351 77034240, 733295872, 1035127552, 986582784, 332381952, <span class="keyword">...</span>
0352 334852352, 364956416, 596672256, 800381696, 480316672, <span class="keyword">...</span>
0353 574863104, 647347968, 702910208, 499965184, 364968704, <span class="keyword">...</span>
0354 120862976, 1023256320, 995114240, 13951232, 32520448, <span class="keyword">...</span>
0355 702127360, 45176064, 444945664, 237860096, 152839936, <span class="keyword">...</span>
0356 530633984, 429135616, 267272448, 884808960, 933712640, <span class="keyword">...</span>
0357 61605632, 174335744, 564911360, 302327552, 650589440, <span class="keyword">...</span>
0358 450649344]';
0359 DIRECT(1:MAXDIM,23)=[
0360 1073741696, 1065385856, 1073734272, 331949184, 842310784, <span class="keyword">...</span>
0361 799537536, 965852032, 369351808, 662886016, 86119808, <span class="keyword">...</span>
0362 865109888, 299633792, 422735488, 181087360, 174252416, <span class="keyword">...</span>
0363 1041212544, 840196224, 750314368, 391053440, 903306880, <span class="keyword">...</span>
0364 742365312, 236995200, 42492800, 946000512, 771692416, <span class="keyword">...</span>
0365 897405824, 613803136, 924258688, 808338304, 1038125440, <span class="keyword">...</span>
0366 683814272, 177186176, 766008960, 704549248, 194555008, <span class="keyword">...</span>
0367 306383744, 496592512, 416020864, 655186816, 1032204928, <span class="keyword">...</span>
0368 694773632, 577910144, 45797760, 910332544, 536014976, <span class="keyword">...</span>
0369 675946368, 987635840, 788223872, 353993856, 96313472, <span class="keyword">...</span>
0370 85248640]';
0371 DIRECT(1:MAXDIM,24)=[
0372 1073741760, 4210752, 12608, 744788544, 494377792, <span class="keyword">...</span>
0373 115601344, 769248448, 990895808, 851706304, 979326784, <span class="keyword">...</span>
0374 692061120, 429015104, 217132864, 736067008, 55694400, <span class="keyword">...</span>
0375 456152640, 631601984, 787264192, 898599104, 478383808, <span class="keyword">...</span>
0376 507774272, 458270272, 392995136, 872482496, 124824768, <span class="keyword">...</span>
0377 1034161344, 362141632, 1053833280, 943810496, 428920128, <span class="keyword">...</span>
0378 795835200, 835462848, 961843520, 606198080, 785652672, <span class="keyword">...</span>
0379 154954432, 491617344, 297351232, 580735552, 634587968, <span class="keyword">...</span>
0380 185277760, 141505600, 417673280, 106907456, 395575616, <span class="keyword">...</span>
0381 566958656, 352651200, 415242688, 26635200, 1030317504, <span class="keyword">...</span>
0382 247212992]';
0383 DIRECT(1:MAXDIM,25)=[
0384 1073741792, 543187040, 536882016, 981766752, 357858144, <span class="keyword">...</span>
0385 810665952, 369083488, 613015520, 86115232, 845149216, <span class="keyword">...</span>
0386 606076960, 1018241504, 35245536, 635403744, 236633696, <span class="keyword">...</span>
0387 407451232, 427671904, 460141792, 116344544, 990246688, <span class="keyword">...</span>
0388 1024892576, 883429408, 150655392, 173476896, 197666464, <span class="keyword">...</span>
0389 1016740448, 605376608, 970487008, 1006881248, 598265632, <span class="keyword">...</span>
0390 1022861728, 489055392, 216680608, 371439072, 53140576, <span class="keyword">...</span>
0391 965114400, 98534112, 209692256, 264598496, 321437280, <span class="keyword">...</span>
0392 545303840, 324119904, 876587488, 778239712, 802033120, <span class="keyword">...</span>
0393 44406496, 665829024, 803213920, 309742112, 735181344, <span class="keyword">...</span>
0394 1036125600]';
0395 DIRECT(1:MAXDIM,26)=[
0396 1073741808, 273698896, 805312112, 903123536, 153504624, <span class="keyword">...</span>
0397 689632208, 432848944, 859888752, 510853776, 240805744, <span class="keyword">...</span>
0398 250610192, 852489168, 139460144, 283082224, 222702928, <span class="keyword">...</span>
0399 342181488, 922872432, 187528656, 360637424, 814514736, <span class="keyword">...</span>
0400 301037840, 800872624, 272595184, 158627600, 238839088, <span class="keyword">...</span>
0401 927181136, 710248688, 788854608, 1048376560, 484709072, <span class="keyword">...</span>
0402 85709008, 1065469744, 429237328, 490778384, 848024144, <span class="keyword">...</span>
0403 443114288, 687907504, 474573648, 45706416, 681465296, <span class="keyword">...</span>
0404 805303216, 14567920, 369839504, 440402480, 797652624, <span class="keyword">...</span>
0405 115959088, 929784560, 91226544, 205943056, 288358704, <span class="keyword">...</span>
0406 950217296]';
0407 DIRECT(1:MAXDIM,27)=[
0408 1073741816, 947419256, 134232008, 460100200, 1073685336, <span class="keyword">...</span>
0409 398354904, 1069834248, 686054696, 108299224, 273898840, <span class="keyword">...</span>
0410 731382552, 938170664, 86812552, 677346808, 602208712, <span class="keyword">...</span>
0411 652635752, 209797064, 323968376, 384078968, 561178056, <span class="keyword">...</span>
0412 923399256, 996597176, 942777416, 885167400, 89791048, <span class="keyword">...</span>
0413 956122504, 87393464, 987661336, 993412952, 827749496, <span class="keyword">...</span>
0414 903211272, 33649816, 594875176, 65052056, 822835240, <span class="keyword">...</span>
0415 625704888, 1065374584, 612232920, 536299720, 1046858504, <span class="keyword">...</span>
0416 939518840, 160843032, 654656088, 744496936, 872197704, <span class="keyword">...</span>
0417 219111576, 829112632, 455614152, 1064192952, 313010856, <span class="keyword">...</span>
0418 820754920]';
0419 DIRECT(1:MAXDIM,28)=[
0420 1073741820, 71582788, 603989028, 37500, 120660, <span class="keyword">...</span>
0421 182195932, 213921796, 473570692, 41763004, 156352828, <span class="keyword">...</span>
0422 88343188, 698012780, 666108868, 337608156, 1054329884, <span class="keyword">...</span>
0423 799472220, 641885244, 392104980, 502284340, 1002405628, <span class="keyword">...</span>
0424 249907140, 75586228, 206587660, 565275348, 1021426548, <span class="keyword">...</span>
0425 425987996, 598058580, 401940420, 919427316, 822049324, <span class="keyword">...</span>
0426 115655868, 759299588, 562394644, 990942164, 1003212268, <span class="keyword">...</span>
0427 598750292, 675334852, 675293340, 445665316, 903023756, <span class="keyword">...</span>
0428 872412692, 172640916, 1051615436, 91229620, 94586692, <span class="keyword">...</span>
0429 344873452, 7029156, 683421900, 434258804, 955002092, <span class="keyword">...</span>
0430 436424380]';
0431 DIRECT(1:MAXDIM,29)=[
0432 1073741822, 644245094, 1040195102, 537039474, 537089354, <span class="keyword">...</span>
0433 642656334, 73402402, 664239174, 1014620426, 500917234, <span class="keyword">...</span>
0434 1042677826, 347788318, 1069154738, 373259762, 362587674, <span class="keyword">...</span>
0435 239395914, 132283950, 903121466, 445210638, 968851198, <span class="keyword">...</span>
0436 230153254, 935549622, 308815630, 861891286, 496995094, <span class="keyword">...</span>
0437 670740382, 657311830, 573565382, 248331478, 1064650798, <span class="keyword">...</span>
0438 338312798, 669059078, 1065190902, 379125622, 111897166, <span class="keyword">...</span>
0439 520650422, 740300390, 1046483950, 66254898, 992513134, <span class="keyword">...</span>
0440 234871606, 610553330, 450553866, 566758134, 787800806, <span class="keyword">...</span>
0441 1071709866, 971732606, 528102354, 790919122, 917384366, <span class="keyword">...</span>
0442 656244162]';
0443 DIRECT(1:MAXDIM,30)=[
0444 1073741823, 357913941, 50344755, 268538457, 805540473, <span class="keyword">...</span>
0445 3487029, 3146769, 592038967, 729591963, 781578389, <span class="keyword">...</span>
0446 66781679, 113956361, 331153483, 967802327, 935343371, <span class="keyword">...</span>
0447 324351309, 609305915, 818137857, 131059769, 918519549, <span class="keyword">...</span>
0448 827341719, 922770101, 456972047, 363883221, 1057014661, <span class="keyword">...</span>
0449 900956063, 580478293, 520383687, 315470533, 227601711, <span class="keyword">...</span>
0450 169598765, 909227271, 796983815, 349496709, 393974911, <span class="keyword">...</span>
0451 378320037, 777004343, 927999269, 616377385, 345930959, <span class="keyword">...</span>
0452 989849787, 627400495, 892675125, 260314121, 1041964063, <span class="keyword">...</span>
0453 531367163, 195776757, 237309785, 949187605, 719002587, <span class="keyword">...</span>
0454 495745187]';
0455 <span class="keyword">end</span>
0456
0457 <span class="comment">%</span>
0458 <span class="comment">% TRAP FOR A WRONG SUBROUTINE CALL</span>
0459 <span class="comment">%</span>
0460 <span class="keyword">if</span> ((I&lt;0) | (N&lt;1) | (I&gt;MAXNUM) | (N&gt;MAXDIM)),
0461 disp(<span class="string">'LP-TAU CALL FAILED'</span>)
0462 disp(<span class="string">' PRESS &lt;ENTER&gt; TO EXIT LPTAU'</span>)
0463 pause
0464 <span class="keyword">return</span>
0465 <span class="keyword">end</span>
0466 <span class="keyword">if</span> ((PRVNUM+1==I) &amp; (N&lt;=PRVDIM)),
0467 <span class="comment">%</span>
0468 <span class="comment">% RECURRENT GENERATION OF THE POINT</span>
0469 <span class="comment">%</span>
0470 <span class="comment">%</span>
0471 <span class="comment">% SEARCH POSITION OF THE RIGHTMOST &quot;1&quot;</span>
0472 <span class="comment">% IN THE BINARY REPRESENTATION OF I</span>
0473 <span class="comment">%</span>
0474 L=0;
0475 POS=0;
0476 <span class="keyword">while</span> L&lt;QP &amp; POS==0,
0477 L=L+1;
0478 POS=bitand(bitshift(I,-(L-1)),1);
0479 <span class="keyword">end</span>
0480 <span class="comment">%</span>
0481 <span class="comment">% RIGHTMOST POSITION IS L</span>
0482 <span class="comment">%</span>
0483 <span class="keyword">for</span> J=1:N
0484 MASKV(J)=bitxor(MASKV(J),DIRECT(J,L));
0485 VECTOR(J)=MASKV(J)*SCALE;
0486 <span class="keyword">end</span>
0487 <span class="keyword">else</span>
0488 <span class="comment">%</span>
0489 <span class="comment">% GENERATION OF THE POINT FROM &quot;I&quot; AND &quot;N&quot;</span>
0490 <span class="comment">%</span>
0491 MASKV=zeros(1,N);
0492 IM=bitxor(I,bitshift(I,-1));
0493 M=0;
0494 <span class="keyword">while</span> IM~=0 &amp; M&lt;QP,
0495 M=M+1;
0496 <span class="keyword">if</span> (bitand(IM,1)==1),
0497 <span class="keyword">for</span> J=1:N
0498 MASKV(J)=bitxor(MASKV(J),DIRECT(J,M));
0499 <span class="keyword">end</span>
0500 <span class="keyword">end</span>
0501 IM=bitshift(IM,-1);
0502 <span class="keyword">end</span>
0503 <span class="keyword">for</span> J=1:N
0504 VECTOR(J)=MASKV(J)*SCALE;
0505 <span class="keyword">end</span>
0506 <span class="keyword">end</span>
0507 <span class="comment">%</span>
0508 PRVNUM=I;
0509 PRVDIM=N;
0510 <span class="keyword">return</span>
0511
0512</pre></div>
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<h1>MakeAllFigures
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>stephane.adjemian@cepremap.cnrs.fr [02-04-2005]</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function MakeAllFigures(NumberOfPlots,Caption,FigureProperties,Info) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> stephane.adjemian@cepremap.cnrs.fr [02-04-2005]</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function MakeAllFigures(NumberOfPlots,Caption,FigureProperties,Info)</a>
0002 <span class="comment">% stephane.adjemian@cepremap.cnrs.fr [02-04-2005]</span>
0003 <span class="keyword">global</span> M_ options_
0004
0005 FigHandle = figure(<span class="string">'Name'</span>,FigureProperties.Name);
0006
0007 NAMES = cell(NumberOfPlots,1);
0008 <span class="keyword">if</span> options_.TeX
0009 TeXNAMES = cell(NumberOfPlots,1);
0010 <span class="keyword">end</span>
0011
0012 <span class="keyword">if</span> NumberOfPlots == 9
0013 nr = 3;
0014 nc = 3;
0015 <span class="keyword">elseif</span> NumberOfPlots == 8
0016 nr = 3;
0017 nc = 3;
0018 <span class="keyword">elseif</span> NumberOfPlots == 7
0019 nr = 3;
0020 nc = 3;
0021 <span class="keyword">elseif</span> NumberOfPlots == 6
0022 nr = 2;
0023 nc = 3;
0024 <span class="keyword">elseif</span> NumberOfPlots == 5
0025 nr = 3;
0026 nc = 2;
0027 <span class="keyword">elseif</span> NumberOfPlots == 4
0028 nr = 2;
0029 nc = 2;
0030 <span class="keyword">elseif</span> NumberOfPlots == 3
0031 nr = 2;
0032 nc = 2;
0033 <span class="keyword">elseif</span> NumberOfPlots == 2
0034 nr = 1;
0035 nc = 2;
0036 <span class="keyword">elseif</span> NumberOfPlots == 1
0037 nr = 1;
0038 nc = 1;
0039 <span class="keyword">end</span>
0040
0041 <span class="keyword">for</span> plt = 1:NumberOfPlots
0042 eval([<span class="string">'NumberOfCurves = Info.Box'</span> int2str(plt) <span class="string">'.Number;'</span>])
0043 NumberOfObservations = zeros(2,1);
0044 x = cell(NumberOfCurves,1);
0045 y = cell(NumberOfCurves,1);
0046 PltType = cell(NumberofCurves,1);
0047 top = NaN(NumberOfCurves,1);
0048 bottom = NaN(NumberOfCurves,1);
0049 binf = NaN(NumberOfCurves,1);
0050 bsup = NaN(NumberOfCurves,1);
0051 <span class="keyword">for</span> curve = 1:NumberOfCurves
0052 eval([<span class="string">'x{'</span> curve <span class="string">'} = Info.Box'</span> int2str(plt) <span class="string">'.Curve'</span> int2str(curve) <span class="string">'.xdata;'</span>])
0053 eval([<span class="string">'y{'</span> curve <span class="string">'} = Info.Box'</span> int2str(plt) <span class="string">'.Curve'</span> int2str(curve) <span class="string">'.ydata;'</span>])
0054 eval([<span class="string">'name = Info.Box'</span> int2str(plt) <span class="string">'.Curve'</span> int2str(curve) <span class="string">'.variablename;'</span>])
0055 eval([<span class="string">'PltType{'</span> curve <span class="string">'} = Info.Box'</span> int2str(plt) <span class="string">'.Curve'</span> int2str(curve) <span class="string">'.type'</span>]);
0056 <span class="keyword">if</span> length(x{curve})-length(y{curve})
0057 disp(<span class="string">'MakeFigure :: The number of observations in x doesn''t match with '</span>)
0058 disp([<span class="string">'the number of observation in y for '</span> name ])
0059 <span class="keyword">return</span>
0060 <span class="keyword">end</span>
0061 <span class="keyword">if</span> Info.PlotProperties.CutTop
0062 top(curve) = max(y{curve});
0063 <span class="keyword">else</span> Info.PlotProperties.CutBottom
0064 bottom(curve) = min(y{curve});
0065 <span class="keyword">end</span>
0066 binf(curve) = min(x{curve});
0067 bsup(curve) = max(x{curve});
0068 <span class="keyword">end</span>
0069 ymax = max(top);
0070 ymin = min(bottom);
0071 xmin = min(binf);
0072 xmax = max(bsup);
0073 <span class="keyword">if</span> isnan(ymin(plt))
0074 ymin = 0;
0075 <span class="keyword">end</span>
0076 eval([<span class="string">'NAMES{'</span> int2str(plt) <span class="string">'} = Info.Box'</span> int2str(plt) <span class="string">'.name;'</span>])
0077 <span class="keyword">if</span> options_.TeX
0078 eval([<span class="string">'TeXNAMES{'</span> int2str(plt) <span class="string">'} = Info.Box'</span> int2str(plt) <span class="string">'.texname;'</span>])
0079 <span class="keyword">end</span>
0080 subplot(nr,nc,plt)
0081 hold on
0082 <span class="keyword">for</span> curve = 1:NumberOfCurves
0083 hh = plot(x{curve},y{curve});
0084 <span class="keyword">if</span> strcmpi(PltType{curve},<span class="string">'PriorDensity'</span>)
0085 set(hh,<span class="string">'Color'</span>,[0.7 0.7 0.7],<span class="string">'LineStyle'</span>,<span class="string">'-'</span>,<span class="string">'LineWidth'</span>,2)
0086 <span class="comment">%</span>
0087 <span class="comment">%</span>
0088 <span class="keyword">elseif</span> strcmpi(PltType{curve},<span class="string">'DensityEstimate'</span>)
0089 set(hh,<span class="string">'Color'</span>,<span class="string">'k'</span>,<span class="string">'LineStyle'</span>,<span class="string">'-'</span>,<span class="string">'LineWidth'</span>,2)
0090 <span class="comment">%</span>
0091 <span class="comment">%</span>
0092 <span class="keyword">elseif</span> strcmpi(PltType{curve},<span class="string">'ModeEstimate'</span>)
0093 set(hh,<span class="string">'Color'</span>,<span class="string">'g'</span>,<span class="string">'LineStyle'</span>,<span class="string">'--'</span>,<span class="string">'LineWidth'</span>,2)
0094 <span class="comment">%</span>
0095 <span class="comment">%</span>
0096 <span class="keyword">elseif</span> strcmpi(PltType{curve},<span class="string">'SmoothVariable'</span>)
0097 set(hh,<span class="string">'Color'</span>,<span class="string">'k'</span>,<span class="string">'LineStyle'</span>,<span class="string">'-'</span>,<span class="string">'LineWidth'</span>,2)
0098 <span class="comment">%</span>
0099 <span class="comment">%</span>
0100 <span class="keyword">elseif</span> strcmpi(PltType{curve},<span class="string">'Deciles'</span>)
0101 set(hh,<span class="string">'Color'</span>,<span class="string">'g'</span>,<span class="string">'LineStyle'</span>,<span class="string">'-'</span>,<span class="string">'LineWidth'</span>,1)
0102 <span class="comment">%</span>
0103 <span class="comment">%</span>
0104 <span class="keyword">elseif</span> strcmpi(PltType{curve},<span class="string">'Forecasts'</span>)
0105 set(hh,<span class="string">'Color'</span>,<span class="string">''</span>,<span class="string">'LineStyle'</span>,<span class="string">'-'</span>,<span class="string">'LineWidth'</span>,2)
0106 <span class="comment">%</span>
0107 <span class="comment">%</span>
0108 <span class="keyword">elseif</span> strcmpi(PltType{curve},<span class="string">'ForecastsHPD'</span>)
0109 set(hh,<span class="string">'Color'</span>,<span class="string">'k'</span>,<span class="string">'LineStyle'</span>,<span class="string">'-'</span>,<span class="string">'LineWidth'</span>,1)
0110 <span class="comment">%</span>
0111 <span class="comment">%</span>
0112 <span class="keyword">elseif</span> strcmpi(PltType{curve},<span class="string">'ForecastsDeciles'</span>)
0113 set(hh,<span class="string">'Color'</span>,<span class="string">'g'</span>,<span class="string">'LineStyle'</span>,<span class="string">'-'</span>,<span class="string">'LineWidth'</span>,1)
0114 <span class="comment">%</span>
0115 <span class="comment">%</span>
0116 <span class="keyword">elseif</span> strcmpi(PltType{curve},<span class="string">'DiagnosticWithin'</span>)
0117 set(hh,<span class="string">'Color'</span>,<span class="string">'b'</span>,<span class="string">'LineStyle'</span>,<span class="string">'-'</span>,<span class="string">'LineWidth'</span>,2)
0118 <span class="comment">%</span>
0119 <span class="comment">%</span>
0120 <span class="keyword">elseif</span> strcmpi(PltType{curve},<span class="string">'DiagnosticPooled'</span>)
0121 set(hh,<span class="string">'Color'</span>,<span class="string">'r'</span>,<span class="string">'LineStyle'</span>,<span class="string">'-'</span>,<span class="string">'LineWidth'</span>,2)
0122 <span class="comment">%</span>
0123 <span class="comment">%</span>
0124 <span class="keyword">end</span>
0125 <span class="keyword">end</span>
0126 axis([xmin xmax ymin ymax])
0127 title(NAMES{plt})
0128 drawnow
0129 hold off
0130 <span class="keyword">end</span>
0131
0132 <span class="keyword">if</span> Info.SaveFormat.Eps
0133 <span class="keyword">if</span> isempty(Info.SaveFormat.Name)
0134 eval([<span class="string">'print -depsc2 '</span> M_.fname Info.SaveFormat.GenericName int2str(Info.SaveFormat.Number)]);
0135 <span class="keyword">else</span>
0136 eval([<span class="string">'print -depsc2 '</span> M_.fname Info.SaveFormat.GenericName Info.SaveFormat.Name]);
0137 <span class="keyword">end</span>
0138 <span class="keyword">end</span>
0139 <span class="keyword">if</span> Info.SaveFormat.Pdf
0140 <span class="keyword">if</span> isempty(Info.SaveFormat.Name)
0141 eval([<span class="string">'print -dpdf '</span> M_.fname Info.SaveFormat.GenericName int2str(Info.SaveFormat.Number)]);
0142 <span class="keyword">else</span>
0143 eval([<span class="string">'print -dpdf '</span> M_.fname Info.SaveFormat.GenericName Info.SaveFormat.Name]);
0144 <span class="keyword">end</span>
0145 <span class="keyword">end</span>
0146 <span class="keyword">if</span> Info.SaveFormat.Fig
0147 <span class="keyword">if</span> isempty(Info.SaveFormat.Name)
0148 saveas(FigHandle,[M_.fname Info.SaveFormat.GenericName int2str(Info.SaveFormat.Number) <span class="string">'.fig'</span>]);
0149 <span class="keyword">else</span>
0150 saveas(FigHandle,[M_.fname Info.SaveFormat.GenericName Info.SaveFormat.Name <span class="string">'.fig'</span>]);
0151 <span class="keyword">end</span>
0152 <span class="keyword">end</span></pre></div>
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<title>Description of McMCDiagnostics</title>
<meta name="keywords" content="McMCDiagnostics">
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<!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png">&nbsp;Master index</a></td>
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<h1>McMCDiagnostics
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>stephane.adjemian@ens.fr [06-04-2005]</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function McmcDiagnostic </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> stephane.adjemian@ens.fr [06-04-2005]</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="CheckPath.html" class="code" title="function DirectoryName = CheckPath(type)">CheckPath</a> 06-03-2005</li><li><a href="get_the_name.html" class="code" title="function [nam,texnam] = get_the_name(k,TeX)">get_the_name</a> stephane.adjemian@cepremap.cnrs.fr [07-13-2004]</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="dynare_estimation.html" class="code" title="function dynare_estimation(var_list_)">dynare_estimation</a> </li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function McmcDiagnostic</a>
0002 <span class="comment">% stephane.adjemian@ens.fr [06-04-2005]</span>
0003
0004 <span class="keyword">global</span> options_ estim_params_ M_
0005
0006 DirectoryName = <a href="CheckPath.html" class="code" title="function DirectoryName = CheckPath(type)">CheckPath</a>(<span class="string">'Output'</span>);
0007 MhDirectoryName = <a href="CheckPath.html" class="code" title="function DirectoryName = CheckPath(type)">CheckPath</a>(<span class="string">'metropolis'</span>);
0008
0009 TeX = options_.TeX;
0010 nblck = options_.mh_nblck;
0011 <span class="comment">% Brooks and Gelman tests need more than one block</span>
0012 <span class="keyword">if</span> nblck == 1
0013 <span class="keyword">return</span>;
0014 <span class="keyword">end</span>
0015 npar = estim_params_.nvx;
0016 npar = npar + estim_params_.nvn;
0017 npar = npar + estim_params_.ncx;
0018 npar = npar + estim_params_.ncn;
0019 npar = npar + estim_params_.np ;
0020 MAX_nruns = ceil(options_.MaxNumberOfBytes/(npar+2)/8);
0021
0022 load([MhDirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_mh_history.mat'</span>])
0023
0024 mcfiles = [];
0025 <span class="keyword">for</span> blck = 1:nblck
0026 mcfiles = cat(3,mcfiles,dir([MhDirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_mh*_blck'</span> int2str(blck) <span class="string">'.mat'</span>]));
0027 <span class="keyword">end</span>
0028 NumberOfMcFilesPerBlock = size(mcfiles,1);
0029
0030
0031 PastDraws = sum(record.MhDraws,1);
0032 LastFileNumber = PastDraws(2);
0033 LastLineNumber = record.MhDraws(<span class="keyword">end</span>,3);
0034 NumberOfDraws = PastDraws(1);
0035
0036 Origin = 1000;
0037 StepSize = ceil((NumberOfDraws-Origin)/100);<span class="comment">% So that the computational time does not</span>
0038 ALPHA = 0.2; <span class="comment">% increase too much with the number of simulations.</span>
0039 time = 1:NumberOfDraws;
0040 xx = Origin:StepSize:NumberOfDraws;
0041 NumberOfLines = length(xx);
0042 tmp = zeros(NumberOfDraws*nblck,3);
0043 UDIAG = zeros(NumberOfLines,6,npar);
0044
0045 <span class="keyword">if</span> NumberOfDraws &lt; Origin
0046 disp(<span class="string">'MCMC Diagnostics :: The number of simulations is to small to compute the MCMC convergence diagnostics.'</span>)
0047 <span class="keyword">return</span>
0048 <span class="keyword">end</span>
0049
0050 <span class="keyword">if</span> TeX
0051 fidTeX = fopen([DirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_UnivariateDiagnostics.TeX'</span>],<span class="string">'w'</span>);
0052 fprintf(fidTeX,<span class="string">'%% TeX eps-loader file generated by McmcDiagnostics.m (Dynare).\n'</span>);
0053 fprintf(fidTeX,[<span class="string">'%% '</span> datestr(now,0) <span class="string">'\n'</span>]);
0054 fprintf(fidTeX,<span class="string">' \n'</span>);
0055 <span class="keyword">end</span>
0056
0057
0058 disp(<span class="string">'MCMC Diagnostics: Univariate convergence diagnostic, Brooks and Gelman (1998):'</span>)
0059 <span class="keyword">for</span> j=1:npar
0060 fprintf(<span class="string">' Parameter %d... '</span>,j);
0061 <span class="keyword">for</span> b = 1:nblck
0062 startline = 0;
0063 <span class="keyword">for</span> n = 1:NumberOfMcFilesPerBlock-1
0064 <span class="comment">%load([MhDirectoryName '/' mcfiles(n,1,b).name],'x2');</span>
0065 load([MhDirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_mh'</span>,int2str(n),<span class="string">'_blck'</span> int2str(b) <span class="string">'.mat'</span>],<span class="string">'x2'</span>);
0066 tmp((b-1)*NumberOfDraws+startline+1:(b-1)*NumberOfDraws+MAX_nruns*n,1) = x2(:,j);
0067 clear x2;
0068 startline = startline + MAX_nruns;
0069 <span class="keyword">end</span>
0070 <span class="comment">%load([MhDirectoryName '/' mcfiles(NumberOfMcFilesPerBlock,1,b).name],'x2');</span>
0071 load([MhDirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_mh'</span>,int2str(NumberOfMcFilesPerBlock),<span class="string">'_blck'</span> int2str(b) <span class="string">'.mat'</span>],<span class="string">'x2'</span>);
0072 tmp((b-1)*NumberOfDraws+startline+1:(b-1)*NumberOfDraws+MAX_nruns*(LastFileNumber-1)+LastLineNumber,1) = x2(:,j);
0073 clear x2;
0074 startline = startline + LastLineNumber;
0075 <span class="keyword">end</span>
0076 tmp(:,2) = kron(transpose(1:nblck),ones(NumberOfDraws,1));
0077 tmp(:,3) = kron(ones(nblck,1),time');
0078 tmp = sortrows(tmp,1);
0079 ligne = 0;
0080 <span class="keyword">for</span> iter = Origin:StepSize:NumberOfDraws
0081 ligne = ligne+1;
0082 linea = ceil(0.5*iter);
0083 n = iter-linea+1;
0084 cinf = round(n*ALPHA/2);
0085 csup = round(n*(1-ALPHA/2));
0086 CINF = round(nblck*n*ALPHA/2);
0087 CSUP = round(nblck*n*(1-ALPHA/2));
0088 temp = tmp(find((tmp(:,3)&gt;=linea) &amp; (tmp(:,3)&lt;=iter)),1:2);
0089 UDIAG(ligne,1,j) = temp(CSUP,1)-temp(CINF,1);
0090 moyenne = mean(temp(:,1));<span class="comment">%% Pooled mean.</span>
0091 UDIAG(ligne,3,j) = sum((temp(:,1)-moyenne).^2)/(nblck*n-1);
0092 UDIAG(ligne,5,j) = sum(abs(temp(:,1)-moyenne).^3)/(nblck*n-1);
0093 <span class="keyword">for</span> i=1:nblck
0094 pmet = temp(find(temp(:,2)==i));
0095 UDIAG(ligne,2,j) = UDIAG(ligne,2,j) + pmet(csup,1)-pmet(cinf,1);
0096 moyenne = mean(pmet,1); <span class="comment">%% Within mean.</span>
0097 UDIAG(ligne,4,j) = UDIAG(ligne,4,j) + sum((pmet(:,1)-moyenne).^2)/(n-1);
0098 UDIAG(ligne,6,j) = UDIAG(ligne,6,j) + sum(abs(pmet(:,1)-moyenne).^3)/(n-1);
0099 <span class="keyword">end</span>
0100 <span class="keyword">end</span>
0101 fprintf(<span class="string">'Done! \n'</span>);
0102 <span class="keyword">end</span>
0103 UDIAG(:,[2 4 6],:) = UDIAG(:,[2 4 6],:)/nblck;
0104 disp(<span class="string">' '</span>)
0105 clear pmet temp moyenne CSUP CINF csup cinf n linea iter tmp;
0106 pages = floor(npar/3);
0107 k = 0;
0108 <span class="keyword">for</span> i = 1:pages
0109 <span class="keyword">if</span> options_.nograph
0110 h = figure(<span class="string">'Name'</span>,<span class="string">'MCMC univariate diagnostic (Brooks and Gelman,1998)'</span>,<span class="string">'Visible'</span>,<span class="string">'off'</span>);
0111 <span class="keyword">else</span>
0112 h = figure(<span class="string">'Name'</span>,<span class="string">'MCMC univariate diagnostic (Brooks and Gelman,1998)'</span>);
0113 <span class="keyword">end</span>
0114 boxplot = 1;
0115 <span class="keyword">if</span> TeX
0116 NAMES = [];
0117 TEXNAMES = [];
0118 <span class="keyword">end</span>
0119 <span class="keyword">for</span> j = 1:3 <span class="comment">% Loop over parameters</span>
0120 k = k+1;
0121 [nam,namtex] = <a href="get_the_name.html" class="code" title="function [nam,texnam] = get_the_name(k,TeX)">get_the_name</a>(k,TeX);
0122 <span class="keyword">for</span> crit = 1:3<span class="comment">% Loop over criteria</span>
0123 <span class="keyword">if</span> crit == 1
0124 plt1 = UDIAG(:,1,k);
0125 plt2 = UDIAG(:,2,k);
0126 namnam = [nam , <span class="string">' (Interval)'</span>];
0127 <span class="keyword">elseif</span> crit == 2
0128 plt1 = UDIAG(:,3,k);
0129 plt2 = UDIAG(:,4,k);
0130 namnam = [nam , <span class="string">' (m2)'</span>];
0131 <span class="keyword">elseif</span> crit == 3
0132 plt1 = UDIAG(:,5,k);
0133 plt2 = UDIAG(:,6,k);
0134 namnam = [nam , <span class="string">' (m3)'</span>];
0135 <span class="keyword">end</span>
0136 <span class="keyword">if</span> TeX
0137 NAMES = strvcat(NAMES,deblank(namnam));
0138 TEXNAMES = strvcat(TEXNAMES,deblank(namtex));
0139 <span class="keyword">end</span>
0140 subplot(3,3,boxplot);
0141 plot(xx,plt1,<span class="string">'-b'</span>); <span class="comment">% Pooled</span>
0142 hold on;
0143 plot(xx,plt2,<span class="string">'-r'</span>); <span class="comment">% Within (mean)</span>
0144 hold off;
0145 xlim([xx(1) xx(NumberOfLines)])
0146 title(namnam,<span class="string">'Interpreter'</span>,<span class="string">'none'</span>)
0147 boxplot = boxplot + 1;
0148 <span class="keyword">end</span>
0149 <span class="keyword">end</span>
0150 eval([<span class="string">'print -depsc2 '</span> DirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_udiag'</span> int2str(i)]);
0151 eval([<span class="string">'print -dpdf '</span> DirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_udiag'</span> int2str(i)]);
0152 <span class="keyword">if</span> options_.nograph, set(h,<span class="string">'visible'</span>,<span class="string">'on'</span>), <span class="keyword">end</span>
0153 saveas(h,[DirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_udiag'</span> int2str(i) <span class="string">'.fig'</span>]);
0154 <span class="keyword">if</span> options_.nograph, close(h), <span class="keyword">end</span>
0155 <span class="keyword">if</span> TeX
0156 fprintf(fidTeX,<span class="string">'\\begin{figure}[H]\n'</span>);
0157 <span class="keyword">for</span> jj = 1:size(NAMES,1)
0158 fprintf(fidTeX,<span class="string">'\\psfrag{%s}[1][][0.5][0]{%s}\n'</span>,deblank(NAMES(jj,:)),deblank(TEXNAMES(jj,:)));
0159 <span class="keyword">end</span>
0160 fprintf(fidTeX,<span class="string">'\\centering \n'</span>);
0161 fprintf(fidTeX,<span class="string">'\\includegraphics[scale=0.5]{%s_udiag%s}\n'</span>,M_.fname,int2str(i));
0162 fprintf(fidTeX,<span class="string">'\\caption{Univariate convergence diagnostics for the Metropolis-Hastings.\n'</span>);
0163 fprintf(fidTeX,<span class="string">'The first, second and third columns are respectively the criteria based on\n'</span>);
0164 fprintf(fidTeX,<span class="string">'the eighty percent interval, the second and third moments.}'</span>);
0165 fprintf(fidTeX,<span class="string">'\\label{Fig:UnivariateDiagnostics:%s}\n'</span>,int2str(i));
0166 fprintf(fidTeX,<span class="string">'\\end{figure}\n'</span>);
0167 fprintf(fidTeX,<span class="string">'\n'</span>);
0168 <span class="keyword">end</span>
0169 <span class="keyword">end</span>
0170 reste = npar-k;
0171 <span class="keyword">if</span> reste
0172 <span class="keyword">if</span> reste == 1
0173 nr = 3;
0174 nc = 1;
0175 <span class="keyword">elseif</span> reste == 2;
0176 nr = 2;
0177 nc = 3;
0178 <span class="keyword">end</span>
0179 <span class="keyword">if</span> TeX
0180 NAMES = [];
0181 TEXNAMES = [];
0182 <span class="keyword">end</span>
0183 <span class="keyword">if</span> options_.nograph
0184 h = figure(<span class="string">'Name'</span>,<span class="string">'MCMC univariate diagnostic (Brooks and Gelman, 1998)'</span>,<span class="string">'Visible'</span>,<span class="string">'off'</span>);
0185 <span class="keyword">else</span>
0186 h = figure(<span class="string">'Name'</span>,<span class="string">'MCMC univariate diagnostic (Brooks and Gelman, 1998)'</span>);
0187 <span class="keyword">end</span>
0188 boxplot = 1;
0189 <span class="keyword">for</span> j = 1:reste
0190 k = k+1;
0191 [nam,namtex] = <a href="get_the_name.html" class="code" title="function [nam,texnam] = get_the_name(k,TeX)">get_the_name</a>(k,TeX);
0192 <span class="keyword">for</span> crit = 1:3
0193 <span class="keyword">if</span> crit == 1
0194 plt1 = UDIAG(:,1,k);
0195 plt2 = UDIAG(:,2,k);
0196 namnam = [nam , <span class="string">' (Interval)'</span>];
0197 <span class="keyword">elseif</span> crit == 2
0198 plt1 = UDIAG(:,3,k);
0199 plt2 = UDIAG(:,4,k);
0200 namnam = [nam , <span class="string">' (m2)'</span>];
0201 <span class="keyword">elseif</span> crit == 3
0202 plt1 = UDIAG(:,5,k);
0203 plt2 = UDIAG(:,6,k);
0204 namnam = [nam , <span class="string">' (m3)'</span>];
0205 <span class="keyword">end</span>
0206 <span class="keyword">if</span> TeX
0207 NAMES = strvcat(NAMES,deblank(namnam));
0208 TEXNAMES = strvcat(TEXNAMES,deblank(namtex));
0209 <span class="keyword">end</span>
0210 subplot(nr,nc,boxplot);
0211 plot(xx,plt1,<span class="string">'-b'</span>); <span class="comment">% Pooled</span>
0212 hold on;
0213 plot(xx,plt2,<span class="string">'-r'</span>); <span class="comment">% Within (mean)</span>
0214 hold off;
0215 xlim([xx(1) xx(NumberOfLines)]);
0216 title(namnam,<span class="string">'Interpreter'</span>,<span class="string">'none'</span>);
0217 boxplot = boxplot + 1;
0218 <span class="keyword">end</span>
0219 <span class="keyword">end</span>
0220 eval([<span class="string">'print -depsc2 '</span> DirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_udiag'</span> int2str(pages+1)]);
0221 eval([<span class="string">'print -dpdf '</span> DirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_udiag'</span> int2str(pages+1)]);
0222 <span class="keyword">if</span> options_.nograph, set(h,<span class="string">'visible'</span>,<span class="string">'on'</span>), <span class="keyword">end</span>
0223 saveas(h,[DirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_udiag'</span> int2str(pages+1) <span class="string">'.fig'</span>]);
0224 <span class="keyword">if</span> options_.nograph, close(h), <span class="keyword">end</span>
0225 <span class="keyword">if</span> TeX
0226 fprintf(fidTeX,<span class="string">'\\begin{figure}[H]\n'</span>);
0227 <span class="keyword">for</span> jj = 1:size(NAMES,1);
0228 fprintf(fidTeX,<span class="string">'\\psfrag{%s}[1][][0.5][0]{%s}\n'</span>,deblank(NAMES(jj,:)),deblank(TEXNAMES(jj,:)));
0229 <span class="keyword">end</span>
0230 fprintf(fidTeX,<span class="string">'\\centering \n'</span>);
0231 fprintf(fidTeX,<span class="string">'\\includegraphics[scale=0.5]{%s_udiag%s}\n'</span>,M_.fname,int2str(pages+1));
0232 <span class="keyword">if</span> reste == 2
0233 fprintf(fidTeX,<span class="string">'\\caption{Univariate convergence diagnostics for the Metropolis-Hastings.\n'</span>);
0234 fprintf(fidTeX,<span class="string">'The first, second and third columns are respectively the criteria based on\n'</span>);
0235 fprintf(fidTeX,<span class="string">'the eighty percent interval, the second and third moments.}'</span>);
0236 <span class="keyword">elseif</span> reste == 1
0237 fprintf(fidTeX,<span class="string">'\\caption{Univariate convergence diagnostics for the Metropolis-Hastings.\n'</span>);
0238 fprintf(fidTeX,<span class="string">'The first, second and third rows are respectively the criteria based on\n'</span>);
0239 fprintf(fidTeX,<span class="string">'the eighty percent interval, the second and third moments.}'</span>);
0240 <span class="keyword">end</span>
0241 fprintf(fidTeX,<span class="string">'\\label{Fig:UnivariateDiagnostics:%s}\n'</span>,int2str(pages+1));
0242 fprintf(fidTeX,<span class="string">'\\end{figure}\n'</span>);
0243 fprintf(fidTeX,<span class="string">'\n'</span>);
0244 fprintf(fidTeX,<span class="string">'% End Of TeX file.'</span>);
0245 fclose(fidTeX);
0246 <span class="keyword">end</span>
0247 <span class="keyword">end</span> <span class="comment">% if reste &gt; 0</span>
0248 clear UDIAG;
0249 <span class="comment">%%</span>
0250 <span class="comment">%% Multivariate diagnostic.</span>
0251 <span class="comment">%%</span>
0252 <span class="keyword">if</span> TeX
0253 fidTeX = fopen([DirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_MultivariateDiagnostics.TeX'</span>],<span class="string">'w'</span>);
0254 fprintf(fidTeX,<span class="string">'%% TeX eps-loader file generated by McmcDiagnostics.m (Dynare).\n'</span>);
0255 fprintf(fidTeX,[<span class="string">'%% '</span> datestr(now,0) <span class="string">'\n'</span>]);
0256 fprintf(fidTeX,<span class="string">' \n'</span>);
0257 NAMES = [];
0258 <span class="keyword">end</span>
0259 tmp = zeros(NumberOfDraws*nblck,3);
0260 MDIAG = zeros(NumberOfLines,6);
0261 <span class="keyword">for</span> b = 1:nblck
0262 startline = 0;
0263 <span class="keyword">for</span> n = 1:NumberOfMcFilesPerBlock-1
0264 <span class="comment">%load([MhDirectoryName '/' mcfiles(n,1,b).name],'logpo2');</span>
0265 load([MhDirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_mh'</span>,int2str(n),<span class="string">'_blck'</span> int2str(b) <span class="string">'.mat'</span>],<span class="string">'logpo2'</span>);
0266 tmp((b-1)*NumberOfDraws+startline+1:(b-1)*NumberOfDraws+MAX_nruns*n,1) = logpo2;
0267 startline = startline+MAX_nruns;
0268 <span class="keyword">end</span>
0269 <span class="comment">%load([MhDirectoryName '/' mcfiles(NumberOfMcFilesPerBlock,1,b).name],'logpo2');</span>
0270 load([MhDirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_mh'</span>,int2str(NumberOfMcFilesPerBlock),<span class="string">'_blck'</span> int2str(b) <span class="string">'.mat'</span>],<span class="string">'logpo2'</span>);
0271 tmp((b-1)*NumberOfDraws+startline+1:(b-1)*NumberOfDraws+ MAX_nruns*(LastFileNumber-1)+LastLineNumber,1) = logpo2;
0272 <span class="keyword">end</span>
0273 clear logpo2;
0274 tmp(:,2) = kron(transpose(1:nblck),ones(NumberOfDraws,1));
0275 tmp(:,3) = kron(ones(nblck,1),time');
0276 tmp = sortrows(tmp,1);
0277 ligne = 0;
0278 <span class="keyword">for</span> iter = Origin:StepSize:NumberOfDraws
0279 ligne = ligne+1;
0280 linea = ceil(0.5*iter);
0281 n = iter-linea+1;
0282 cinf = round(n*ALPHA/2);
0283 csup = round(n*(1-ALPHA/2));
0284 CINF = round(nblck*n*ALPHA/2);
0285 CSUP = round(nblck*n*(1-ALPHA/2));
0286 temp = tmp(find((tmp(:,3)&gt;=linea) &amp; (tmp(:,3)&lt;=iter)),1:2);
0287 MDIAG(ligne,1) = temp(CSUP,1)-temp(CINF,1);
0288 moyenne = mean(temp(:,1));<span class="comment">%% Pooled mean.</span>
0289 MDIAG(ligne,3) = sum((temp(:,1)-moyenne).^2)/(nblck*n-1);
0290 MDIAG(ligne,5) = sum(abs(temp(:,1)-moyenne).^3)/(nblck*n-1);
0291 <span class="keyword">for</span> i=1:nblck
0292 pmet = temp(find(temp(:,2)==i));
0293 MDIAG(ligne,2) = MDIAG(ligne,2) + pmet(csup,1)-pmet(cinf,1);
0294 moyenne = mean(pmet,1); <span class="comment">%% Within mean.</span>
0295 MDIAG(ligne,4) = MDIAG(ligne,4) + sum((pmet(:,1)-moyenne).^2)/(n-1);
0296 MDIAG(ligne,6) = MDIAG(ligne,6) + sum(abs(pmet(:,1)-moyenne).^3)/(n-1);
0297 <span class="keyword">end</span>
0298 <span class="keyword">end</span>
0299 MDIAG(:,[2 4 6],:) = MDIAG(:,[2 4 6],:)/nblck;
0300 <span class="keyword">if</span> options_.nograph
0301 h = figure(<span class="string">'Name'</span>,<span class="string">'Multivatiate diagnostic'</span>,<span class="string">'Visible'</span>,<span class="string">'off'</span>);
0302 <span class="keyword">else</span>
0303 h = figure(<span class="string">'Name'</span>,<span class="string">'Multivatiate diagnostic'</span>);
0304 <span class="keyword">end</span>
0305 boxplot = 1;
0306 <span class="keyword">for</span> crit = 1:3
0307 <span class="keyword">if</span> crit == 1
0308 plt1 = MDIAG(:,1);
0309 plt2 = MDIAG(:,2);
0310 namnam = <span class="string">'Interval'</span>;
0311 <span class="keyword">elseif</span> crit == 2
0312 plt1 = MDIAG(:,3);
0313 plt2 = MDIAG(:,4);
0314 namnam = <span class="string">'m2'</span>;
0315 <span class="keyword">elseif</span> crit == 3
0316 plt1 = MDIAG(:,5);
0317 plt2 = MDIAG(:,6);
0318 namnam = <span class="string">'m3'</span>;
0319 <span class="keyword">end</span>
0320 <span class="keyword">if</span> TeX
0321 NAMES = strvcat(NAMES,namnam);
0322 <span class="keyword">end</span>
0323 subplot(3,1,boxplot);
0324 plot(xx,plt1,<span class="string">'-b'</span>); <span class="comment">% Pooled</span>
0325 hold on
0326 plot(xx,plt2,<span class="string">'-r'</span>); <span class="comment">% Within (mean)</span>
0327 hold off
0328 xlim([xx(1) xx(NumberOfLines)])
0329 title(namnam,<span class="string">'Interpreter'</span>,<span class="string">'none'</span>);
0330 boxplot = boxplot + 1;
0331 <span class="keyword">end</span>
0332 eval([<span class="string">'print -depsc2 '</span> DirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_mdiag'</span>]);
0333 eval([<span class="string">'print -dpdf '</span> DirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_mdiag'</span>]);
0334 <span class="keyword">if</span> options_.nograph, set(h,<span class="string">'visible'</span>,<span class="string">'on'</span>), <span class="keyword">end</span>
0335 saveas(h,[DirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_mdiag.fig'</span>]);
0336 <span class="keyword">if</span> options_.nograph, close(h), <span class="keyword">end</span>
0337 <span class="keyword">if</span> TeX
0338 fprintf(fidTeX,<span class="string">'\\begin{figure}[H]\n'</span>);
0339 <span class="keyword">for</span> jj = 1:3
0340 fprintf(fidTeX,<span class="string">'\\psfrag{%s}[1][][0.5][0]{%s}\n'</span>,deblank(NAMES(jj,:)),<span class="string">' '</span>);
0341 <span class="keyword">end</span>
0342 fprintf(fidTeX,<span class="string">'\\centering \n'</span>);
0343 fprintf(fidTeX,<span class="string">'\\includegraphics[scale=0.5]{%s_mdiag}\n'</span>,M_.fname);
0344 fprintf(fidTeX,<span class="string">'\\caption{Multivariate convergence diagnostics for the Metropolis-Hastings.\n'</span>);
0345 fprintf(fidTeX,<span class="string">'The first, second and third rows are respectively the criteria based on\n'</span>);
0346 fprintf(fidTeX,<span class="string">'the eighty percent interval, the second and third moments. The different \n'</span>);
0347 fprintf(fidTeX,<span class="string">'parameters are aggregated using the posterior kernel.}'</span>);
0348 fprintf(fidTeX,<span class="string">'\\label{Fig:MultivariateDiagnostics}\n'</span>);
0349 fprintf(fidTeX,<span class="string">'\\end{figure}\n'</span>);
0350 fprintf(fidTeX,<span class="string">'\n'</span>);
0351 fprintf(fidTeX,<span class="string">'% End Of TeX file.'</span>);
0352 fclose(fidTeX);
0353 <span class="keyword">end</span></pre></div>
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<div><a href="../index.html">Home</a> &gt; <a href="index.html">.</a> &gt; PlotPosteriorDistributions.m</div>
<!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png">&nbsp;Master index</a></td>
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<h1>PlotPosteriorDistributions
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>stephane.adjemian@ens.fr [09-09-2005]</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function PlotPosteriorDistributions() </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> stephane.adjemian@ens.fr [09-09-2005]</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="CheckPath.html" class="code" title="function DirectoryName = CheckPath(type)">CheckPath</a> 06-03-2005</li><li><a href="draw_prior_density.html" class="code" title="function [x,f,abscissa,dens,binf,bsup] = draw_prior_density(indx);">draw_prior_density</a> stephane.adjemian@ens.fr [07-15-2004]</li><li><a href="get_the_name.html" class="code" title="function [nam,texnam] = get_the_name(k,TeX)">get_the_name</a> stephane.adjemian@cepremap.cnrs.fr [07-13-2004]</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="dynare_estimation.html" class="code" title="function dynare_estimation(var_list_)">dynare_estimation</a> </li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function PlotPosteriorDistributions()</a>
0002 <span class="comment">% stephane.adjemian@ens.fr [09-09-2005]</span>
0003 <span class="keyword">global</span> estim_params_ M_ options_ bayestopt_ oo_
0004
0005 OutputDirectoryName = <a href="CheckPath.html" class="code" title="function DirectoryName = CheckPath(type)">CheckPath</a>(<span class="string">'Output'</span>);
0006
0007 TeX = options_.TeX;
0008 nblck = options_.mh_nblck;
0009 nvx = estim_params_.nvx;
0010 nvn = estim_params_.nvn;
0011 ncx = estim_params_.ncx;
0012 ncn = estim_params_.ncn;
0013 np = estim_params_.np ;
0014 npar = nvx+nvn+ncx+ncn+np;
0015
0016 MaxNumberOfPlotPerFigure = 9;<span class="comment">% The square root must be an integer!</span>
0017 nn = sqrt(MaxNumberOfPlotPerFigure);
0018
0019 figurename = <span class="string">'Priors and posteriors'</span>;
0020
0021 <span class="keyword">if</span> TeX
0022 fidTeX = fopen([OutputDirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_PriorsAndPosteriors.TeX'</span>],<span class="string">'w'</span>);
0023 fprintf(fidTeX,<span class="string">'%% TeX eps-loader file generated by PlotPosteriorDistributions.m (Dynare).\n'</span>);
0024 fprintf(fidTeX,[<span class="string">'%% '</span> datestr(now,0) <span class="string">'\n'</span>]);
0025 fprintf(fidTeX,<span class="string">' \n'</span>);
0026 <span class="keyword">end</span>
0027
0028 figunumber = 0;
0029 subplotnum = 0;
0030
0031 <span class="keyword">for</span> i=1:npar
0032 subplotnum = subplotnum+1;
0033 <span class="keyword">if</span> subplotnum == 1
0034 figunumber = figunumber+1;
0035 <span class="keyword">if</span> options_.nograph
0036 hfig = figure(<span class="string">'Name'</span>,figurename,<span class="string">'Visible'</span>,<span class="string">'off'</span>);
0037 <span class="keyword">else</span>
0038 hfig = figure(<span class="string">'Name'</span>,figurename);
0039 <span class="keyword">end</span>
0040 <span class="keyword">end</span>
0041 <span class="keyword">if</span> subplotnum == 1
0042 <span class="keyword">if</span> TeX
0043 TeXNAMES = [];
0044 <span class="keyword">end</span>
0045 NAMES = [];
0046 <span class="keyword">end</span>
0047 [nam,texnam] = <a href="get_the_name.html" class="code" title="function [nam,texnam] = get_the_name(k,TeX)">get_the_name</a>(i,TeX);
0048 NAMES = strvcat(NAMES,nam);
0049 <span class="keyword">if</span> TeX
0050 TeXNAMES = strvcat(TeXNAMES,texnam);
0051 <span class="keyword">end</span>
0052 [x2,f2,abscissa,dens,binf2,bsup2] = <a href="draw_prior_density.html" class="code" title="function [x,f,abscissa,dens,binf,bsup] = draw_prior_density(indx);">draw_prior_density</a>(i);
0053 top2 = max(f2);
0054 <span class="keyword">if</span> i &lt;= nvx
0055 name = deblank(M_.exo_names(estim_params_.var_exo(i,1),:));
0056 eval([<span class="string">'x1 = oo_.posterior_density.shocks_std.'</span> name <span class="string">'(:,1);'</span>])
0057 eval([<span class="string">'f1 = oo_.posterior_density.shocks_std.'</span> name <span class="string">'(:,2);'</span>])
0058 eval([<span class="string">'pmode = oo_.posterior_mode.shocks_std.'</span> name <span class="string">';'</span>])
0059 <span class="keyword">elseif</span> i &lt;= nvx+nvn
0060 name = deblank(options_.varobs(estim_params_.var_endo(i-nvx,1),:));
0061 eval([<span class="string">'x1 = oo_.posterior_density.measurement_errors_std.'</span> name <span class="string">'(:,1);'</span>])
0062 eval([<span class="string">'f1 = oo_.posterior_density.measurement_errors_std.'</span> name <span class="string">'(:,2);'</span>])
0063 eval([<span class="string">'pmode = oo_.posterior_mode.measurement_errors_std.'</span> name <span class="string">';'</span>])
0064 <span class="keyword">elseif</span> i &lt;= nvx+nvn+ncx
0065 j = i - (nvx+nvn)
0066 k1 = estim_params_.corrx(j,1);
0067 k2 = estim_params_.corrx(j,2);
0068 name = [deblank(M_.exo_names(k1,:)) <span class="string">'_'</span> deblank(M_.exo_names(k2,:))];
0069 eval([<span class="string">'x1 = oo_.posterior_density.shocks_corr.'</span> name <span class="string">'(:,1);'</span>])
0070 eval([<span class="string">'f1 = oo_.posterior_density.shocks_corr.'</span> name <span class="string">'(:,2);'</span>])
0071 eval([<span class="string">'pmode = oo_.posterior_mode.shocks_corr.'</span> name <span class="string">';'</span>])
0072 <span class="keyword">elseif</span> i &lt;= nvx+nvn+ncx+ncn
0073 j = i - (nvx+nvn+ncx);
0074 k1 = estim_params_.corrn(j,1);
0075 k2 = estim_params_.corrn(j,2);
0076 name = [deblank(M_.endo_names(k1,:)) <span class="string">'_'</span> deblank(M_.endo_names(k2,:))];
0077 eval([<span class="string">'x1 = oo_.posterior_density.measurement_errors_corr.'</span> name <span class="string">'(:,1);'</span>])
0078 eval([<span class="string">'f1 = oo_.posterior_density.measurement_errors_corr.'</span> name <span class="string">'(:,2);'</span>])
0079 eval([<span class="string">'pmode = oo_.posterior_mode.measurement_errors_corr.'</span> name <span class="string">';'</span>])
0080 <span class="keyword">else</span>
0081 j = i - (nvx+nvn+ncx+ncn);
0082 name = deblank(M_.param_names(estim_params_.param_vals(j,1),:));
0083 eval([<span class="string">'x1 = oo_.posterior_density.'</span> name <span class="string">'(:,1);'</span>])
0084 eval([<span class="string">'f1 = oo_.posterior_density.'</span> name <span class="string">'(:,2);'</span>])
0085 eval([<span class="string">'pmode = oo_.posterior_mode.parameters.'</span> name <span class="string">';'</span>])
0086 <span class="keyword">end</span>
0087 top1 = max(f1);
0088 top0 = max([top1;top2]);
0089 binf1 = x1(1);
0090 bsup1 = x1(end);
0091 borneinf = min(binf1,binf2);
0092 bornesup = max(bsup1,bsup2);
0093 subplot(nn,nn,subplotnum)
0094 hh = plot(x2,f2,<span class="string">'-k'</span>,<span class="string">'linewidth'</span>,2);
0095 set(hh,<span class="string">'color'</span>,[0.7 0.7 0.7]);
0096 hold on;
0097 plot(x1,f1,<span class="string">'-k'</span>,<span class="string">'linewidth'</span>,2);
0098 plot( [pmode pmode], [0.0 1.1*top0], <span class="string">'--g'</span>, <span class="string">'linewidth'</span>, 2);
0099 box on;
0100 axis([borneinf bornesup 0 1.1*top0]);
0101 title(nam,<span class="string">'Interpreter'</span>,<span class="string">'none'</span>);
0102 hold off;
0103 drawnow
0104 <span class="keyword">if</span> subplotnum == MaxNumberOfPlotPerFigure | i == npar;
0105 eval([<span class="string">'print -depsc2 '</span> OutputDirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_PriorsAndPosteriors'</span> int2str(figunumber)]);
0106 eval([<span class="string">'print -dpdf '</span> OutputDirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_PriorsAndPosteriors'</span> int2str(figunumber)]);
0107 <span class="keyword">if</span> options_.nograph,
0108 set(hfig,<span class="string">'Visible'</span>,<span class="string">'on'</span>);
0109 <span class="keyword">end</span>
0110 saveas(hfig,[OutputDirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_PriorsAndPosteriors'</span> int2str(figunumber) <span class="string">'.fig'</span>]);
0111 <span class="keyword">if</span> TeX
0112 fprintf(fidTeX,<span class="string">'\\begin{figure}[H]\n'</span>);
0113 <span class="keyword">for</span> j = 1:size(NAMES,1)
0114 fprintf(fidTeX,<span class="string">'\\psfrag{%s}[1][][0.5][0]{%s}\n'</span>,deblank(NAMES(j,:)),deblank(TeXNAMES(j,:)));
0115 <span class="keyword">end</span>
0116 fprintf(fidTeX,<span class="string">'\\centering\n'</span>);
0117 fprintf(fidTeX,<span class="string">'\\includegraphics[scale=0.5]{%s_PriorsAndPosteriors%s}\n'</span>,M_.fname,int2str(figunumber));
0118 fprintf(fidTeX,<span class="string">'\\caption{Priors and posteriors.}'</span>);
0119 fprintf(fidTeX,<span class="string">'\\label{Fig:PriorsAndPosteriors:%s}\n'</span>,int2str(figunumber));
0120 fprintf(fidTeX,<span class="string">'\\end{figure}\n'</span>);
0121 fprintf(fidTeX,<span class="string">' \n'</span>);
0122 <span class="keyword">if</span> i == npar
0123 fprintf(fidTeX,<span class="string">'%% End of TeX file.\n'</span>);
0124 fclose(fidTeX);
0125 <span class="keyword">end</span>
0126 <span class="keyword">end</span>
0127 <span class="keyword">if</span> options_.nograph,
0128 close(hfig),
0129 <span class="keyword">end</span>
0130 subplotnum = 0;
0131 <span class="keyword">end</span>
0132 <span class="keyword">end</span></pre></div>
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<div><a href="../index.html">Home</a> &gt; <a href="index.html">.</a> &gt; PosteriorIRF.m</div>
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<h1>PosteriorIRF
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>stephane.adjemian@ens.fr [09-25-2005]</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function PosteriorIRF(type) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> stephane.adjemian@ens.fr [09-25-2005]</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="CheckPath.html" class="code" title="function DirectoryName = CheckPath(type)">CheckPath</a> 06-03-2005</li><li><a href="GetOneDraw.html" class="code" title="function [xparams, logpost] = GetOneDraw(type)">GetOneDraw</a> stephane.adjemian@ens.fr [09-25-2005]</li><li><a href="ReshapeMatFiles.html" class="code" title="function ReshapeMatFiles(type)">ReshapeMatFiles</a> Reshape and sort (along the mcmc simulations) the mat files generated</li><li><a href="irf.html" class="code" title="function y = irf(dr, e1, long, drop, replic, iorder)">irf</a> </li><li><a href="posterior_moments.html" class="code" title="function [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(xx,info)">posterior_moments</a> stephane.adjemian@ens.fr [09-09-2005]</li><li><a href="resol.html" class="code" title="function [dr,info]=resol(ys,check_flag)">resol</a> Copyright (C) 2001 Michel Juillard</li><li><a href="set_parameters.html" class="code" title="function set_parameters(xparam1)">set_parameters</a> </li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="dynare_estimation.html" class="code" title="function dynare_estimation(var_list_)">dynare_estimation</a> </li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function PosteriorIRF(type)</a>
0002 <span class="comment">% stephane.adjemian@ens.fr [09-25-2005]</span>
0003 <span class="keyword">global</span> options_ estim_params_ oo_ M_
0004 nvx = estim_params_.nvx;
0005 nvn = estim_params_.nvn;
0006 ncx = estim_params_.ncx;
0007 ncn = estim_params_.ncn;
0008 np = estim_params_.np ;
0009 npar = nvx+nvn+ncx+ncn+np;
0010 offset = npar-np;
0011 <span class="comment">%%</span>
0012 MaxNumberOfPlotPerFigure = 9;<span class="comment">% The square root must be an integer!</span>
0013 nn = sqrt(MaxNumberOfPlotPerFigure);
0014 DirectoryName = <a href="CheckPath.html" class="code" title="function DirectoryName = CheckPath(type)">CheckPath</a>(<span class="string">'Output'</span>);
0015 <span class="keyword">if</span> strcmpi(type,<span class="string">'posterior'</span>)
0016 MhDirectoryName = <a href="CheckPath.html" class="code" title="function DirectoryName = CheckPath(type)">CheckPath</a>(<span class="string">'metropolis'</span>);
0017 <span class="keyword">else</span>
0018 MhDirectoryName = <a href="CheckPath.html" class="code" title="function DirectoryName = CheckPath(type)">CheckPath</a>(<span class="string">'prior'</span>);
0019 <span class="keyword">end</span>
0020 MAX_nirfs = ceil(options_.MaxNumberOfBytes/(options_.irf*length(oo_.steady_state)*M_.exo_nbr)/8)+50;
0021 MAX_nruns = ceil(options_.MaxNumberOfBytes/(npar+2)/8);
0022
0023 <span class="keyword">if</span> strcmpi(type,<span class="string">'posterior'</span>)
0024 load([ MhDirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_mh_history'</span>])
0025 TotalNumberOfMhDraws = sum(record.MhDraws(:,1));
0026 NumberOfDraws = TotalNumberOfMhDraws-floor(options_.mh_drop*TotalNumberOfMhDraws);
0027 <span class="keyword">else</span><span class="comment">% type = 'prior'</span>
0028 NumberOfDraws = 500;
0029 <span class="keyword">end</span>
0030 B = min([round(.5*NumberOfDraws),500]); options_.B = B;
0031 <span class="keyword">try</span> delete([MhDirectoryName <span class="string">'\'</span> M_.fname <span class="string">'_IRFs*'</span>]);
0032 <span class="keyword">catch</span> disp(<span class="string">'No _IRFs files to be deleted!'</span>)
0033 <span class="keyword">end</span>
0034 irun = 0;
0035 irun2 = 0;
0036 NumberOfIRFfiles = 1;
0037 ifil2 = 1;
0038 <span class="keyword">if</span> strcmpi(type,<span class="string">'posterior'</span>)
0039 h = waitbar(0,<span class="string">'Bayesian (posterior) IRFs...'</span>);
0040 <span class="keyword">else</span>
0041 h = waitbar(0,<span class="string">'Bayesian (prior) IRFs...'</span>);
0042 <span class="keyword">end</span>
0043 <span class="keyword">if</span> B &lt;= MAX_nruns
0044 stock_param = zeros(B, npar);
0045 <span class="keyword">else</span>
0046 stock_param = zeros(MAX_nruns, npar);
0047 <span class="keyword">end</span>
0048 <span class="keyword">if</span> B &gt;= MAX_nirfs
0049 stock_irf = zeros(options_.irf,M_.endo_nbr,M_.exo_nbr,MAX_nirfs);
0050 <span class="keyword">else</span>
0051 stock_irf = zeros(options_.irf,M_.endo_nbr,M_.exo_nbr,B);
0052 <span class="keyword">end</span>
0053 <span class="keyword">for</span> b=1:B
0054 irun = irun+1;
0055 irun2 = irun2+1;
0056 deep = <a href="GetOneDraw.html" class="code" title="function [xparams, logpost] = GetOneDraw(type)">GetOneDraw</a>(type);
0057 stock_param(irun2,:) = deep;
0058 <a href="set_parameters.html" class="code" title="function set_parameters(xparam1)">set_parameters</a>(deep);
0059 dr = <a href="resol.html" class="code" title="function [dr,info]=resol(ys,check_flag)">resol</a>(oo_.steady_state,0);
0060 SS(M_.exo_names_orig_ord,M_.exo_names_orig_ord) = M_.Sigma_e+1e-14*eye(M_.exo_nbr);
0061 SS = transpose(chol(SS));
0062 <span class="keyword">for</span> i = 1:M_.exo_nbr
0063 <span class="keyword">if</span> SS(i,i) &gt; 1e-13
0064 y=<a href="irf.html" class="code" title="function y = irf(dr, e1, long, drop, replic, iorder)">irf</a>(dr,SS(M_.exo_names_orig_ord,i), options_.irf, options_.drop,options_.replic,options_.order);
0065 <span class="keyword">if</span> options_.relative_irf
0066 y = 100*y/cs(i,i);
0067 <span class="keyword">end</span>
0068 <span class="keyword">for</span> j = 1:M_.endo_nbr
0069 <span class="keyword">if</span> max(y(j,:)) - min(y(j,:)) &gt; 1e-10
0070 stock_irf(:,j,i,irun) = transpose(y(j,:));
0071 <span class="keyword">end</span>
0072 <span class="keyword">end</span>
0073 <span class="keyword">end</span>
0074 <span class="keyword">end</span>
0075 <span class="keyword">if</span> irun == MAX_nirfs | irun == B | b == B
0076 <span class="keyword">if</span> b == B
0077 stock_irf = stock_irf(:,:,:,1:irun);
0078 <span class="keyword">end</span>
0079 save([MhDirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_irf'</span> int2str(NumberOfIRFfiles)],<span class="string">'stock_irf'</span>);
0080 NumberOfIRFfiles = NumberOfIRFfiles+1;
0081 irun = 0;
0082 <span class="keyword">end</span>
0083 <span class="keyword">if</span> irun2 &gt; MAX_nruns | b == B
0084 <span class="keyword">if</span> b == B
0085 stock_param = stock_param(1:irun2,:);
0086 <span class="keyword">end</span>
0087 stock = stock_param;
0088 save([MhDirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_param_irf'</span> int2str(ifil2)],<span class="string">'stock'</span>);
0089 ifil2 = ifil2 + 1;
0090 irun2 = 1;
0091 <span class="keyword">end</span>
0092 waitbar(b/B,h);
0093 <span class="keyword">end</span>
0094 NumberOfIRFfiles = NumberOfIRFfiles-1;
0095 ifil2 = ifil2-1;
0096 close(h);
0097
0098 <a href="ReshapeMatFiles.html" class="code" title="function ReshapeMatFiles(type)">ReshapeMatFiles</a>(<span class="string">'irf'</span>)
0099
0100 varlist = options_.varlist;
0101 <span class="keyword">if</span> isempty(varlist)
0102 varlist = M_.endo_names;
0103 SelecVariables = transpose(1:M_.endo_nbr);
0104 nvar = M_.endo_nbr;
0105 <span class="keyword">else</span>
0106 nvar = size(varlist,1);
0107 SelecVariables = [];
0108 <span class="keyword">for</span> i=1:nvar
0109 <span class="keyword">if</span> ~isempty(strmatch(varlist(i,:),M_.endo_names,<span class="string">'exact'</span>))
0110 SelecVariables = [SelecVariables;strmatch(varlist(i,:),M_.endo_names,<span class="string">'exact'</span>)];
0111 <span class="keyword">end</span>
0112 <span class="keyword">end</span>
0113 <span class="keyword">end</span>
0114
0115 MeanIRF = zeros(options_.irf,nvar,M_.exo_nbr);
0116 MedianIRF = zeros(options_.irf,nvar,M_.exo_nbr);
0117 StdIRF = zeros(options_.irf,nvar,M_.exo_nbr);
0118 DistribIRF = zeros(options_.irf,9,nvar,M_.exo_nbr);
0119 HPDIRF = zeros(options_.irf,2,nvar,M_.exo_nbr);
0120
0121 <span class="keyword">if</span> options_.TeX
0122 varlist_TeX = [];
0123 <span class="keyword">for</span> i=1:nvar
0124 varlist_TeX = strvcat(varlist_TeX,M_.endo_names_tex(SelecVariables(i),:));
0125 <span class="keyword">end</span>
0126 <span class="keyword">end</span>
0127
0128 fprintf(<span class="string">'MH: Posterior IRFs...\n'</span>);
0129 tit(M_.exo_names_orig_ord,:) = M_.exo_names;
0130 kdx = 0;
0131 <span class="keyword">for</span> file = 1:NumberOfIRFfiles
0132 load([MhDirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_IRFs'</span> int2str(file)]);
0133 <span class="keyword">for</span> i = 1:M_.exo_nbr
0134 <span class="keyword">for</span> j = 1:nvar
0135 <span class="keyword">for</span> k = 1:size(STOCK_IRF,1)
0136 kk = k+kdx;
0137 [MeanIRF(kk,j,i),MedianIRF(kk,j,i),VarIRF(kk,j,i),HPDIRF(kk,:,j,i),DistribIRF(kk,:,j,i)] = <span class="keyword">...</span>
0138 <a href="posterior_moments.html" class="code" title="function [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(xx,info)">posterior_moments</a>(squeeze(STOCK_IRF(k,SelecVariables(j),i,:)),0);
0139 <span class="keyword">end</span>
0140 <span class="keyword">end</span>
0141 <span class="keyword">end</span>
0142 kdx = kdx + size(STOCK_IRF,1);
0143 <span class="keyword">end</span>
0144 clear STOCK_IRF;
0145
0146 <span class="keyword">for</span> i = 1:M_.exo_nbr
0147 <span class="keyword">for</span> j = 1:nvar
0148 name = [deblank(M_.endo_names(SelecVariables(j),:)) <span class="string">'_'</span> deblank(tit(i,:))];
0149 eval([<span class="string">'oo_.PosteriorIRF.Mean.'</span> name <span class="string">' = MeanIRF(:,j,i);'</span>]);
0150 eval([<span class="string">'oo_.PosteriorIRF.Median.'</span> name <span class="string">' = MedianIRF(:,j,i);'</span>]);
0151 eval([<span class="string">'oo_.PosteriorIRF.Var.'</span> name <span class="string">' = VarIRF(:,j,i);'</span>]);
0152 eval([<span class="string">'oo_.PosteriorIRF.Distribution.'</span> name <span class="string">' = DistribIRF(:,:,j,i);'</span>]);
0153 eval([<span class="string">'oo_.PosteriorIRF.HPDinf.'</span> name <span class="string">' = HPDIRF(:,1,j,i);'</span>]);
0154 eval([<span class="string">'oo_.PosteriorIRF.HPDsup.'</span> name <span class="string">' = HPDIRF(:,2,j,i);'</span>]);
0155 <span class="keyword">end</span>
0156 <span class="keyword">end</span>
0157 <span class="comment">%%</span>
0158 <span class="comment">%% Finally i build the plots.</span>
0159 <span class="comment">%%</span>
0160 <span class="keyword">if</span> options_.TeX
0161 fidTeX = fopen([DirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_BayesianIRF.TeX'</span>],<span class="string">'w'</span>);
0162 fprintf(fidTeX,<span class="string">'%% TeX eps-loader file generated by PosteriorIRF.m (Dynare).\n'</span>);
0163 fprintf(fidTeX,[<span class="string">'%% '</span> datestr(now,0) <span class="string">'\n'</span>]);
0164 fprintf(fidTeX,<span class="string">' \n'</span>);
0165 titTeX(M_.exo_names_orig_ord,:) = M_.exo_names_tex;
0166 <span class="keyword">end</span>
0167 <span class="comment">%%</span>
0168 subplotnum = 0;
0169 <span class="keyword">for</span> i=1:M_.exo_nbr
0170 NAMES = [];
0171 <span class="keyword">if</span> options_.TeX
0172 TEXNAMES = [];
0173 <span class="keyword">end</span>
0174 figunumber = 0;
0175 <span class="keyword">for</span> j=1:nvar
0176 <span class="keyword">if</span> max(abs(MeanIRF(:,j,i))) &gt; 10^(-6)
0177 subplotnum = subplotnum+1;
0178 <span class="keyword">if</span> options_.nograph
0179 <span class="keyword">if</span> subplotnum == 1 &amp; options_.relative_irf
0180 hh = figure(<span class="string">'Name'</span>,[<span class="string">'Relative response to orthogonalized shock to '</span> tit(i,:)],<span class="string">'Visible'</span>,<span class="string">'off'</span>);
0181 <span class="keyword">elseif</span> subplotnum == 1 &amp; ~options_.relative_irf
0182 hh = figure(<span class="string">'Name'</span>,[<span class="string">'Orthogonalized shock to '</span> tit(i,:)],<span class="string">'Visible'</span>,<span class="string">'off'</span>);
0183 <span class="keyword">end</span>
0184 <span class="keyword">else</span>
0185 <span class="keyword">if</span> subplotnum == 1 &amp; options_.relative_irf
0186 hh = figure(<span class="string">'Name'</span>,[<span class="string">'Relative response to orthogonalized shock to '</span> tit(i,:)]);
0187 <span class="keyword">elseif</span> subplotnum == 1 &amp; ~options_.relative_irf
0188 hh = figure(<span class="string">'Name'</span>,[<span class="string">'Orthogonalized shock to '</span> tit(i,:)]);
0189 <span class="keyword">end</span>
0190 <span class="keyword">end</span>
0191 set(0,<span class="string">'CurrentFigure'</span>,hh)
0192 subplot(nn,nn,subplotnum);
0193 plot([1 options_.irf],[0 0],<span class="string">'-r'</span>,<span class="string">'linewidth'</span>,0.5);
0194 hold on
0195 <span class="keyword">for</span> k = 1:9
0196 plot(1:options_.irf,DistribIRF(:,k,j,i),<span class="string">'-g'</span>,<span class="string">'linewidth'</span>,0.5)
0197 <span class="keyword">end</span>
0198 plot(1:options_.irf,MeanIRF(:,j,i),<span class="string">'-k'</span>,<span class="string">'linewidth'</span>,1)
0199 xlim([1 options_.irf]);
0200 hold off
0201 name = deblank(varlist(j,:));
0202 NAMES = strvcat(NAMES,name);
0203 <span class="keyword">if</span> options_.TeX
0204 texname = deblank(varlist_TeX(j,:));
0205 TEXNAMES = strvcat(TEXNAMES,[<span class="string">'$'</span> texname <span class="string">'$'</span>]);
0206 <span class="keyword">end</span>
0207 title(name,<span class="string">'Interpreter'</span>,<span class="string">'none'</span>)
0208 <span class="keyword">end</span>
0209 <span class="keyword">if</span> subplotnum == MaxNumberOfPlotPerFigure | (j == nvar &amp; subplotnum&gt;0)
0210 figunumber = figunumber+1;
0211 set(hh,<span class="string">'visible'</span>,<span class="string">'on'</span>)
0212 eval([<span class="string">'print -depsc2 '</span> DirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_Bayesian_IRF_'</span> deblank(tit(i,:)) <span class="string">'_'</span> int2str(figunumber)]);
0213 eval([<span class="string">'print -dpdf '</span> DirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_Bayesian_IRF_'</span> deblank(tit(i,:)) <span class="string">'_'</span> int2str(figunumber)]);
0214 saveas(hh,[DirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_Bayesian_IRF_'</span> deblank(tit(i,:)) <span class="string">'_'</span> int2str(figunumber) <span class="string">'.fig'</span>]);
0215 set(hh,<span class="string">'visible'</span>,<span class="string">'off'</span>)
0216 <span class="keyword">if</span> options_.nograph, close(hh), <span class="keyword">end</span>
0217 <span class="keyword">if</span> options_.TeX
0218 fprintf(fidTeX,<span class="string">'\\begin{figure}[H]\n'</span>);
0219 <span class="keyword">for</span> jj = 1:size(TEXNAMES,1)
0220 fprintf(fidTeX,[<span class="string">'\\psfrag{%s}[1][][0.5][0]{%s}\n'</span>],deblank(NAMES(jj,:)),deblank(TEXNAMES(jj,:)));
0221 <span class="keyword">end</span>
0222 fprintf(fidTeX,<span class="string">'\\centering \n'</span>);
0223 fprintf(fidTeX,<span class="string">'\\includegraphics[scale=0.5]{%s_Bayesian_IRF_%s}\n'</span>,M_.fname,deblank(tit(i,:)));
0224 <span class="keyword">if</span> options_.relative_irf
0225 fprintf(fidTeX,[<span class="string">'\\caption{Bayesian relative IRF.}'</span>]);
0226 <span class="keyword">else</span>
0227 fprintf(fidTeX,<span class="string">'\\caption{Bayesian IRF.}'</span>);
0228 <span class="keyword">end</span>
0229 fprintf(fidTeX,<span class="string">'\\label{Fig:BayesianIRF:%s}\n'</span>,deblank(tit(i,:)));
0230 fprintf(fidTeX,<span class="string">'\\end{figure}\n'</span>);
0231 fprintf(fidTeX,<span class="string">' \n'</span>);
0232 <span class="keyword">end</span>
0233 subplotnum = 0;
0234 <span class="keyword">end</span>
0235 <span class="keyword">end</span><span class="comment">% loop over selected endo_var</span>
0236 <span class="keyword">end</span><span class="comment">% loop over exo_var</span>
0237 <span class="comment">%%</span>
0238 <span class="keyword">if</span> options_.TeX
0239 fprintf(fidTeX,<span class="string">'%% End of TeX file.\n'</span>);
0240 fclose(fidTeX);
0241 <span class="keyword">end</span>
0242 fprintf(<span class="string">'MH: Posterior IRFs, done!\n'</span>);</pre></div>
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<title>Description of PosteriorSmoother</title>
<meta name="keywords" content="PosteriorSmoother">
<meta name="description" content="stephane.adjemian@ens.fr [09-25-2005]">
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<div><a href="../index.html">Home</a> &gt; <a href="index.html">.</a> &gt; PosteriorSmoother.m</div>
<!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png">&nbsp;Master index</a></td>
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<h1>PosteriorSmoother
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>stephane.adjemian@ens.fr [09-25-2005]</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function PosteriorSmoother(Y,gend, type) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> stephane.adjemian@ens.fr [09-25-2005]</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="CheckPath.html" class="code" title="function DirectoryName = CheckPath(type)">CheckPath</a> 06-03-2005</li><li><a href="DsgeSmoother.html" class="code" title="function [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK] = DsgeSmoother(xparam1,gend,Y)">DsgeSmoother</a> stephane.adjemian@cepremap.cnrs.fr [09-07-2004]</li><li><a href="GetOneDraw.html" class="code" title="function [xparams, logpost] = GetOneDraw(type)">GetOneDraw</a> stephane.adjemian@ens.fr [09-25-2005]</li><li><a href="resol.html" class="code" title="function [dr,info]=resol(ys,check_flag)">resol</a> Copyright (C) 2001 Michel Juillard</li><li><a href="set_all_parameters.html" class="code" title="function set_all_parameters(xparam1)">set_all_parameters</a> </li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function PosteriorSmoother(Y,gend, type)</a>
0002 <span class="comment">% stephane.adjemian@ens.fr [09-25-2005]</span>
0003 <span class="keyword">global</span> options_ estim_params_ oo_ M_
0004
0005 nvx = estim_params_.nvx;
0006 nvn = estim_params_.nvn;
0007 ncx = estim_params_.ncx;
0008 ncn = estim_params_.ncn;
0009 np = estim_params_.np ;
0010 npar = nvx+nvn+ncx+ncn+np;
0011 offset = npar-np;
0012 naK = length(options_.filter_step_ahead);
0013 <span class="comment">%%</span>
0014 MaxNumberOfPlotPerFigure = 4;<span class="comment">% The square root must be an integer!</span>
0015 MaxNumberOfBytes=options_.MaxNumberOfBytes;
0016 endo_nbr=M_.endo_nbr;
0017 exo_nbr=M_.exo_nbr;
0018 nvobs = size(options_.varobs,1);
0019 nn = sqrt(MaxNumberOfPlotPerFigure);
0020 <span class="comment">%%</span>
0021 <a href="CheckPath.html" class="code" title="function DirectoryName = CheckPath(type)">CheckPath</a>(<span class="string">'Plots/'</span>);
0022 DirectoryName = <a href="CheckPath.html" class="code" title="function DirectoryName = CheckPath(type)">CheckPath</a>(<span class="string">'metropolis'</span>);
0023 load([ DirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_mh_history'</span>])
0024 FirstMhFile = record.KeepedDraws.FirstMhFile;
0025 FirstLine = record.KeepedDraws.FirstLine;
0026 TotalNumberOfMhFiles = sum(record.MhDraws(:,2)); LastMhFile = TotalNumberOfMhFiles;
0027 TotalNumberOfMhDraws = sum(record.MhDraws(:,1));
0028 NumberOfDraws = TotalNumberOfMhDraws-floor(options_.mh_drop*TotalNumberOfMhDraws);
0029 clear record;
0030 MAX_nruns = ceil(options_.MaxNumberOfBytes/(npar+2)/8);
0031 MAX_nsmoo = ceil(MaxNumberOfBytes/((endo_nbr)*gend)/8);
0032 MAX_ninno = ceil(MaxNumberOfBytes/(exo_nbr*gend)/8);
0033 MAX_nerro = ceil(MaxNumberOfBytes/(size(options_.varobs,1)*gend)/8);
0034 MAX_naK = ceil(MaxNumberOfBytes/(size(options_.varobs,1)*length(options_.filter_step_ahead)*gend)/8);
0035 <span class="comment">%%</span>
0036 B = round(0.25*NumberOfDraws);
0037 <span class="comment">%%</span>
0038 varlist = options_.varlist;
0039 <span class="keyword">if</span> isempty(varlist)
0040 varlist = M_.endo_names;
0041 SelecVariables = transpose(1:M_.endo_nbr);
0042 nvar = M_.endo_nbr;
0043 <span class="keyword">else</span>
0044 nvar = size(varlist,1);
0045 SelecVariables = [];
0046 <span class="keyword">for</span> i=1:nvar
0047 <span class="keyword">if</span> ~isempty(strmatch(varlist(i,:),M_.endo_names,<span class="string">'exact'</span>))
0048 SelecVariables = [SelecVariables;strmatch(varlist(i,:),M_.endo_names,<span class="string">'exact'</span>)];
0049 <span class="keyword">end</span>
0050 <span class="keyword">end</span>
0051 <span class="keyword">end</span>
0052
0053 irun1 = 1;
0054 irun2 = 1;
0055 irun3 = 1;
0056 irun4 = 1;
0057 irun5 = 1;
0058 ifil1 = 1;
0059 ifil2 = 1;
0060 ifil3 = 1;
0061 ifil4 = 1;
0062 ifil5 = 1;
0063 h = waitbar(0,<span class="string">'Bayesian smoother...'</span>);
0064 <span class="keyword">if</span> B &lt;= MAX_nruns
0065 stock_param = zeros(B, npar);
0066 stock_logpo = zeros(B,1);
0067 stock_ys = zeros(B,endo_nbr);
0068 <span class="keyword">else</span>
0069 stock_param = zeros(MAX_nruns, npar);
0070 stock_logpo = zeros(MAX_nruns,1);
0071 stock_ys = zeros(MAX_nruns,endo_nbr);
0072 <span class="keyword">end</span>
0073 <span class="keyword">if</span> options_.smoother
0074 <span class="keyword">if</span> B &lt;= MAX_nsmoo
0075 stock_smooth = zeros(endo_nbr,gend,B);
0076 <span class="keyword">else</span>
0077 stock_smooth = zeros(endo_nbr,gend,MAX_nsmoo);
0078 <span class="keyword">end</span>
0079 <span class="keyword">if</span> B &lt;= MAX_ninno
0080 stock_innov = zeros(exo_nbr,gend,B);
0081 <span class="keyword">else</span>
0082 stock_innov = zeros(exo_nbr,gend,MAX_ninno);
0083 <span class="keyword">end</span>
0084 <span class="keyword">if</span> nvn &amp; B &lt;= MAX_nerro
0085 stock_error = zeros(nvobs,gend,B);
0086 <span class="keyword">else</span> nvn &amp; B &gt; MAX_nerro
0087 stock_error = zeros(nvobs,gend,MAX_nerro);
0088 <span class="keyword">end</span>
0089 <span class="keyword">end</span>
0090 <span class="keyword">if</span> options_.filter_step_ahead ~= 0
0091 <span class="keyword">if</span> B &lt;= MAX_naK
0092 stock_filter = zeros(naK,endo_nbr,gend+options_.filter_step_ahead(end),B);
0093 <span class="keyword">else</span>
0094 stock_filter = zeros(naK,endo_nbr,gend+options_.filter_step_ahead(end),MAX_naK);
0095 <span class="keyword">end</span>
0096 <span class="keyword">end</span>
0097 <span class="keyword">for</span> b=1:B
0098 <span class="comment">%deep = GetOneDraw(NumberOfDraws,FirstMhFile,LastMhFile,FirstLine,MAX_nruns,DirectoryName);</span>
0099 [deep, logpo] = <a href="GetOneDraw.html" class="code" title="function [xparams, logpost] = GetOneDraw(type)">GetOneDraw</a>(type);
0100 <a href="set_all_parameters.html" class="code" title="function set_all_parameters(xparam1)">set_all_parameters</a>(deep);
0101 dr = <a href="resol.html" class="code" title="function [dr,info]=resol(ys,check_flag)">resol</a>(oo_.steady_state,0);
0102 [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK] = <a href="DsgeSmoother.html" class="code" title="function [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK] = DsgeSmoother(xparam1,gend,Y)">DsgeSmoother</a>(deep,gend,Y);
0103
0104 stock_smooth(:,:,irun1) = alphahat(1:endo_nbr,:);
0105 <span class="keyword">if</span> nvx
0106 stock_innov(:,:,irun2) = etahat;
0107 <span class="keyword">end</span>
0108 <span class="keyword">if</span> nvn
0109 stock_error(:,:,irun3) = epsilonhat;
0110 <span class="keyword">end</span>
0111 <span class="keyword">if</span> naK
0112 stock_filter(:,:,:,irun4) = aK(options_.filter_step_ahead,1:endo_nbr,:);
0113 <span class="keyword">end</span>
0114 stock_param(irun5,:) = deep;
0115 stock_logpo(irun5,1) = logpo;
0116 stock_ys(irun5,:) = SteadyState';
0117
0118 irun1 = irun1 + 1;
0119 irun2 = irun2 + 1;
0120 irun3 = irun3 + 1;
0121 irun4 = irun4 + 1;
0122 irun5 = irun5 + 1;
0123
0124 <span class="keyword">if</span> irun1 &gt; MAX_nsmoo | b == B
0125 <span class="keyword">if</span> b == B
0126 stock_smooth = stock_smooth(:,:,1:irun1-1);
0127 <span class="keyword">end</span>
0128 stock = stock_smooth;
0129 save([DirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_smooth'</span> int2str(ifil1)],<span class="string">'stock'</span>);
0130 ifil1 = ifil1 + 1;
0131 irun1 = 1;
0132 <span class="keyword">end</span>
0133
0134 <span class="keyword">if</span> nvx &amp; (irun2 &gt; MAX_ninno | b == B)
0135 <span class="keyword">if</span> b == B
0136 stock_innov = stock_innov(:,:,1:irun2-1);
0137 <span class="keyword">end</span>
0138 stock = stock_innov;
0139 save([DirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_inno'</span> int2str(ifil2)],<span class="string">'stock'</span>);
0140 ifil2 = ifil2 + 1;
0141 irun2 = 1;
0142 <span class="keyword">end</span>
0143
0144 <span class="keyword">if</span> nvn &amp; (irun3 &gt; MAX_error | b == B)
0145 <span class="keyword">if</span> b == B
0146 stock_error = stock_error(:,:,1:irun3-1);
0147 <span class="keyword">end</span>
0148 stock = stock_error;
0149 save([DirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_error'</span> int2str(ifil3)],<span class="string">'stock'</span>);
0150 ifil3 = ifil3 + 1;
0151 irun3 = 1;
0152 <span class="keyword">end</span>
0153
0154 <span class="keyword">if</span> naK &amp; (irun4 &gt; MAX_naK | b == B)
0155 <span class="keyword">if</span> b == B
0156 stock_filter = stock_filter(:,:,:,1:irun4-1);
0157 <span class="keyword">end</span>
0158 stock = stock_filter;
0159 save([DirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_filter'</span> int2str(ifil4)],<span class="string">'stock'</span>);
0160 ifil4 = ifil4 + 1;
0161 irun4 = 1;
0162 <span class="keyword">end</span>
0163
0164 <span class="keyword">if</span> irun5 &gt; MAX_nruns | b == B
0165 <span class="keyword">if</span> b == B
0166 stock_param = stock_param(1:irun5-1,:);
0167 stock_logpo = stock_logpo(1:irun5-1,1);
0168 stock_ys = stock_ys(1:irun5-1,:);
0169 <span class="keyword">end</span>
0170 stock = stock_param;
0171 save([DirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_param'</span> int2str(ifil5)],<span class="string">'stock'</span>,<span class="string">'stock_logpo'</span>,<span class="string">'stock_ys'</span>);
0172 ifil5 = ifil5 + 1;
0173 irun5 = 1;
0174 <span class="keyword">end</span>
0175
0176 waitbar(b/B,h);
0177 <span class="keyword">end</span>
0178 close(h)
0179
0180 stock_gend=gend;
0181 stock_data=Y;
0182 save([DirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_data'</span>],<span class="string">'stock_gend'</span>,<span class="string">'stock_data'</span>);</pre></div>
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<title>Description of ReshapeMatFiles</title>
<meta name="keywords" content="ReshapeMatFiles">
<meta name="description" content="Reshape and sort (along the mcmc simulations) the mat files generated">
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<div><a href="../index.html">Home</a> &gt; <a href="index.html">.</a> &gt; ReshapeMatFiles.m</div>
<!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png">&nbsp;Master index</a></td>
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<h1>ReshapeMatFiles
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>Reshape and sort (along the mcmc simulations) the mat files generated</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function ReshapeMatFiles(type) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> Reshape and sort (along the mcmc simulations) the mat files generated
by DYNARE.
4D-arrays are splitted along the first dimension.
3D-arrays are splitted along the second dimension.</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="CheckPath.html" class="code" title="function DirectoryName = CheckPath(type)">CheckPath</a> 06-03-2005</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="PosteriorIRF.html" class="code" title="function PosteriorIRF(type)">PosteriorIRF</a> stephane.adjemian@ens.fr [09-25-2005]</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function ReshapeMatFiles(type)</a>
0002 <span class="comment">% Reshape and sort (along the mcmc simulations) the mat files generated</span>
0003 <span class="comment">% by DYNARE.</span>
0004 <span class="comment">%</span>
0005 <span class="comment">% 4D-arrays are splitted along the first dimension.</span>
0006 <span class="comment">% 3D-arrays are splitted along the second dimension.</span>
0007 <span class="comment">%</span>
0008 <span class="comment">%</span>
0009 <span class="keyword">global</span> M_ options_
0010
0011 MhDirectoryName = [ <a href="CheckPath.html" class="code" title="function DirectoryName = CheckPath(type)">CheckPath</a>(<span class="string">'metropolis'</span>) <span class="string">'/'</span> ];
0012
0013 <span class="keyword">switch</span> type
0014 <span class="keyword">case</span> <span class="string">'irf'</span>
0015 CAPtype = <span class="string">'IRF'</span>;
0016 TYPEsize = [ options_.irf , M_.endo_nbr , M_.exo_nbr ];
0017 TYPEarray = 4;
0018 <span class="keyword">case</span> <span class="string">'smooth'</span>
0019 CAPtype = <span class="string">'SMOOTH'</span>;
0020 TYPEsize = [ M_.endo_nbr , options_.nobs ];
0021 TYPEarray = 3;
0022 <span class="keyword">case</span> <span class="string">'filter'</span>
0023 CAPtype = <span class="string">'FILTER'</span>;
0024 TYPEsize = [ M_.endo_nbr , options_.nobs + 1 ];<span class="comment">% TO BE CHECKED!</span>
0025 TYPEarray = 3;
0026 <span class="keyword">case</span> <span class="string">'error'</span>
0027 CAPtype = <span class="string">'ERROR'</span>;
0028 TYPEsize = [ size(options_.varobs,1) , options_.nobs ];
0029 TYPEarray = 3;
0030 <span class="keyword">case</span> <span class="string">'innov'</span>
0031 CAPtype = <span class="string">'INNOV'</span>;
0032 TYPEsize = [ M_.exo_nbr , options_.nobs ];
0033 TYPEarray = 3;
0034 <span class="keyword">case</span> <span class="string">'forcst'</span>
0035 CAPtype = <span class="string">'FORCST'</span>;
0036 TYPEsize = [ M_.endo_nbr , options_.forecast ];
0037 TYPEarray = 3;
0038 <span class="keyword">case</span> <span class="string">'forcst1'</span>
0039 CAPtype = <span class="string">'FORCST1'</span>;
0040 TYPEsize = [ M_.endo_nbr , options_.forecast ];
0041 TYPEarray = 3;
0042 <span class="keyword">otherwise</span>
0043 disp(<span class="string">'ReshapeMatFiles :: Unknown argument!'</span>)
0044 <span class="keyword">return</span>
0045 <span class="keyword">end</span>
0046
0047 TYPEfiles = dir([MhDirectoryName M_.fname <span class="string">'_'</span> type <span class="string">'*'</span>]);
0048 NumberOfTYPEfiles = length(TYPEfiles);
0049 B = options_.B;
0050
0051 <span class="keyword">switch</span> TYPEarray
0052 <span class="keyword">case</span> 4
0053 <span class="keyword">if</span> NumberOfTYPEfiles &gt; 1
0054 NumberOfPeriodsPerTYPEfiles = ceil(TYPEsize(1)/NumberOfTYPEfiles);
0055 reste = TYPEsize(1)-NumberOfPeriodsPerTYPEfiles*(NumberOfTYPEfiles-1);
0056 idx = 0;
0057 jdx = 0;
0058 <span class="keyword">for</span> f1=1:NumberOfTYPEfiles-1
0059 eval([<span class="string">'STOCK_'</span> CAPtype <span class="string">' = zeros(NumberOfPeriodsPerTYPEfiles,TYPEsize(2),TYPEsize(3),B);'</span>])
0060 <span class="keyword">for</span> f2 = 1:NumberOfTYPEfiles
0061 load([MhDirectoryName M_.fname <span class="string">'_'</span> type int2str(f2)]);
0062 eval([<span class="string">'STOCK_'</span> CAPtype <span class="string">'(:,:,:,idx+1:idx+size(stock_'</span> type <span class="string">',4))=stock_'</span> type <span class="string">'(jdx+1:jdx+NumberOfPeriodsPerTYPEfiles,:,:,:);'</span>])
0063 eval([<span class="string">'idx = idx + size(stock_'</span> type <span class="string">',4);'</span>])
0064 <span class="keyword">end</span>
0065 eval([<span class="string">'STOCK_'</span> CAPtype <span class="string">' = sort(STOCK_'</span> CAPtype <span class="string">',4);'</span>])
0066 save([MhDirectoryName M_.fname <span class="string">'_'</span> CAPtype <span class="string">'s'</span> int2str(f1)],[<span class="string">'STOCK_'</span> CAPtype]);
0067 jdx = jdx + NumberOfPeriodsPerTYPEfiles;
0068 idx = 0;
0069 <span class="keyword">end</span>
0070 eval([<span class="string">'STOCK_'</span> CAPtype <span class="string">' = zeros(reste,TYPEsize(2),TYPEsize(3),B);'</span>])
0071 <span class="keyword">for</span> f2 = 1:NumberOfTYPEfiles
0072 load([MhDirectoryName M_.fname <span class="string">'_'</span> type int2str(f2)]);
0073 eval([<span class="string">'STOCK_'</span> CAPtype <span class="string">'(:,:,:,idx+1:idx+size(stock_'</span> type <span class="string">',4))=stock_'</span> type <span class="string">'(jdx+1:jdx+reste,:,:,:);'</span>])
0074 eval([<span class="string">'idx = idx + size(stock_'</span> type <span class="string">',4);'</span>])
0075 <span class="keyword">end</span>
0076 eval([<span class="string">'STOCK_'</span> CAPtype <span class="string">' = sort(STOCK_'</span> CAPtype <span class="string">',4);'</span>])
0077 save([MhDirectoryName M_.fname <span class="string">'_'</span> CAPtype <span class="string">'s'</span> int2str(NumberOfTYPEfiles)],[<span class="string">'STOCK_'</span> CAPtype]);
0078 <span class="keyword">else</span>
0079 load([MhDirectoryName M_.fname <span class="string">'_'</span> type <span class="string">'1'</span>]);
0080 eval([<span class="string">'STOCK_'</span> CAPtype <span class="string">' = sort(stock_'</span> type <span class="string">',4);'</span>])
0081 save([MhDirectoryName M_.fname <span class="string">'_'</span> CAPtype <span class="string">'s'</span> int2str(1)],[<span class="string">'STOCK_'</span> CAPtype ]);
0082 <span class="keyword">end</span>
0083 <span class="keyword">for</span> file = 1:NumberOfTYPEfiles
0084 delete([MhDirectoryName M_.fname <span class="string">'_'</span> type int2str(file) <span class="string">'.mat'</span>])
0085 <span class="keyword">end</span>
0086 <span class="keyword">case</span> 3
0087 <span class="keyword">if</span> NumberOfTYPEfiles&gt;1
0088 NumberOfPeriodsPerTYPEfiles = ceil( TYPEsize(2)/NumberOfTYPEfiles );
0089 reste = TYPEsize(2)-NumberOfPeriodsPerTYPEfiles*(NumberOfTYPEfiles-1);
0090 idx = 0;
0091 jdx = 0;
0092 <span class="keyword">for</span> f1=1:NumberOfTYPEfiles-1
0093 eval([<span class="string">'STOCK_'</span> CAPtype <span class="string">' = zeros(TYPEsize(1),NumberOfPeriodsPerTYPEfiles,B);'</span>])
0094 <span class="keyword">for</span> f2 = 1:NumberOfTYPEfiles
0095 load([MhDirectoryName M_.fname <span class="string">'_'</span> type int2str(f2)]);
0096 eval([<span class="string">'STOCK_'</span> CAPtype <span class="string">'(:,:,idx+1:idx+size(stock_ '</span> type <span class="string">',3))=stock_'</span> type <span class="string">'(:,jdx+1:jdx+NumberOfPeriodsPerTYPEfiles,:);'</span>])
0097 eval([<span class="string">'idx = idx + size(stock_'</span> type <span class="string">',3);'</span>])
0098 <span class="keyword">end</span>
0099 eval([<span class="string">'STOCK_'</span> CAPtype <span class="string">' = sort(STOCK_'</span> CAPtype <span class="string">',3);'</span>])
0100 save([MhDirectoryName M_.fname <span class="string">'_'</span> CAPtype <span class="string">'s'</span> int2str(f1)],[<span class="string">'STOCK_'</span> CAPtype]);
0101 jdx = jdx + NumberOfPeriodsPerTYPEfiles;
0102 idx = 0;
0103 <span class="keyword">end</span>
0104 eval([<span class="string">'STOCK_'</span> CAPtype <span class="string">' = zeros(TYPEsize(1),reste,B);'</span>])
0105 <span class="keyword">for</span> f2 = 1:NumberOfTYPEfiles
0106 load([MhDirectoryName M_.fname <span class="string">'_'</span> type int2str(f2)]);
0107 eval([<span class="string">'STOCK_'</span> CAPtype <span class="string">'(:,:,idx+1:idx+size(stock_'</span> type <span class="string">',3))=stock_'</span> type <span class="string">'(:,jdx+1:jdx+reste,:);'</span>])
0108 eval([<span class="string">'idx = idx + size(stock_'</span> type <span class="string">',3);'</span>])
0109 <span class="keyword">end</span>
0110 eval([<span class="string">'STOCK_'</span> CAPtype <span class="string">' = sort(STOCK_'</span> CAPtype <span class="string">',3);'</span>])
0111 save([MhDirectoryName M_.fname <span class="string">'_'</span> CAPtype <span class="string">'s'</span> int2str(NumberOfTYPEfiles)],[<span class="string">'STOCK_'</span> CAPtype]);
0112 <span class="keyword">else</span>
0113 load([MhDirectoryName M_.fname <span class="string">'_'</span> type <span class="string">'1'</span>]);
0114 eval([<span class="string">'STOCK_'</span> CAPtype <span class="string">' = sort(stock_'</span> type <span class="string">',3);'</span>])
0115 save([MhDirectoryName M_.fname <span class="string">'_'</span> CAPtype <span class="string">'s'</span> int2str(1)],[<span class="string">'STOCK_'</span> CAPtype ]);
0116 <span class="keyword">end</span>
0117 <span class="keyword">for</span> file = 1:NumberOfTYPEfiles
0118 delete([MhDirectoryName M_.fname <span class="string">'_'</span> type int2str(file) <span class="string">'.mat'</span>])
0119 <span class="keyword">end</span>
0120 <span class="keyword">end</span></pre></div>
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<!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png">&nbsp;Master index</a></td>
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<h1>UnivariateSpectralDensity
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>stephane.adjemian@ens.fr</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function [omega,f] = UnivariateSpectralDensity(dr,var_list) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> stephane.adjemian@ens.fr
This function computes the theoretical spectral density of each
endogenous variable declared in var_list. Results are stored in
oo_ and may be plotted.
Adapted from th_autocovariances.m.</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="kalman_transition_matrix.html" class="code" title="function [A,B] = kalman_transition_matrix(dr)">kalman_transition_matrix</a> makes transition matrices out of ghx and ghu for Kalman filter</li><li><a href="lyapunov_symm.html" class="code" title="function [x,ns_var]=lyapunov_symm(a,b)">lyapunov_symm</a> solves x-a*x*a'=b for b (and then x) symmetrical</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="stoch_simul.html" class="code" title="function info=stoch_simul(var_list)">stoch_simul</a> Copyright (C) 2001 Michel Juillard</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <span class="comment">% stephane.adjemian@ens.fr</span>
0002 <a name="_sub0" href="#_subfunctions" class="code">function [omega,f] = UnivariateSpectralDensity(dr,var_list)</a>
0003 <span class="comment">% This function computes the theoretical spectral density of each</span>
0004 <span class="comment">% endogenous variable declared in var_list. Results are stored in</span>
0005 <span class="comment">% oo_ and may be plotted.</span>
0006 <span class="comment">%</span>
0007 <span class="comment">% Adapted from th_autocovariances.m.</span>
0008 <span class="keyword">global</span> options_ oo_ M_
0009
0010 omega = []; f = [];
0011
0012 <span class="keyword">if</span> options_.order &gt; 1
0013 disp(<span class="string">'UnivariateSpectralDensity :: I Cannot compute the theoretical spectral density'</span>)
0014 disp(<span class="string">'with a second order approximation of the DSGE model!'</span>)
0015 disp(<span class="string">'Set order = 1.'</span>)
0016 <span class="keyword">return</span>
0017 <span class="keyword">end</span>
0018
0019 pltinfo = 1;<span class="comment">%options_.SpectralDensity.Th.plot;</span>
0020 cutoff = 100;<span class="comment">%options_.SpectralDensity.Th.cutoff;</span>
0021 sdl = 0.1;<span class="comment">%options_.SepctralDensity.Th.sdl;</span>
0022 omega = (0:sdl:pi)';
0023 GridSize = length(omega);
0024 exo_names_orig_ord = M_.exo_names_orig_ord;
0025 <span class="keyword">if</span> sscanf(version(<span class="string">'-release'</span>),<span class="string">'%d'</span>) &lt; 13
0026 warning off
0027 <span class="keyword">else</span>
0028 eval(<span class="string">'warning off MATLAB:dividebyzero'</span>)
0029 <span class="keyword">end</span>
0030 <span class="keyword">if</span> nargin&lt;2
0031 var_list = [];
0032 <span class="keyword">end</span>
0033 nvar = size(var_list,1);
0034 <span class="keyword">if</span> nvar == 0
0035 nvar = length(dr.order_var);
0036 ivar = [1:nvar]';
0037 <span class="keyword">else</span>
0038 ivar=zeros(nvar,1);
0039 <span class="keyword">for</span> i=1:nvar
0040 i_tmp = strmatch(var_list(i,:),M_.endo_names,<span class="string">'exact'</span>);
0041 <span class="keyword">if</span> isempty(i_tmp)
0042 error ([<span class="string">'One of the variable specified does not exist'</span>]) ;
0043 <span class="keyword">else</span>
0044 ivar(i) = i_tmp;
0045 <span class="keyword">end</span>
0046 <span class="keyword">end</span>
0047 <span class="keyword">end</span>
0048 f = zeros(nvar,GridSize);
0049 ghx = dr.ghx;
0050 ghu = dr.ghu;
0051 npred = dr.npred;
0052 nstatic = dr.nstatic;
0053 kstate = dr.kstate;
0054 order = dr.order_var;
0055 iv(order) = [1:length(order)];
0056 nx = size(ghx,2);
0057 ikx = [nstatic+1:nstatic+npred];
0058 A = zeros(nx,nx);
0059 k0 = kstate(find(kstate(:,2) &lt;= M_.maximum_lag+1),:);
0060 i0 = find(k0(:,2) == M_.maximum_lag+1);
0061 i00 = i0;
0062 n0 = length(i0);
0063 A(i0,:) = ghx(ikx,:);
0064 AS = ghx(:,i0);
0065 ghu1 = zeros(nx,M_.exo_nbr);
0066 ghu1(i0,:) = ghu(ikx,:);
0067 <span class="keyword">for</span> i=M_.maximum_lag:-1:2
0068 i1 = find(k0(:,2) == i);
0069 n1 = size(i1,1);
0070 j1 = zeros(n1,1);
0071 j2 = j1;
0072 <span class="keyword">for</span> k1 = 1:n1
0073 j1(k1) = find(k0(i00,1)==k0(i1(k1),1));
0074 j2(k1) = find(k0(i0,1)==k0(i1(k1),1));
0075 <span class="keyword">end</span>
0076 AS(:,j1) = AS(:,j1)+ghx(:,i1);
0077 i0 = i1;
0078 <span class="keyword">end</span>
0079 b = ghu1*M_.Sigma_e*ghu1';
0080 [A,B] = <a href="kalman_transition_matrix.html" class="code" title="function [A,B] = kalman_transition_matrix(dr)">kalman_transition_matrix</a>(dr);
0081 <span class="comment">% index of predetermined variables in A</span>
0082 i_pred = [nstatic+(1:npred) M_.endo_nbr+1:length(A)];
0083 A = A(i_pred,i_pred);
0084 [vx, ns_var] = <a href="lyapunov_symm.html" class="code" title="function [x,ns_var]=lyapunov_symm(a,b)">lyapunov_symm</a>(A,b);
0085 i_ivar = find(~ismember(ivar,dr.order_var(ns_var+nstatic)));
0086 ivar = ivar(i_ivar);
0087 iky = iv(ivar);
0088 aa = ghx(iky,:);
0089 bb = ghu(iky,:);
0090 Gamma = zeros(nvar,cutoff+1);
0091 tmp = aa*vx*aa'+ bb*M_.Sigma_e*bb';
0092 k = find(abs(tmp) &lt; 1e-12);
0093 tmp(k) = 0;
0094 Gamma(:,1) = diag(tmp);
0095 vxy = (A*vx*aa'+ghu1*M_.Sigma_e*bb');
0096 tmp = aa*vxy;
0097 k = find(abs(tmp) &lt; 1e-12);
0098 tmp(k) = 0;
0099 Gamma(:,2) = diag(tmp);
0100 <span class="keyword">for</span> i=2:cutoff
0101 vxy = A*vxy;
0102 tmp = aa*vxy;
0103 k = find(abs(tmp) &lt; 1e-12);
0104 tmp(k) = 0;
0105 Gamma(:,i+1) = diag(tmp);
0106 <span class="keyword">end</span>
0107 H = 1:cutoff;
0108 <span class="keyword">for</span> i=1:nvar
0109 f(i,:) = Gamma(i,1)/(2*pi) + Gamma(i,H+1)*cos(H'*omega')/pi;
0110 <span class="keyword">end</span>
0111
0112 <span class="keyword">if</span> sscanf(version(<span class="string">'-release'</span>),<span class="string">'%d'</span>) &lt; 13
0113 warning on
0114 <span class="keyword">else</span>
0115 eval(<span class="string">'warning on MATLAB:dividebyzero'</span>)
0116 <span class="keyword">end</span>
0117
0118 <span class="keyword">if</span> pltinfo
0119 <span class="keyword">for</span> i= 1:nvar
0120 figure(<span class="string">'Name'</span>,[<span class="string">'Spectral Density of '</span> deblank(M_.endo_names(ivar(i),:)) <span class="string">'.'</span>])
0121 plot(omega,f(i,:),<span class="string">'-k'</span>,<span class="string">'linewidth'</span>,2)
0122 xlabel(<span class="string">'0 \leq \omega \leq \pi'</span>)
0123 ylabel(<span class="string">'f(\omega)'</span>)
0124 box on
0125 axis tight
0126 <span class="keyword">end</span>
0127 <span class="keyword">end</span></pre></div>
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<h1>asamin
&nbsp;&nbsp;<img src="../{MEXTYPE}.png" alt="Windows" border="0" title="Windows"></h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>ASAMIN A gateway function to Adaptive Simulated Annealing (ASA)</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>This is a script file. </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> ASAMIN A gateway function to Adaptive Simulated Annealing (ASA)
ASAMIN is a matlab gateway function to Lester Ingber's Adaptive
Simulated Annealing (ASA)
Copyright (c) 1999-2001 Shinichi Sakata. All Rights Reserved.
$Id: asamin.m,v 1.1.2.1 2004/03/27 14:41:06 michel Exp $
Usage:
asamin ('set')
lists the current value of each option.
asamin ('set', opt_name)
shows the current value of the option given by a character string
opt_name; e.g.,
asamin ('set', 'seed')
asamin ('set', opt_name, opt_value)
set the value opt_value to the option opt_name; e.g.,
asamin ('set', 'seed', 654342)
asamin ('set', 'asa_out_file', 'example.log')
The valid options in these commands are:
rand_seed
test_in_cost_func
use_rejected_cost
asa_out_file
limit_acceptances
limit_generated
limit_invalid
accepted_to_generated_ratio
cost_precision
maximum_cost_repeat
number_cost_samples
temperature_ratio_scale
cost_parameter_scale
temperature_anneal_scale
include_integer_parameters
user_initial_parameters
sequential_parameters
initial_parameter_temperature
acceptance_frequency_modulus
generated_frequency_modulus
reanneal_cost
reanneal_parameters
delta_x
rand_seed is the seed of the random number generation in ASA.
If test_in_cost_func is set to zero, the cost function should
simply return the value of the objective function. When
test_in_cost_func is set to one, asamin () calls the cost
function with a threshold value as well as the parameter
value. The cost function needs to judge if the value of the cost
function exceeds the threshold as well as compute the value of
the cost function when asamin () requires. (See COST FUNCTION
below for details.)
All other items but use_rejected_cost belong to structure
USER_OPTIONS in ASA. See ASA_README in the ASA package for
details. The default value of use_rejected_cost is zero. If you
set this option to one, ASA uses the current cost value to
compute certain indices, even if the current state is rejected by
the user cost function, provided that the current cost value is
lower than the cost value of the past best state. (See COST
FUNCTION below about the user cost function.)
asamin ('reset')
resets all option values to the hard-coded default values.
[fstar,xstar,grad,hessian,state] = ...
asamin ('minimize', func, xinit, xmin, xmax, xtype,...
parm1, parm2, ...)
minimizes the cost function func (also see COST FUNCTION below).
The argument xinit specifies the initial value of the arguments
of the cost function. Each element of the vectors xmin and xmax
specify the lower and upper bounds of the corresponding
argument. The vector xtype indicates the types of the
arguments. If xtype(i) is -1 if the i'th argument is real;
xtype(i) is 1 if the i'th argument is integer. If this argument
should be ignored in reannealing, multiply the corresponding
element of xtype by 2 so that the element is 2 or -2. All
parameters following xtype are optional and simply passed to the
cost function each time the cost function is called.
This way of calling asamin returns the following values:
fstar
The value of the objective function at xstar.
xstar
The argument vector at the exit from the ASA routine. If things go
well, xstar should be the minimizer of &quot;func&quot;.
grad
The gradient of &quot;func&quot; at xstar.
hessian
The Hessian of &quot;func&quot; at xstar.
state
The vector containing the information on the exit state.
state(1) is the exit_code, and state(2) is the cost flag. See
ASA_README for details.
COST FUNCTION
If test_in_cost_func is set to zero, asamin () calls the &quot;cost
function&quot; (say, cost_func) with one argument, say x (the real cost
function is evaluated at this point). Cost_func is expected to
return the value of the objective function and cost_flag, the
latter of which must be zero if any constraint (if any) is
violated; otherwise one.
When test_in_cost_func is equal to one, asamin () calls the &quot;cost
function&quot; (say, cost_func) with three arguments, say, x (at which
the real cost function is evaluated), critical_cost_value, and
no_test_flag. Asamin expects cost_func to return three scalar
values, say, cost_value, cost_flag, and user_acceptance_flag in the
following manner.
1. The function cost_func first checks if x satisfies the
constraints of the minimization problem. If any of the
constraints is not satisfied, cost_func sets zero to cost_flag
and return. (user_acceptance_flag and cost_value will not be used
by asamin () in this case.) If all constraints are satisfied, set
one to cost_flag, and proceed to the next step.
2. If asamin () calls cost_func with no_test_flag==1, cost_func
must compute the value of the cost function, set it to cost_value
and return. When no_test_flag==0, cost_func is expected to judge
if the value of the cost function is greater than
critical_cost_value. If the value of the cost function is found
greater than critical_cost_value, cost_func must set zero to
user_acceptance_flag and return. (asamin () will not use
cost_value in this case.) On the other hand, if the value of the
cost function is found no greater than critical_cost_value,
cost_func must compute the cost function at x, set it to
cost_value, and set one to user_acceptance_flag.
Remark: To understand the usefulness of test_in_cost_func == 1,
note that it is sometimes easier to check if the value of the cost
function is greater than critical_cost_value than compute the value
of the cost function. For example, suppose that the cost function g
is implicitly defined by an equation f(g(x),x)=0, where f is
strictly increasing in the first argument, and evaluation of g(x)
is computationally expensive (e.g., requiring an iterative method
to find a solution to f(y,x)=0). But we can easily show that
f(critical_cost_value,x) &lt; 0 if and only if g(x) &gt;
critical_cost_value. We can judge if g(x) &gt; critical_cost_value by
computing f(critical_cost_value,x). The value of g(x) is not
necessary.</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="dynare_estimation.html" class="code" title="function dynare_estimation(var_list_)">dynare_estimation</a> </li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <span class="comment">% ASAMIN A gateway function to Adaptive Simulated Annealing (ASA)</span>
0002 <span class="comment">%</span>
0003 <span class="comment">% ASAMIN is a matlab gateway function to Lester Ingber's Adaptive</span>
0004 <span class="comment">% Simulated Annealing (ASA)</span>
0005 <span class="comment">%</span>
0006 <span class="comment">% Copyright (c) 1999-2001 Shinichi Sakata. All Rights Reserved.</span>
0007 <span class="comment">%</span>
0008 <span class="comment">% $Id: asamin.m,v 1.1.2.1 2004/03/27 14:41:06 michel Exp $</span>
0009 <span class="comment">%</span>
0010 <span class="comment">% Usage:</span>
0011 <span class="comment">%</span>
0012 <span class="comment">% asamin ('set')</span>
0013 <span class="comment">%</span>
0014 <span class="comment">% lists the current value of each option.</span>
0015 <span class="comment">%</span>
0016 <span class="comment">% asamin ('set', opt_name)</span>
0017 <span class="comment">%</span>
0018 <span class="comment">% shows the current value of the option given by a character string</span>
0019 <span class="comment">% opt_name; e.g.,</span>
0020 <span class="comment">%</span>
0021 <span class="comment">% asamin ('set', 'seed')</span>
0022 <span class="comment">%</span>
0023 <span class="comment">% asamin ('set', opt_name, opt_value)</span>
0024 <span class="comment">%</span>
0025 <span class="comment">% set the value opt_value to the option opt_name; e.g.,</span>
0026 <span class="comment">%</span>
0027 <span class="comment">% asamin ('set', 'seed', 654342)</span>
0028 <span class="comment">% asamin ('set', 'asa_out_file', 'example.log')</span>
0029 <span class="comment">%</span>
0030 <span class="comment">% The valid options in these commands are:</span>
0031 <span class="comment">%</span>
0032 <span class="comment">% rand_seed</span>
0033 <span class="comment">% test_in_cost_func</span>
0034 <span class="comment">% use_rejected_cost</span>
0035 <span class="comment">% asa_out_file</span>
0036 <span class="comment">% limit_acceptances</span>
0037 <span class="comment">% limit_generated</span>
0038 <span class="comment">% limit_invalid</span>
0039 <span class="comment">% accepted_to_generated_ratio</span>
0040 <span class="comment">% cost_precision</span>
0041 <span class="comment">% maximum_cost_repeat</span>
0042 <span class="comment">% number_cost_samples</span>
0043 <span class="comment">% temperature_ratio_scale</span>
0044 <span class="comment">% cost_parameter_scale</span>
0045 <span class="comment">% temperature_anneal_scale</span>
0046 <span class="comment">% include_integer_parameters</span>
0047 <span class="comment">% user_initial_parameters</span>
0048 <span class="comment">% sequential_parameters</span>
0049 <span class="comment">% initial_parameter_temperature</span>
0050 <span class="comment">% acceptance_frequency_modulus</span>
0051 <span class="comment">% generated_frequency_modulus</span>
0052 <span class="comment">% reanneal_cost</span>
0053 <span class="comment">% reanneal_parameters</span>
0054 <span class="comment">% delta_x</span>
0055 <span class="comment">%</span>
0056 <span class="comment">% rand_seed is the seed of the random number generation in ASA.</span>
0057 <span class="comment">%</span>
0058 <span class="comment">% If test_in_cost_func is set to zero, the cost function should</span>
0059 <span class="comment">% simply return the value of the objective function. When</span>
0060 <span class="comment">% test_in_cost_func is set to one, asamin () calls the cost</span>
0061 <span class="comment">% function with a threshold value as well as the parameter</span>
0062 <span class="comment">% value. The cost function needs to judge if the value of the cost</span>
0063 <span class="comment">% function exceeds the threshold as well as compute the value of</span>
0064 <span class="comment">% the cost function when asamin () requires. (See COST FUNCTION</span>
0065 <span class="comment">% below for details.)</span>
0066 <span class="comment">%</span>
0067 <span class="comment">% All other items but use_rejected_cost belong to structure</span>
0068 <span class="comment">% USER_OPTIONS in ASA. See ASA_README in the ASA package for</span>
0069 <span class="comment">% details. The default value of use_rejected_cost is zero. If you</span>
0070 <span class="comment">% set this option to one, ASA uses the current cost value to</span>
0071 <span class="comment">% compute certain indices, even if the current state is rejected by</span>
0072 <span class="comment">% the user cost function, provided that the current cost value is</span>
0073 <span class="comment">% lower than the cost value of the past best state. (See COST</span>
0074 <span class="comment">% FUNCTION below about the user cost function.)</span>
0075 <span class="comment">%</span>
0076 <span class="comment">% asamin ('reset')</span>
0077 <span class="comment">% resets all option values to the hard-coded default values.</span>
0078 <span class="comment">%</span>
0079 <span class="comment">% [fstar,xstar,grad,hessian,state] = ...</span>
0080 <span class="comment">% asamin ('minimize', func, xinit, xmin, xmax, xtype,...</span>
0081 <span class="comment">% parm1, parm2, ...)</span>
0082 <span class="comment">%</span>
0083 <span class="comment">% minimizes the cost function func (also see COST FUNCTION below).</span>
0084 <span class="comment">% The argument xinit specifies the initial value of the arguments</span>
0085 <span class="comment">% of the cost function. Each element of the vectors xmin and xmax</span>
0086 <span class="comment">% specify the lower and upper bounds of the corresponding</span>
0087 <span class="comment">% argument. The vector xtype indicates the types of the</span>
0088 <span class="comment">% arguments. If xtype(i) is -1 if the i'th argument is real;</span>
0089 <span class="comment">% xtype(i) is 1 if the i'th argument is integer. If this argument</span>
0090 <span class="comment">% should be ignored in reannealing, multiply the corresponding</span>
0091 <span class="comment">% element of xtype by 2 so that the element is 2 or -2. All</span>
0092 <span class="comment">% parameters following xtype are optional and simply passed to the</span>
0093 <span class="comment">% cost function each time the cost function is called.</span>
0094 <span class="comment">%</span>
0095 <span class="comment">% This way of calling asamin returns the following values:</span>
0096 <span class="comment">%</span>
0097 <span class="comment">% fstar</span>
0098 <span class="comment">% The value of the objective function at xstar.</span>
0099 <span class="comment">% xstar</span>
0100 <span class="comment">% The argument vector at the exit from the ASA routine. If things go</span>
0101 <span class="comment">% well, xstar should be the minimizer of &quot;func&quot;.</span>
0102 <span class="comment">% grad</span>
0103 <span class="comment">% The gradient of &quot;func&quot; at xstar.</span>
0104 <span class="comment">% hessian</span>
0105 <span class="comment">% The Hessian of &quot;func&quot; at xstar.</span>
0106 <span class="comment">% state</span>
0107 <span class="comment">% The vector containing the information on the exit state.</span>
0108 <span class="comment">% state(1) is the exit_code, and state(2) is the cost flag. See</span>
0109 <span class="comment">% ASA_README for details.</span>
0110 <span class="comment">%</span>
0111 <span class="comment">%</span>
0112 <span class="comment">%</span>
0113 <span class="comment">% COST FUNCTION</span>
0114 <span class="comment">%</span>
0115 <span class="comment">% If test_in_cost_func is set to zero, asamin () calls the &quot;cost</span>
0116 <span class="comment">% function&quot; (say, cost_func) with one argument, say x (the real cost</span>
0117 <span class="comment">% function is evaluated at this point). Cost_func is expected to</span>
0118 <span class="comment">% return the value of the objective function and cost_flag, the</span>
0119 <span class="comment">% latter of which must be zero if any constraint (if any) is</span>
0120 <span class="comment">% violated; otherwise one.</span>
0121 <span class="comment">%</span>
0122 <span class="comment">% When test_in_cost_func is equal to one, asamin () calls the &quot;cost</span>
0123 <span class="comment">% function&quot; (say, cost_func) with three arguments, say, x (at which</span>
0124 <span class="comment">% the real cost function is evaluated), critical_cost_value, and</span>
0125 <span class="comment">% no_test_flag. Asamin expects cost_func to return three scalar</span>
0126 <span class="comment">% values, say, cost_value, cost_flag, and user_acceptance_flag in the</span>
0127 <span class="comment">% following manner.</span>
0128 <span class="comment">%</span>
0129 <span class="comment">% 1. The function cost_func first checks if x satisfies the</span>
0130 <span class="comment">% constraints of the minimization problem. If any of the</span>
0131 <span class="comment">% constraints is not satisfied, cost_func sets zero to cost_flag</span>
0132 <span class="comment">% and return. (user_acceptance_flag and cost_value will not be used</span>
0133 <span class="comment">% by asamin () in this case.) If all constraints are satisfied, set</span>
0134 <span class="comment">% one to cost_flag, and proceed to the next step.</span>
0135 <span class="comment">%</span>
0136 <span class="comment">% 2. If asamin () calls cost_func with no_test_flag==1, cost_func</span>
0137 <span class="comment">% must compute the value of the cost function, set it to cost_value</span>
0138 <span class="comment">% and return. When no_test_flag==0, cost_func is expected to judge</span>
0139 <span class="comment">% if the value of the cost function is greater than</span>
0140 <span class="comment">% critical_cost_value. If the value of the cost function is found</span>
0141 <span class="comment">% greater than critical_cost_value, cost_func must set zero to</span>
0142 <span class="comment">% user_acceptance_flag and return. (asamin () will not use</span>
0143 <span class="comment">% cost_value in this case.) On the other hand, if the value of the</span>
0144 <span class="comment">% cost function is found no greater than critical_cost_value,</span>
0145 <span class="comment">% cost_func must compute the cost function at x, set it to</span>
0146 <span class="comment">% cost_value, and set one to user_acceptance_flag.</span>
0147 <span class="comment">%</span>
0148 <span class="comment">% Remark: To understand the usefulness of test_in_cost_func == 1,</span>
0149 <span class="comment">% note that it is sometimes easier to check if the value of the cost</span>
0150 <span class="comment">% function is greater than critical_cost_value than compute the value</span>
0151 <span class="comment">% of the cost function. For example, suppose that the cost function g</span>
0152 <span class="comment">% is implicitly defined by an equation f(g(x),x)=0, where f is</span>
0153 <span class="comment">% strictly increasing in the first argument, and evaluation of g(x)</span>
0154 <span class="comment">% is computationally expensive (e.g., requiring an iterative method</span>
0155 <span class="comment">% to find a solution to f(y,x)=0). But we can easily show that</span>
0156 <span class="comment">% f(critical_cost_value,x) &lt; 0 if and only if g(x) &gt;</span>
0157 <span class="comment">% critical_cost_value. We can judge if g(x) &gt; critical_cost_value by</span>
0158 <span class="comment">% computing f(critical_cost_value,x). The value of g(x) is not</span>
0159 <span class="comment">% necessary.</span>
0160 <span class="comment">%</span></pre></div>
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<title>Description of beta_rnd</title>
<meta name="keywords" content="beta_rnd">
<meta name="description" content="PURPOSE: random draws from the beta(a,b) distribution">
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<h1>beta_rnd
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>PURPOSE: random draws from the beta(a,b) distribution</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function rnd = beta_rnd (n, a, b) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> PURPOSE: random draws from the beta(a,b) distribution
--------------------------------------------------------------
USAGE: rnd = beta_rnd(n,a,b)
where: n = size of the vector of draws
a = beta distribution parameter, a = scalar
b = beta distribution parameter b = scalar
NOTE: mean = a/(a+b), variance = ab/((a+b)*(a+b)*(a+b+1))
--------------------------------------------------------------
RETURNS: n-vector of random draws from the beta(a,b) distribution
--------------------------------------------------------------
SEE ALSO: beta_d, beta_pdf, beta_inv, <a href="beta_rnd.html" class="code" title="function rnd = beta_rnd (n, a, b)">beta_rnd</a>
--------------------------------------------------------------</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="gamm_rnd.html" class="code" title="function gb = gamm_rnd(nrow,m,k)">gamm_rnd</a> PURPOSE: a matrix of random draws from the gamma distribution</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="rndprior.html" class="code" title="function y = rndprior(bayestopt_)">rndprior</a> </li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function rnd = beta_rnd (n, a, b)</a>
0002 <span class="comment">% PURPOSE: random draws from the beta(a,b) distribution</span>
0003 <span class="comment">%--------------------------------------------------------------</span>
0004 <span class="comment">% USAGE: rnd = beta_rnd(n,a,b)</span>
0005 <span class="comment">% where: n = size of the vector of draws</span>
0006 <span class="comment">% a = beta distribution parameter, a = scalar</span>
0007 <span class="comment">% b = beta distribution parameter b = scalar</span>
0008 <span class="comment">% NOTE: mean = a/(a+b), variance = ab/((a+b)*(a+b)*(a+b+1))</span>
0009 <span class="comment">%--------------------------------------------------------------</span>
0010 <span class="comment">% RETURNS: n-vector of random draws from the beta(a,b) distribution</span>
0011 <span class="comment">%--------------------------------------------------------------</span>
0012 <span class="comment">% SEE ALSO: beta_d, beta_pdf, beta_inv, beta_rnd</span>
0013 <span class="comment">%--------------------------------------------------------------</span>
0014
0015 <span class="comment">% written by:</span>
0016 <span class="comment">% James P. LeSage, Dept of Economics</span>
0017 <span class="comment">% University of Toledo</span>
0018 <span class="comment">% 2801 W. Bancroft St,</span>
0019 <span class="comment">% Toledo, OH 43606</span>
0020 <span class="comment">% jlesage@spatial-econometrics.com</span>
0021
0022
0023 <span class="keyword">if</span> (nargin ~= 3)
0024 error(<span class="string">'Wrong # of arguments to beta_rnd'</span>);
0025 <span class="keyword">end</span>;
0026
0027 <span class="keyword">if</span> any(any((a&lt;=0)|(b&lt;=0)))
0028 error(<span class="string">'Parameter a or b is nonpositive'</span>)
0029 <span class="keyword">end</span>
0030
0031 a1n = <a href="gamm_rnd.html" class="code" title="function gb = gamm_rnd(nrow,m,k)">gamm_rnd</a>(n,a,1);
0032 a1d = <a href="gamm_rnd.html" class="code" title="function gb = gamm_rnd(nrow,m,k)">gamm_rnd</a>(n,b,1);
0033 rnd = a1n./(a1n+a1d);
0034</pre></div>
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<title>Description of bfgsi</title>
<meta name="keywords" content="bfgsi">
<meta name="description" content="H = bfgsi(H0,dg,dx)">
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<h1>bfgsi
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>H = bfgsi(H0,dg,dx)</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function H = bfgsi(H0,dg,dx) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> H = bfgsi(H0,dg,dx)
dg is previous change in gradient; dx is previous change in x;
6/8/93 version that updates inverse hessian instead of hessian
itself.
Copyright by Christopher Sims 1996. This material may be freely
reproduced and modified.</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="csminwel.html" class="code" title="function [fh,xh,gh,H,itct,fcount,retcodeh] = csminwel(fcn,x0,H0,grad,crit,nit,varargin)">csminwel</a> [fhat,xhat,ghat,Hhat,itct,fcount,retcodehat] = csminwel(fcn,x0,H0,grad,crit,nit,varargin)</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function H = bfgsi(H0,dg,dx)</a>
0002 <span class="comment">% H = bfgsi(H0,dg,dx)</span>
0003 <span class="comment">% dg is previous change in gradient; dx is previous change in x;</span>
0004 <span class="comment">% 6/8/93 version that updates inverse hessian instead of hessian</span>
0005 <span class="comment">% itself.</span>
0006 <span class="comment">% Copyright by Christopher Sims 1996. This material may be freely</span>
0007 <span class="comment">% reproduced and modified.</span>
0008 <span class="keyword">if</span> size(dg,2)&gt;1
0009 dg=dg';
0010 <span class="keyword">end</span>
0011 <span class="keyword">if</span> size(dx,2)&gt;1
0012 dx=dx';
0013 <span class="keyword">end</span>
0014 Hdg = H0*dg;
0015 dgdx = dg'*dx;
0016 <span class="keyword">if</span> (abs(dgdx) &gt;1e-12)
0017 H = H0 + (1+(dg'*Hdg)/dgdx)*(dx*dx')/dgdx - (dx*Hdg'+Hdg*dx')/dgdx;
0018 <span class="keyword">else</span>
0019 disp(<span class="string">'bfgs update failed.'</span>)
0020 disp([<span class="string">'|dg| = '</span> num2str(sqrt(dg'*dg)) <span class="string">'|dx| = '</span> num2str(sqrt(dx'*dx))]);
0021 disp([<span class="string">'dg''*dx = '</span> num2str(dgdx)])
0022 disp([<span class="string">'|H*dg| = '</span> num2str(Hdg'*Hdg)])
0023 H=H0;
0024 <span class="keyword">end</span>
0025 save H.dat H</pre></div>
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<title>Description of bicgstab</title>
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<h1>bicgstab
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong></strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function [x,status]=bicgstab(func,b,x,tole,kmax,varargin) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"></pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="calib_obj.html" class="code" title="function f=calib_obj(M_.Sigma_e,A,ghu1,ghx,ghu,targets,var_weights,iy,nar)">calib_obj</a> targets and iy order: 1) variances 2) correlations</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function [x,status]=bicgstab(func,b,x,tole,kmax,varargin)</a>
0002 status = 0;
0003 r=b-feval(func,x,varargin{:});
0004 rh_0 = r;
0005 rh = r;
0006 rho_0 = 1;
0007 alpha = 1;
0008 w = 1;
0009 v = 0;
0010 p = 0;
0011 k = 0;
0012 rho_1 = rh_0'*r;
0013 tolr = tole*norm(b);
0014
0015 <span class="keyword">while</span> norm(r) &gt; tolr &amp; k &lt; kmax
0016 k = k+1;
0017 beta = (rho_1/rho_0)*(alpha/w);
0018 p = r+beta*(p-w*v);
0019 v = feval(func,p,varargin{:});
0020 alpha = rho_1/(rh_0'*v);
0021 r = r-alpha*v;
0022 t = feval(func,r,varargin{:});
0023 w = (t'*r)/(t'*t);
0024 rho_0 = rho_1;
0025 rho_1 = -w*(rh_0'*t);
0026 x = x+alpha*p+w*r;
0027 r = r-w*t;
0028 <span class="keyword">end</span>
0029 <span class="keyword">if</span> k == kmax
0030 status = 1;
0031 warning(sprintf(<span class="string">'BICSTABN didn''t converge after %d iterations: norm(r) = %g'</span>,kmax,norm(r)));
0032 <span class="keyword">end</span></pre></div>
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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"
"http://www.w3.org/TR/REC-html40/loose.dtd">
<html>
<head>
<title>Description of bksup</title>
<meta name="keywords" content="bksup">
<meta name="description" content="Copyright (C) 2001 Michel Juillard">
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
<meta name="generator" content="m2html &copy; 2003 Guillaume Flandin">
<meta name="robots" content="index, follow">
<link type="text/css" rel="stylesheet" href="../m2html.css">
</head>
<body>
<a name="_top"></a>
<div><a href="../index.html">Home</a> &gt; <a href="index.html">.</a> &gt; bksup.m</div>
<!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png">&nbsp;Master index</a></td>
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<h1>bksup
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>Copyright (C) 2001 Michel Juillard</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function d1 = bksupk(ny,fid,jcf,icc1) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> Copyright (C) 2001 Michel Juillard</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="irf.html" class="code" title="function y = irf(dr, e1, long, drop, replic, iorder)">irf</a> </li><li><a href="selif.html" class="code" title="function x = selif(a,b)">selif</a> Copyright (C) 2001 Michel Juillard</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <span class="comment">% Copyright (C) 2001 Michel Juillard</span>
0002 <span class="comment">%</span>
0003 <a name="_sub0" href="#_subfunctions" class="code">function d1 = bksupk(ny,fid,jcf,icc1)</a>
0004
0005 <span class="keyword">global</span> M_ options_ c ncc
0006
0007 icf = [1:jcf-1] ;
0008 ir = [(options_.periods-1)*ny+1:ny*options_.periods] ;
0009 <a href="irf.html" class="code" title="function y = irf(dr, e1, long, drop, replic, iorder)">irf</a> = icc1+(options_.periods-1)*ny ;
0010 d1 = zeros(options_.periods*ny,1) ;
0011
0012 ofs = (((options_.periods-1)*ny+1)-1)*ncc*8 ;
0013 junk = fseek(fid,ofs,-1) ;
0014 c = fread(fid,[ncc,ny],<span class="string">'float64'</span>) ;
0015 c = c' ;
0016
0017 d1(ir) = c(:,jcf) ;
0018 ir = ir-ny ;
0019
0020 i = 2 ;
0021
0022 <span class="keyword">while</span> i &lt;= M_.maximum_lead | i &lt;= options_.periods
0023 irf1 = <a href="selif.html" class="code" title="function x = selif(a,b)">selif</a>(<a href="irf.html" class="code" title="function y = irf(dr, e1, long, drop, replic, iorder)">irf</a>,<a href="irf.html" class="code" title="function y = irf(dr, e1, long, drop, replic, iorder)">irf</a>&lt;options_.periods*ny) ;
0024
0025 ofs = (((options_.periods-i)*ny+1)-1)*ncc*8 ;
0026 junk = fseek(fid,ofs,-1) ;
0027 c = fread(fid,[ncc,ny],<span class="string">'float64'</span>) ;
0028 c = c' ;
0029
0030 d1(ir) = c(:,jcf) - c(:,1:size(irf1,1))*d1(irf1) ;
0031 ir = ir - ny ;
0032 <a href="irf.html" class="code" title="function y = irf(dr, e1, long, drop, replic, iorder)">irf</a> = <a href="irf.html" class="code" title="function y = irf(dr, e1, long, drop, replic, iorder)">irf</a> - ny ;
0033 i = i + 1 ;
0034 <span class="keyword">end</span>
0035
0036 <span class="keyword">while</span> i &lt;= options_.periods
0037
0038 ofs = (((options_.periods-i)*ny+1)-1)*ncc*8 ;
0039 junk = fseek(fid,ofs,-1) ;
0040 c = fread(fid,[ncc,ny],<span class="string">'float64'</span>) ;
0041 c = c' ;
0042
0043 d1(ir) = c(:,jcf)-c(:,icf)*d1(<a href="irf.html" class="code" title="function y = irf(dr, e1, long, drop, replic, iorder)">irf</a>) ;
0044 ir = ir-ny ;
0045 <a href="irf.html" class="code" title="function y = irf(dr, e1, long, drop, replic, iorder)">irf</a> = <a href="irf.html" class="code" title="function y = irf(dr, e1, long, drop, replic, iorder)">irf</a>-ny ;
0046 i = i+1;
0047 <span class="keyword">end</span>
0048
0049 <span class="keyword">return</span> ;</pre></div>
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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"
"http://www.w3.org/TR/REC-html40/loose.dtd">
<html>
<head>
<title>Description of bksup1</title>
<meta name="keywords" content="bksup1">
<meta name="description" content="Copyright (C) 2001 Michel Juillard">
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
<meta name="generator" content="m2html &copy; 2003 Guillaume Flandin">
<meta name="robots" content="index, follow">
<link type="text/css" rel="stylesheet" href="../m2html.css">
</head>
<body>
<a name="_top"></a>
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<h1>bksup1
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>Copyright (C) 2001 Michel Juillard</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function d = bksup1(ny,jcf) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> Copyright (C) 2001 Michel Juillard</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="irf.html" class="code" title="function y = irf(dr, e1, long, drop, replic, iorder)">irf</a> </li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="sim1.html" class="code" title="function sim1">sim1</a> Copyright (C) 2001 Michel Juillard</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <span class="comment">% Copyright (C) 2001 Michel Juillard</span>
0002 <span class="comment">%</span>
0003 <a name="_sub0" href="#_subfunctions" class="code">function d = bksup1(ny,jcf)</a>
0004
0005 <span class="keyword">global</span> options_ iyf c
0006
0007 ir = [(options_.periods-2)*ny+1:ny+(options_.periods-2)*ny] ;
0008 <a href="irf.html" class="code" title="function y = irf(dr, e1, long, drop, replic, iorder)">irf</a> = iyf+(options_.periods-1)*ny ;
0009 icf = [1:size(iyf,2)] ;
0010
0011 <span class="keyword">for</span> i = 2:options_.periods
0012 c(ir,jcf) = c(ir,jcf)-c(ir,icf)*c(<a href="irf.html" class="code" title="function y = irf(dr, e1, long, drop, replic, iorder)">irf</a>,jcf) ;
0013 ir = ir-ny ;
0014 <a href="irf.html" class="code" title="function y = irf(dr, e1, long, drop, replic, iorder)">irf</a> = <a href="irf.html" class="code" title="function y = irf(dr, e1, long, drop, replic, iorder)">irf</a>-ny ;
0015 <span class="keyword">end</span>
0016
0017 d = c(:,jcf) ;
0018
0019 <span class="keyword">return</span> ;</pre></div>
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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"
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<html>
<head>
<title>Description of bksupk</title>
<meta name="keywords" content="bksupk">
<meta name="description" content="">
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
<meta name="generator" content="m2html &copy; 2003 Guillaume Flandin">
<meta name="robots" content="index, follow">
<link type="text/css" rel="stylesheet" href="../m2html.css">
</head>
<body>
<a name="_top"></a>
<div><a href="../index.html">Home</a> &gt; <a href="index.html">.</a> &gt; bksupk.m</div>
<!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png">&nbsp;Master index</a></td>
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<h1>bksupk
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong></strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function d1 = bksupk(ny,fid,jcf,icc1) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"></pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="irf.html" class="code" title="function y = irf(dr, e1, long, drop, replic, iorder)">irf</a> </li><li><a href="selif.html" class="code" title="function x = selif(a,b)">selif</a> Copyright (C) 2001 Michel Juillard</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="simk.html" class="code" title="function simk">simk</a> Copyright (C) 2001 Michel Juillard</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function d1 = bksupk(ny,fid,jcf,icc1)</a>
0002
0003 <span class="keyword">global</span> M_ options_
0004
0005 icf = [1:jcf-1] ;
0006 ir = [(options_.periods-1)*ny+1:ny*options_.periods] ;
0007 <a href="irf.html" class="code" title="function y = irf(dr, e1, long, drop, replic, iorder)">irf</a> = icc1+(options_.periods-1)*ny ;
0008 d1 = zeros(options_.periods*ny,1) ;
0009
0010 ofs = (((options_.periods-1)*ny+1)-1)*jcf*8 ;
0011 junk = fseek(fid,ofs,-1) ;
0012 c = fread(fid,[jcf,ny],<span class="string">'float64'</span>) ;
0013
0014 d1(ir) = c(:,jcf) ;
0015 ir = ir-ny ;
0016
0017 i = 2 ;
0018
0019 <span class="keyword">while</span> i &lt;= M_.maximum_lead | i &lt;= options_.periods
0020 irf1 = <a href="selif.html" class="code" title="function x = selif(a,b)">selif</a>(<a href="irf.html" class="code" title="function y = irf(dr, e1, long, drop, replic, iorder)">irf</a>,<a href="irf.html" class="code" title="function y = irf(dr, e1, long, drop, replic, iorder)">irf</a>&lt;=options_.periods*ny) ;
0021
0022 ofs = (((options_.periods-i)*ny+1)-1)*jcf*8 ;
0023 junk = fseek(fid,ofs,-1) ;
0024 c = fread(fid,[jcf,ny],<span class="string">'float64'</span>) ;
0025
0026 d1(ir) = c(:,jcf) - c(:,1:size(irf1,1))*d1(irf1) ;
0027 ir = ir - ny ;
0028 <a href="irf.html" class="code" title="function y = irf(dr, e1, long, drop, replic, iorder)">irf</a> = <a href="irf.html" class="code" title="function y = irf(dr, e1, long, drop, replic, iorder)">irf</a> - ny ;
0029 i = i + 1 ;
0030 <span class="keyword">end</span>
0031
0032 <span class="keyword">while</span> i &lt;= options_.periods
0033
0034 ofs = (((options_.periods-i)*ny+1)-1)*jcf*8 ;
0035 junk = fseek(fid,ofs,-1) ;
0036 c = fread(fid,[jcf,ny],<span class="string">'float64'</span>) ;
0037
0038 d1(ir) = c(:,jcf)-c(:,icf)*d1(<a href="irf.html" class="code" title="function y = irf(dr, e1, long, drop, replic, iorder)">irf</a>) ;
0039 ir = ir-ny ;
0040 <a href="irf.html" class="code" title="function y = irf(dr, e1, long, drop, replic, iorder)">irf</a> = <a href="irf.html" class="code" title="function y = irf(dr, e1, long, drop, replic, iorder)">irf</a>-ny ;
0041 i = i+1;
0042 <span class="keyword">end</span>
0043
0044 <span class="keyword">return</span> ;</pre></div>
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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"
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<html>
<head>
<title>Description of bseastr</title>
<meta name="keywords" content="bseastr">
<meta name="description" content="Copyright (C) 2001 Michel Juillard">
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
<meta name="generator" content="m2html &copy; 2003 Guillaume Flandin">
<meta name="robots" content="index, follow">
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<h1>bseastr
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>Copyright (C) 2001 Michel Juillard</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function x = bseastr(s1,s2) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> Copyright (C) 2001 Michel Juillard</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="dcompare.html" class="code" title="function dcompare(s1)">dcompare</a> Copyright (C) 2001 Michel Juillard</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <span class="comment">% Copyright (C) 2001 Michel Juillard</span>
0002 <span class="comment">%</span>
0003 <a name="_sub0" href="#_subfunctions" class="code">function x = bseastr(s1,s2)</a>
0004
0005 m = size(s1,1) ;
0006 x = zeros(m,1) ;
0007 s1=upper(deblank(s1));
0008 s2=upper(deblank(s2));
0009
0010 <span class="keyword">for</span> im = 1:m
0011 key = s1(im,:) ;
0012 h = size(s2,1) ;
0013 l = 1 ;
0014 <span class="keyword">while</span> l &lt;= h
0015 mid = round((h+l)/2) ;
0016 temp = s2(mid,:) ;
0017 <span class="keyword">if</span> ~ strcmp(key,temp)
0018 <span class="keyword">for</span> i = 1:min(length(key),length(temp))
0019 <span class="keyword">if</span> temp(i) &gt; key(i)
0020 h = mid - 1 ;
0021 <span class="keyword">break</span>
0022 <span class="keyword">else</span>
0023 l = mid + 1 ;
0024 <span class="keyword">break</span>
0025 <span class="keyword">end</span>
0026 <span class="keyword">end</span>
0027 <span class="keyword">else</span>
0028 x(im) = mid ;
0029 <span class="keyword">break</span>
0030 <span class="keyword">end</span>
0031 <span class="keyword">end</span>
0032 <span class="keyword">end</span>
0033</pre></div>
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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"
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<html>
<head>
<title>Description of calib</title>
<meta name="keywords" content="calib">
<meta name="description" content="">
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<meta name="generator" content="m2html &copy; 2003 Guillaume Flandin">
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<h1>calib
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong></strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function M_.Sigma_e = calib(var_indices,targets,var_weights,nar,cova,M_.Sigma_e) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"></pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="calib_obj.html" class="code" title="function f=calib_obj(M_.Sigma_e,A,ghu1,ghx,ghu,targets,var_weights,iy,nar)">calib_obj</a> targets and iy order: 1) variances 2) correlations</li><li><a href="calib_obj2.html" class="code" title="function objective=calib_obj2(M_.Sigma_e,A,ghu1,ghx,ghu,targets,var_weights,iy,nar)">calib_obj2</a> targets and iy order: 1) variances 2) correlations</li><li><a href="indnv.html" class="code" title="function a=indnv(x,y)">indnv</a> Copyright (C) 2001 Michel Juillard</li><li><a href="resol.html" class="code" title="function [dr,info]=resol(ys,check_flag)">resol</a> Copyright (C) 2001 Michel Juillard</li><li><a href="union.html" class="code" title="function x = union(a,b)">union</a> </li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function M_.Sigma_e = calib(var_indices,targets,var_weights,nar,cova,M_.Sigma_e)</a>
0002 <span class="keyword">global</span> oo_ M_ vx
0003
0004 ncstr = 0;
0005 ni = size(var_indices,1);
0006 <span class="keyword">for</span> i=1:nar+3
0007 ncstr = ncstr + size(var_indices{i},1);
0008 <span class="keyword">end</span>
0009 <span class="keyword">if</span> cova
0010 <span class="keyword">if</span> ncstr &lt; M_.exo_nbr*(M_.exo_nbr+1)/2
0011 error([<span class="string">'number of preset variances is smaller than number of shock'</span> <span class="keyword">...</span>
0012 <span class="string">' variances and covariances to be estimated !'</span>])
0013 <span class="keyword">end</span>
0014 <span class="keyword">else</span>
0015 <span class="keyword">if</span> ncstr &lt; M_.exo_nbr
0016 error([<span class="string">'number of preset variances is smaller than number of shock'</span> <span class="keyword">...</span>
0017 <span class="string">' variances to be estimated !'</span>])
0018 <span class="keyword">end</span>
0019 <span class="keyword">end</span>
0020
0021 <span class="comment">% computes approximate solution at order 1</span>
0022 dr = <a href="resol.html" class="code" title="function [dr,info]=resol(ys,check_flag)">resol</a>(oo_.steady_state,0,0,1);
0023
0024 ghx = dr.ghx;
0025 ghu = dr.ghu;
0026 npred = dr.npred;
0027 nstatic = dr.nstatic;
0028 kstate = dr.kstate;
0029 order = dr.order_var;
0030 iv(order) = [1:M_.endo_nbr];
0031 iv = iv';
0032 nx = size(ghx,2);
0033
0034 ikx = [nstatic+1:nstatic+npred];
0035
0036 A = zeros(nx,nx);
0037 A(1:npred,:)=ghx(ikx,:);
0038 offset_r = npred;
0039 offset_c = 0;
0040 i0 = find(kstate(:,2) == M_.maximum_lag+1);
0041 n0 = size(i0,1);
0042 <span class="keyword">for</span> i=M_.maximum_lag:-1:2
0043 i1 = find(kstate(:,2) == i);
0044 n1 = size(i1,1);
0045 j = zeros(n1,1);
0046 <span class="keyword">for</span> j1 = 1:n1
0047 j(j1) = find(kstate(i0,1)==kstate(i1(j1),1));
0048 <span class="keyword">end</span>
0049 A(offset_r+1:offset_r+n1,offset_c+j)=eye(n1);
0050 offset_r = offset_r + n1;
0051 offset_c = offset_c + n0;
0052 i0 = i1;
0053 n0 = n1;
0054 <span class="keyword">end</span>
0055 ghu1 = [ghu(ikx,:);zeros(nx-npred,M_.exo_nbr)];
0056 <span class="comment">% IA = speye(nx*nx)-kron(A,A);</span>
0057 <span class="comment">% kron_ghu = kron(ghu1,ghu1);</span>
0058
0059 <span class="comment">% vx1 such that vec(sigma_x) = vx1 * vec(M_.Sigma_e) (predetermined vars)</span>
0060 vx1 = [];
0061 <span class="comment">% vx1 = IA\kron_ghu;</span>
0062 IA = [];
0063 kron_ghu = [];
0064
0065 <span class="comment">% computes required variables and indices among required variables</span>
0066 iiy = [];
0067 <span class="keyword">for</span> i=1:nar+3
0068 <span class="keyword">if</span> i ~= 3 &amp; ~isempty(var_indices{i})
0069 iiy = <a href="union.html" class="code" title="function x = union(a,b)">union</a>(iiy, iv(var_indices{i}(:,1)));
0070 <span class="keyword">end</span>
0071 <span class="keyword">end</span>
0072 <span class="keyword">if</span> ~isempty(var_indices{2})
0073 iiy = <a href="union.html" class="code" title="function x = union(a,b)">union</a>(iiy, iv(var_indices{2}(:,2)));
0074 <span class="keyword">end</span>
0075 ny = size(iiy,1);
0076
0077 <span class="keyword">for</span> i=1:nar+3
0078 <span class="keyword">if</span> i ~= 3 &amp; ~isempty(var_indices{i})
0079 var_indices{i}(:,1) = <a href="indnv.html" class="code" title="function a=indnv(x,y)">indnv</a>(iv(var_indices{i}(:,1)),iiy);
0080 <span class="keyword">end</span>
0081 <span class="keyword">if</span> i ~= 2 &amp; i ~= 3 &amp; ~isempty(var_indices{i})
0082 var_indices{i} = sub2ind([ny ny],var_indices{i},var_indices{i});
0083 <span class="keyword">end</span>
0084 <span class="keyword">end</span>
0085 <span class="keyword">if</span> ~isempty(var_indices{2})
0086 var_indices{2}(:,2) = <a href="indnv.html" class="code" title="function a=indnv(x,y)">indnv</a>(iv(var_indices{2}(:,2)),iiy);
0087 var_indices{2} = sub2ind([ny ny],var_indices{2}(:,1),var_indices{2}(:,2));
0088 <span class="keyword">end</span>
0089 <span class="keyword">if</span> ~isempty(var_indices{3})
0090 var_indices{3} = sub2ind([M_.exo_nbr M_.exo_nbr],var_indices{3}(:,1),var_indices{3}(:,2));
0091 <span class="keyword">end</span>
0092 <span class="keyword">if</span> isempty(M_.Sigma_e)
0093 M_.Sigma_e = 0.01*eye(M_.exo_nbr);
0094 b = 0.1*ghu1*ghu1';
0095 <span class="keyword">else</span>
0096 b = ghu1*M_.Sigma_e*ghu1';
0097 M_.Sigma_e = chol(M_.Sigma_e+1e-14*eye(M_.exo_nbr));
0098 <span class="keyword">end</span>
0099 options=optimset(<span class="string">'LargeScale'</span>,<span class="string">'on'</span>,<span class="string">'MaxFunEvals'</span>,20000*ny,<span class="string">'TolX'</span>,1e-4, <span class="keyword">...</span>
0100 <span class="string">'TolFun'</span>,1e-4,<span class="string">'Display'</span>,<span class="string">'Iter'</span>,<span class="string">'MaxIter'</span>,10000);
0101 <span class="comment">% [M_.Sigma_e,f]=fminunc(@calib_obj,M_.Sigma_e,options,A,ghu1,ghx(iiy,:),ghu(iiy,:),targets,var_weights,var_indices,nar);</span>
0102 [M_.Sigma_e,f]=fmincon(@<a href="calib_obj.html" class="code" title="function f=calib_obj(M_.Sigma_e,A,ghu1,ghx,ghu,targets,var_weights,iy,nar)">calib_obj</a>,diag(M_.Sigma_e).^2,-eye(M_.exo_nbr),zeros(M_.exo_nbr,1),[],[],[],[],[],options,A,ghu1,ghx(iiy,:),ghu(iiy,:),targets,var_weights,var_indices,nar);
0103 M_.Sigma_e = diag(M_.Sigma_e);
0104
0105 objective = <a href="calib_obj2.html" class="code" title="function objective=calib_obj2(M_.Sigma_e,A,ghu1,ghx,ghu,targets,var_weights,iy,nar)">calib_obj2</a>(diag(M_.Sigma_e),A,ghu1,ghx(iiy,:),ghu(iiy,:),targets,var_weights,var_indices,nar);
0106 disp(<span class="string">'CALIBRATION'</span>)
0107 disp(<span class="string">''</span>)
0108 <span class="keyword">if</span> ~isempty(var_indices{1})
0109 disp(sprintf(<span class="string">'%16s %14s %14s %14s %14s'</span>,<span class="string">'Std. Dev'</span>,<span class="string">'Target'</span>,<span class="string">'Obtained'</span>,<span class="string">'Diff'</span>));
0110 str = <span class="string">' '</span>;
0111 <span class="keyword">for</span> i=1:size(var_indices{1},1)
0112 [i1,i2] = ind2sub([ny ny],var_indices{1}(i));
0113 str = sprintf(<span class="string">'%16s: %14.2f %14.2f %14.2f'</span>,M_.endo_names(order(iiy(i1)),:),targets{1}(i),objective{1}(i),objective{1}(i)-targets{1}(i));
0114 disp(str);
0115 <span class="keyword">end</span>
0116 <span class="keyword">end</span>
0117 <span class="keyword">if</span> ~isempty(var_indices{2})
0118 disp(sprintf(<span class="string">'%32s %14s %14s'</span>,<span class="string">'Correlations'</span>,<span class="string">'Target'</span>,<span class="string">'Obtained'</span>,<span class="string">'Diff'</span>));
0119 str = <span class="string">' '</span>;
0120 <span class="keyword">for</span> i=1:size(var_indices{2},1)
0121 [i1,i2]=ind2sub([ny ny],var_indices{2}(i));
0122 str = sprintf(<span class="string">'%16s,%16s: %14.2f %14.2f %14.2f'</span>,M_.endo_names(order(iiy(i1)),:), <span class="keyword">...</span>
0123 M_.endo_names(order(iiy(i2)),:),targets{2}(i),objective{2}(i),objective{2}(i)-targets{2}(i));
0124 disp(str);
0125 <span class="keyword">end</span>
0126 <span class="keyword">end</span>
0127 <span class="keyword">if</span> ~isempty(var_indices{3})
0128 disp(sprintf(<span class="string">'%32s %16s %16s'</span>,<span class="string">'Constrained shocks (co)variances'</span>,<span class="string">'Target'</span>,<span class="string">'Obtained'</span>));
0129 str = <span class="string">' '</span>;
0130 <span class="keyword">for</span> i=1:size(var_indices{3},1)
0131 [i1,i2]=ind2sub([M_.exo_nbr M_.exo_nbr],var_indices{3}(i));
0132 <span class="keyword">if</span> i1 == i2
0133 str = sprintf(<span class="string">'%32s: %16.4f %16.4f'</span>,M_.exo_name(order(i1),:), <span class="keyword">...</span>
0134 targets{3}(i),objective{3}(i));
0135 <span class="keyword">else</span>
0136 str = sprintf(<span class="string">'%16s,%16s: %16.4f %16.4f'</span>,M_.exo_name(order(i1),:), <span class="keyword">...</span>
0137 M_.exo_name(order(i2), :),targets{3}(i),objective{3}(i));
0138 <span class="keyword">end</span>
0139 disp(str);
0140 <span class="keyword">end</span>
0141 <span class="keyword">end</span>
0142 flag = 1;
0143 <span class="keyword">for</span> j=4:nar+3
0144 <span class="keyword">if</span> ~isempty(var_indices{j})
0145 <span class="keyword">if</span> flag
0146 disp(sprintf(<span class="string">'%16s %16s %16s'</span>,<span class="string">'Autocorrelations'</span>,<span class="string">'Target'</span>,<span class="string">'Obtained'</span>));
0147 str = <span class="string">' '</span>;
0148 flag = 0;
0149 <span class="keyword">end</span>
0150 <span class="keyword">for</span> i=1:size(var_indices{j},1)
0151 [i1,i2] = ind2sub([ny ny],var_indices{j}(i));
0152 str = sprintf(<span class="string">'%16s(%d): %16.4f %16.4f'</span>,M_.endo_names(order(iiy(i1)),:), <span class="keyword">...</span>
0153 j-3,targets{j}(i),objective{j}(i));
0154 disp(str);
0155 <span class="keyword">end</span>
0156 <span class="keyword">end</span>
0157 <span class="keyword">end</span>
0158
0159 disp(<span class="string">''</span>);
0160 disp(<span class="string">'Calibrated variances'</span>)
0161 str = <span class="string">' '</span>;
0162 <span class="keyword">for</span> i=1:M_.exo_nbr
0163 str = [str sprintf(<span class="string">'%16s'</span>,M_.exo_name(i,:))];
0164 <span class="keyword">end</span>
0165 disp(str);
0166 disp(<span class="string">''</span>);
0167 str = <span class="string">' '</span>;
0168 <span class="keyword">for</span> i=1:M_.exo_nbr
0169 str = [str sprintf(<span class="string">'%16f'</span>,M_.Sigma_e(i,i))];
0170 <span class="keyword">end</span>
0171 disp(str);
0172
0173
0174
0175 <span class="comment">% 10/9/02 MJ</span></pre></div>
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<html>
<head>
<title>Description of calib_obj</title>
<meta name="keywords" content="calib_obj">
<meta name="description" content="targets and iy order: 1) variances 2) correlations">
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
<meta name="generator" content="m2html &copy; 2003 Guillaume Flandin">
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<a name="_top"></a>
<div><a href="../index.html">Home</a> &gt; <a href="index.html">.</a> &gt; calib_obj.m</div>
<!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png">&nbsp;Master index</a></td>
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<h1>calib_obj
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>targets and iy order: 1) variances 2) correlations</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function f=calib_obj(M_.Sigma_e,A,ghu1,ghx,ghu,targets,var_weights,iy,nar) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> targets and iy order: 1) variances 2) correlations
3) constraints on M_.Sigma_e itself 4) autocorrelations</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="bicgstab.html" class="code" title="function [x,status]=bicgstab(func,b,x,tole,kmax,varargin)">bicgstab</a> </li><li><a href="f_var.html" class="code" title="function b=f_var(x,a,nx)">f_var</a> </li><li><a href="lyapunov_symm.html" class="code" title="function [x,ns_var]=lyapunov_symm(a,b)">lyapunov_symm</a> solves x-a*x*a'=b for b (and then x) symmetrical</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="calib.html" class="code" title="function M_.Sigma_e = calib(var_indices,targets,var_weights,nar,cova,M_.Sigma_e)">calib</a> </li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <span class="comment">% targets and iy order: 1) variances 2) correlations</span>
0002 <span class="comment">% 3) constraints on M_.Sigma_e itself 4) autocorrelations</span>
0003 <a name="_sub0" href="#_subfunctions" class="code">function f=calib_obj(M_.Sigma_e,A,ghu1,ghx,ghu,targets,var_weights,iy,nar)</a>
0004 <span class="keyword">global</span> vx fold
0005
0006 oo_.gamma_y = cell(nar+1,1);
0007 <span class="comment">% M_.Sigma_e = M_.Sigma_e'*M_.Sigma_e;</span>
0008 M_.Sigma_e=diag(M_.Sigma_e);
0009 nx = size(ghx,2);
0010 b=ghu1*M_.Sigma_e*ghu1';
0011 vx = [];
0012 <span class="keyword">if</span> isempty(vx)
0013 vx = <a href="lyapunov_symm.html" class="code" title="function [x,ns_var]=lyapunov_symm(a,b)">lyapunov_symm</a>(A,b);
0014 <span class="keyword">else</span>
0015 [vx,status] = <a href="bicgstab.html" class="code" title="function [x,status]=bicgstab(func,b,x,tole,kmax,varargin)">bicgstab</a>(@<a href="f_var.html" class="code" title="function b=f_var(x,a,nx)">f_var</a>,b(:),vx(:),1e-8,50,A,nx);
0016 <span class="keyword">if</span> status
0017 vx = <a href="lyapunov_symm.html" class="code" title="function [x,ns_var]=lyapunov_symm(a,b)">lyapunov_symm</a>(A,b);
0018 <span class="keyword">else</span>
0019 vx=reshape(vx,nx,nx);
0020 <span class="keyword">end</span>
0021 <span class="keyword">end</span>
0022 oo_.gamma_y{1} = ghx*vx*ghx'+ ghu*M_.Sigma_e*ghu';
0023 f = 0;
0024 <span class="keyword">if</span> ~isempty(targets{1})
0025 e = targets{1}-sqrt(oo_.gamma_y{1}(iy{1}));
0026 f = e'*(var_weights{1}.*e);
0027 <span class="keyword">end</span>
0028
0029 sy = sqrt(diag(oo_.gamma_y{1}));
0030 sy = sy *sy';
0031 <span class="keyword">if</span> ~isempty(targets{2})
0032 e = targets{2}-oo_.gamma_y{1}(iy{2})./(sy(iy{2})+1e-10);
0033 f = f+e'*(var_weights{2}.*e);
0034 <span class="keyword">end</span>
0035
0036 <span class="keyword">if</span> ~isempty(targets{3})
0037 e = targets{3}-sqrt(M_.Sigma_e(iy{3}));
0038 f = f+e'*(var_weights{3}.*e);
0039 <span class="keyword">end</span>
0040
0041 <span class="comment">% autocorrelations</span>
0042 <span class="keyword">if</span> nar &gt; 0
0043 vxy = (A*vx*ghx'+ghu1*M_.Sigma_e*ghu');
0044
0045 oo_.gamma_y{2} = ghx*vxy./(sy+1e-10);
0046 <span class="keyword">if</span> ~isempty(targets{4})
0047 e = targets{4}-oo_.gamma_y{2}(iy{4});
0048 f = f+e'*(var_weights{4}.*e);
0049 <span class="keyword">end</span>
0050
0051 <span class="keyword">for</span> i=2:nar
0052 vxy = A*vxy;
0053 oo_.gamma_y{i+1} = ghx*vxy./(sy+1e-10);
0054 <span class="keyword">if</span> ~isempty(targets{i+3})
0055 e = targets{i+3}-oo_.gamma_y{i+1}(iy{i+3});
0056 f = f+e'*(var_weights{i+3}.*e);
0057 <span class="keyword">end</span>
0058 <span class="keyword">end</span>
0059 <span class="keyword">end</span>
0060 <span class="keyword">if</span> isempty(fold) | f &lt; 2*fold
0061 fold = f;
0062 vxold = vx;
0063 <span class="keyword">end</span>
0064 <span class="comment">% 11/04/02 MJ generalized for correlations, autocorrelations and</span>
0065 <span class="comment">% constraints on M_.Sigma_e</span>
0066 <span class="comment">% 01/25/03 MJ targets std. dev. instead of variances</span>
0067</pre></div>
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<title>Description of calib_obj2</title>
<meta name="keywords" content="calib_obj2">
<meta name="description" content="targets and iy order: 1) variances 2) correlations">
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
<meta name="generator" content="m2html &copy; 2003 Guillaume Flandin">
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<!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png">&nbsp;Master index</a></td>
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<h1>calib_obj2
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>targets and iy order: 1) variances 2) correlations</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function objective=calib_obj2(M_.Sigma_e,A,ghu1,ghx,ghu,targets,var_weights,iy,nar) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> targets and iy order: 1) variances 2) correlations
3) constraints on M_.Sigma_e itself 4) autocorrelations</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="lyapunov_symm.html" class="code" title="function [x,ns_var]=lyapunov_symm(a,b)">lyapunov_symm</a> solves x-a*x*a'=b for b (and then x) symmetrical</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="calib.html" class="code" title="function M_.Sigma_e = calib(var_indices,targets,var_weights,nar,cova,M_.Sigma_e)">calib</a> </li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <span class="comment">% targets and iy order: 1) variances 2) correlations</span>
0002 <span class="comment">% 3) constraints on M_.Sigma_e itself 4) autocorrelations</span>
0003 <a name="_sub0" href="#_subfunctions" class="code">function objective=calib_obj2(M_.Sigma_e,A,ghu1,ghx,ghu,targets,var_weights,iy,nar)</a>
0004 <span class="keyword">global</span> vx fold
0005
0006 objective = cell (nar+3);
0007 oo_.gamma_y = cell(nar+1,1);
0008 M_.Sigma_e=diag(M_.Sigma_e);
0009 nx = size(ghx,2);
0010 b=ghu1*M_.Sigma_e*ghu1';
0011 vx = <a href="lyapunov_symm.html" class="code" title="function [x,ns_var]=lyapunov_symm(a,b)">lyapunov_symm</a>(A,b);
0012 oo_.gamma_y{1} = ghx*vx*ghx'+ ghu*M_.Sigma_e*ghu';
0013 <span class="keyword">if</span> ~isempty(targets{1})
0014 objective{1} = sqrt(oo_.gamma_y{1}(iy{1}));
0015 <span class="keyword">end</span>
0016
0017 sy = sqrt(diag(oo_.gamma_y{1}));
0018 sy = sy *sy';
0019 <span class="keyword">if</span> ~isempty(targets{2})
0020 objective{2} = oo_.gamma_y{1}(iy{2})./(sy(iy{2})+1e-10);
0021 <span class="keyword">end</span>
0022
0023 <span class="keyword">if</span> ~isempty(targets{3})
0024 objective{3} = M_.Sigma_e(iy{3});
0025 <span class="keyword">end</span>
0026
0027 <span class="comment">% autocorrelations</span>
0028 <span class="keyword">if</span> nar &gt; 0
0029 vxy = (A*vx*ghx'+ghu1*M_.Sigma_e*ghu');
0030
0031 oo_.gamma_y{2} = ghx*vxy./(sy+1e-10);
0032 <span class="keyword">if</span> ~isempty(targets{4})
0033 objective{4} = oo_.gamma_y{2}(iy{4});
0034 <span class="keyword">end</span>
0035
0036 <span class="keyword">for</span> i=2:nar
0037 vxy = A*vxy;
0038 oo_.gamma_y{i+1} = ghx*vxy./(sy+1e-10);
0039 <span class="keyword">if</span> ~isempty(targets{i+3})
0040 objecitve{i+3} = oo_.gamma_y{i+1}(iy{i+3});
0041 <span class="keyword">end</span>
0042 <span class="keyword">end</span>
0043 <span class="keyword">end</span>
0044
0045 <span class="comment">% 11/04/02 MJ generalized for correlations, autocorrelations and</span>
0046 <span class="comment">% constraints on M_.Sigma_e</span></pre></div>
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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"
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<title>Description of check</title>
<meta name="keywords" content="check">
<meta name="description" content="Copyright (C) 2001 Michel Juillard">
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<h1>check
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>Copyright (C) 2001 Michel Juillard</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function result = check </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> Copyright (C) 2001 Michel Juillard</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="print_info.html" class="code" title="function print_info(info)">print_info</a> Copyright (C) 2005 Michel Juillard</li><li><a href="resol.html" class="code" title="function [dr,info]=resol(ys,check_flag)">resol</a> Copyright (C) 2001 Michel Juillard</li><li><a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a> </li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="lnsrch.html" class="code" title="function [x,f,fvec,check]=lnsrch(xold,fold,g,p,stpmax,func,varargin)">lnsrch</a> Copyright (C) 2001 Michel Juillard</li><li><a href="lnsrch1.html" class="code" title="function [x,f,fvec,check]=lnsrch(xold,fold,g,p,stpmax,func,j1,j2,varargin)">lnsrch1</a> Copyright (C) 2001 Michel Juillard</li><li><a href="newrat.html" class="code" title="function [xparam1, hh, gg, fval, igg] = newrat(func0, x, hh, gg, igg, ftol0, nit, flagg, varargin)">newrat</a> </li><li><a href="olr1.html" class="code" title="function dr = olr1(ys,algo,olr_inst,bet,obj_var,W)">olr1</a> Copyright (C) 2001 Michel Juillard</li><li><a href="osr1.html" class="code" title="function oo_.dr=osr1(params,weights)">osr1</a> </li><li><a href="simult_.html" class="code" title="function y_=simult_(y0,dr,ex_,iorder)">simult_</a> Copyright (C) 2001 Michel Juillard</li><li><a href="solve1.html" class="code" title="function [x,check] = solve1(func,x,j1,j2,jacobian_flag,varargin)">solve1</a> Copyright (C) 2001 Michel Juillard</li><li><a href="steady_.html" class="code" title="function steady_()">steady_</a> Copyright (C) 2001 Michel Juillard</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <span class="comment">% Copyright (C) 2001 Michel Juillard</span>
0002 <span class="comment">%</span>
0003
0004 <a name="_sub0" href="#_subfunctions" class="code">function result = check</a>
0005 <span class="keyword">global</span> M_ options_ oo_
0006 <span class="keyword">global</span> it_
0007
0008 temp_options = options_;
0009 tempex = oo_.exo_simul;
0010 <span class="keyword">if</span> ~options_.initval_file
0011 oo_.exo_simul = ones(M_.maximum_lead+M_.maximum_lag+1,1)*oo_.exo_steady_state';
0012 <span class="keyword">end</span>
0013
0014 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'noprint'</span>,0);
0015 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'order'</span>,1);
0016 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'linear'</span>,0);
0017 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'dr_algo'</span>,0);
0018
0019 [dr, info] = <a href="resol.html" class="code" title="function [dr,info]=resol(ys,check_flag)">resol</a>(oo_.steady_state,1);
0020
0021 <span class="keyword">if</span> info(1) ~= 0 &amp; info(1) ~= 3 &amp; info(1) ~= 4
0022 <a href="print_info.html" class="code" title="function print_info(info)">print_info</a>(info);
0023 <span class="keyword">end</span>
0024
0025 oo_.exo_simul = tempex;
0026
0027 eigenvalues_ = dr.eigval;
0028 nyf = nnz(dr.kstate(:,2)&gt;M_.maximum_lag+1);
0029 [m_lambda,i]=sort(abs(eigenvalues_));
0030
0031 <span class="keyword">if</span> options_.noprint == 0
0032 disp(<span class="string">' '</span>)
0033 disp(<span class="string">'EIGENVALUES:'</span>)
0034 disp(sprintf(<span class="string">'%16s %16s %16s\n'</span>,<span class="string">'Modulus'</span>,<span class="string">'Real'</span>,<span class="string">'Imaginary'</span>))
0035 z=[m_lambda real(eigenvalues_(i)) imag(eigenvalues_(i))]';
0036 disp(sprintf(<span class="string">'%16.4g %16.4g %16.4g\n'</span>,z))
0037 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'qz_criterium'</span>,1.000001);
0038 disp(sprintf(<span class="string">'\nThere are %d eigenvalue(s) larger than 1 in modulus '</span>, <span class="keyword">...</span>
0039 nnz(abs(eigenvalues_) &gt; options_.qz_criterium)));
0040 disp(sprintf(<span class="string">'for %d forward-looking variable(s)'</span>,nyf));
0041 disp(<span class="string">' '</span>)
0042 <span class="keyword">if</span> info(1) == 0
0043 <span class="keyword">if</span> dr.rank == nyf
0044 disp(<span class="string">'The rank condition is verified.'</span>)
0045 <span class="keyword">else</span>
0046 disp(<span class="string">'The rank conditions ISN''T verified!'</span>)
0047 <span class="keyword">end</span>
0048 disp(<span class="string">' '</span>)
0049 <span class="keyword">end</span>
0050 <span class="keyword">end</span>
0051
0052 <span class="comment">% keep lambda_ for backward compatibility</span>
0053 lambda_ = eigenvalues_;
0054
0055 options_ = temp_options;
0056
0057 <span class="comment">% 2/9/99 MJ: line 15, added test for absence of exogenous variable.</span>
0058 <span class="comment">% 8/27/2000 MJ: change JACOB call. Added ...,1 to cumsum()</span>
0059 <span class="comment">% 6/24/01 MJ: added count of abs(eigenvalues) &gt; 1</span>
0060 <span class="comment">% 2/21/02 MJ: count eigenvalues &gt; 1[+1e-5]</span>
0061 <span class="comment">% 01/22/03 MJ: warning(warning_state) needs parentheses for Matlab 6.5</span>
0062 <span class="comment">% name conflicts with parameters</span>
0063 <span class="comment">% 05/21/03 MJ: replace computation by dr1.m and add rank check</span>
0064 <span class="comment">% 06/05/03 MJ: corrected bug when M_.maximum_lag &gt; 0</span>
0065
0066
0067
0068
0069</pre></div>
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<title>Description of check_mh</title>
<meta name="keywords" content="check_mh">
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<h1>check_mh
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong></strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function check_mh(fname) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"></pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="my_subplot.html" class="code" title="function my_subplot(i,imax,irow,icol,fig_title)">my_subplot</a> spreads subplots on several figures according to a maximum number of</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function check_mh(fname)</a>
0002 eval([<span class="string">'load '</span> fname]);
0003 nb = size(x3,3);
0004 np = size(x2,2);
0005 nr = size(x2,1);
0006
0007 j1 = ceil(0.5*nr);
0008 x = [j1:100:nr];
0009 z = [];
0010 <span class="keyword">for</span> i=1:np
0011 y1 = zeros(size(x),nb);
0012 <span class="keyword">for</span> k=1:nb
0013 <span class="keyword">for</span> j=1:length(x)
0014 y1(j,k) = mean(x2(1:x(j),i,k));
0015 <span class="keyword">end</span>
0016 <span class="keyword">end</span>
0017 <a href="my_subplot.html" class="code" title="function my_subplot(i,imax,irow,icol,fig_title)">my_subplot</a>(i,np,4,5,<span class="string">'MH convergence'</span>);
0018 plot([y1])
0019 xmin = min(min(x2(:,i,:)));
0020 xmax = max(max(x2(:,i,:)));
0021 z = [z; [i sum(sum(x3(:,i,:) &lt; xmin))/nr sum(sum(x3(:,i) &gt; xmax))/nr]];
0022 <span class="keyword">end</span>
0023 disp(z)
0024</pre></div>
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<title>Description of check_model</title>
<meta name="keywords" content="check_model">
<meta name="description" content="">
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<h1>check_model
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong></strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function check_model() </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"></pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="stoch_simul.html" class="code" title="function info=stoch_simul(var_list)">stoch_simul</a> Copyright (C) 2001 Michel Juillard</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function check_model()</a>
0002 <span class="keyword">global</span> M_
0003
0004 xlen = M_.maximum_exo_lag+M_.maximum_exo_lead + 1;
0005 <span class="keyword">if</span> ~ M_.lead_lag_incidence(M_.maximum_lag+1,:) &gt; 0
0006 error (<span class="string">'RESOL: Error in model specification: some variables don&quot;t appear as current'</span>) ;
0007 <span class="keyword">end</span>
0008
0009 <span class="keyword">if</span> xlen &gt; 1
0010 error ([<span class="string">'RESOL: stochastic exogenous variables must appear only at the'</span> <span class="keyword">...</span>
0011 <span class="string">' current period. Use additional endogenous variables'</span>]) ;
0012 <span class="keyword">end</span>
0013
0014 <span class="keyword">if</span> (M_.exo_det_nbr &gt; 0) &amp; (M_.maximum_lag &gt; 1 | M_.maximum_lead &gt; 1)
0015 error([<span class="string">'Exogenous deterministic variables are currently only allowed in'</span> <span class="keyword">...</span>
0016 <span class="string">' models with leads and lags on only one period'</span>])
0017 <span class="keyword">end</span>
0018</pre></div>
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<meta name="keywords" content="compDist">
<meta name="description" content="">
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<h1>compDist
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong></strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function compdist(xparam1, x2, pltopt, figurename) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"></pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="lpdfgam.html" class="code" title="function ldens = lpdfgam(x,a,b);">lpdfgam</a> log GAMMA PDF</li><li><a href="mj_qgamma.html" class="code" title="function x = mj_qgamma(p,a)">mj_qgamma</a> MJ_QGAMMA The gamma inverse distribution function</li><li><a href="posterior_density_estimate.html" class="code" title="function [abscissa,f,h] = posterior_density_estimate(data,number_of_grid_points,bandwidth,kernel_function)">posterior_density_estimate</a> %</li><li><a href="qbeta.html" class="code" title="function x = qbeta(p,a,b)">qbeta</a> QBETA The beta inverse distribution function</li><li><a href="qgamma.html" class="code" title="function x = qgamma(p,a)">qgamma</a> QGAMMA The gamma inverse distribution function</li><li><a href="qnorm.html" class="code" title="function x = qnorm(p,m,s)">qnorm</a> QNORM The normal inverse distribution function</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function compdist(xparam1, x2, pltopt, figurename)</a>
0002 <span class="keyword">global</span> bayestopt_ estim_params_ M_ options_
0003
0004 <span class="comment">% NOTE: If pltopt ~= 'All' compdist.m just draws prior densities.</span>
0005
0006 <span class="comment">%% Set density estimation parameters:</span>
0007 number_of_grid_points = 2^9; <span class="comment">% 2^9 = 512 !... Must be a power of two.</span>
0008 bandwidth = 0; <span class="comment">% Rule of thumb optimal bandwidth parameter.</span>
0009 kernel_function = <span class="string">'gaussian'</span>; <span class="comment">% You can switch to: 'epanechnikov', 'quartic', 'triangle',</span>
0010 <span class="comment">% 'triweight', 'uniform' or 'cosinus' kernels (iff bandwidth=0, see posterior_density_estimate.m).</span>
0011 truncprior = 10^(-3);
0012
0013
0014 npar=length(xparam1);
0015 nruns=length(x2);
0016 icol=ceil(sqrt(npar));
0017 iraw=icol;
0018 <span class="keyword">if</span> (icol-1)*(icol-2)&gt;=npar
0019 iraw = icol-2;
0020 icol=icol-1;
0021 <span class="keyword">elseif</span> (icol)*(icol-2)&gt;=npar
0022 iraw = icol-2;
0023 <span class="keyword">elseif</span> icol*(icol-1)&gt;=npar
0024 iraw=icol-1;
0025 <span class="keyword">end</span>
0026
0027 pmean=bayestopt_.pmean;
0028 pshape=bayestopt_.pshape;
0029 p1 = bayestopt_.p1;
0030 p2 = bayestopt_.p2;
0031 p3 = bayestopt_.p3;
0032 p4 = bayestopt_.p4;
0033
0034 figure(<span class="string">'Name'</span>,figurename)
0035 <span class="keyword">for</span> i=1:npar;
0036 <span class="keyword">if</span> i&lt;=estim_params_.nvx
0037 vname = deblank(M_.exo_name(estim_params_.var_exo(i,1),:));
0038 nam=[<span class="string">'SE_{'</span>,vname,<span class="string">'}'</span>];
0039 <span class="keyword">elseif</span> i&lt;=(estim_params_.nvx+estim_params_.nvn)
0040 deblank(options_.varobs(estim_params_.var_endo(i-estim_params_.nvx,1),:));
0041 nam=[<span class="string">'SE_{EOBS_'</span>,vname,<span class="string">'}'</span>];
0042 <span class="keyword">elseif</span> i&lt;=(estim_params_.nvx+estim_params_.nvn+estim_params_.ncx)
0043 j = i - (estim_params_.nvx+estim_params_.nvn);
0044 k1 = estim_params_.corrx(j,1);
0045 k2 = estim_params_.corrx(j,2);
0046 vname = [deblank(M_.exo_name(k1,:)) <span class="string">','</span> deblank(M_.exo_name(k2,:))];
0047 nam=[<span class="string">'CC_{'</span>,vname,<span class="string">'}'</span>];
0048 <span class="keyword">elseif</span> i&lt;=(estim_params_.nvx+estim_params_.nvn+estim_params_.ncx+ <span class="keyword">...</span>
0049 estim_params_.ncn)
0050 j = i - (estim_params_.nvx+estim_params_.nvn+estim_params_.ncx);
0051 k1 = estim_params_.corrn(j,1);
0052 k2 = estim_params_.corrn(j,2);
0053 vname = [deblank(M_.exo_name(k1,:)) <span class="string">','</span> deblank(M_.exo_name(k2,:))];
0054 nam=[<span class="string">'CC_{EOBS_'</span>,vname,<span class="string">'}'</span>];
0055 <span class="keyword">else</span>
0056 j = i - (estim_params_.nvx+estim_params_.nvn+estim_params_.ncx+ <span class="keyword">...</span>
0057 estim_params_.ncn);
0058 nam = deblank(estim_params_.param_names(j,:));
0059 <span class="keyword">end</span>
0060 subplot(iraw, icol, i);
0061 <span class="keyword">if</span> strcmpi(pltopt,<span class="string">'all'</span>); <span class="comment">% Estimation of the density...</span>
0062 [abscissa,ff,h] = <a href="posterior_density_estimate.html" class="code" title="function [abscissa,f,h] = posterior_density_estimate(data,number_of_grid_points,bandwidth,kernel_function)">posterior_density_estimate</a>(x2(round(options_.mh_drop*nruns):<span class="keyword">end</span>,i),<span class="keyword">...</span>
0063 number_of_grid_points,bandwidth,kernel_function);
0064 plot(abscissa,ff,<span class="string">'-k'</span>,<span class="string">'linewidth'</span>,2);
0065 <span class="keyword">end</span>;
0066 a = 0;
0067 b = 0;
0068 <span class="keyword">if</span> pshape(i) == 1; <span class="comment">%/* BETA Prior */</span>
0069 density = inline(<span class="string">'((1-x).^(b-1)).*x.^(a-1)./beta(a,b)'</span>,<span class="string">'x'</span>,<span class="string">'a'</span>,<span class="string">'b'</span>);
0070 mu = (p1(i)-p3(i))/(p4(i)-p3(i));
0071 stdd = p2(i)/(p4(i)-p3(i));
0072 a = (1-mu)*mu^2/stdd^2 - mu;
0073 b = a*(1/mu - 1);
0074 infbound = <a href="qbeta.html" class="code" title="function x = qbeta(p,a,b)">qbeta</a>(truncprior,a,b);
0075 supbound = <a href="qbeta.html" class="code" title="function x = qbeta(p,a,b)">qbeta</a>(1-truncprior,a,b);
0076 stepsize = (supbound-infbound)/200;
0077 abscissa = infbound:stepsize:supbound;
0078 f = density(abscissa,a,b);
0079 abscissa = abscissa*(p4(i)-p3(i))+p3(i);
0080 <span class="keyword">if</span> strcmp(pltopt,<span class="string">'all'</span>);
0081 top = max([max(ff);max(f)]);
0082 <span class="keyword">end</span>;
0083 <span class="keyword">elseif</span> pshape(i) == 2; <span class="comment">%/* GAMMA PRIOR */</span>
0084 <span class="comment">% density =</span>
0085 <span class="comment">% inline('((x/b).^(a-1)).*exp(-x/b)*inv(b*gamma(a))','x','a','b');</span>
0086 mu = p1(i)-p3(i);
0087 b = p2(i)^2/mu;
0088 a = mu/b;
0089 infbound = <a href="mj_qgamma.html" class="code" title="function x = mj_qgamma(p,a)">mj_qgamma</a>(truncprior,a)*b;
0090 supbound = <a href="mj_qgamma.html" class="code" title="function x = mj_qgamma(p,a)">mj_qgamma</a>(1-truncprior,a)*b;
0091 stepsize = (supbound-infbound)/200;
0092 abscissa = infbound:stepsize:supbound;
0093 f = exp(<a href="lpdfgam.html" class="code" title="function ldens = lpdfgam(x,a,b);">lpdfgam</a>(abscissa,a,b));
0094 abscissa = abscissa + p3(i);
0095 <span class="keyword">if</span> strcmp(pltopt,<span class="string">'all'</span>);
0096 top = max([max(ff);max(f)]);
0097 <span class="keyword">end</span>;
0098 <span class="keyword">elseif</span> pshape(i) == 3; <span class="comment">%/* GAUSSIAN PRIOR */</span>
0099 density = inline(<span class="string">'inv(sqrt(2*pi)*b)*exp(-0.5*((x-a)/b).^2)'</span>,<span class="string">'x'</span>,<span class="string">'a'</span>,<span class="string">'b'</span>);
0100 a = p1(i);
0101 b = p2(i);
0102 infbound = <a href="qnorm.html" class="code" title="function x = qnorm(p,m,s)">qnorm</a>(truncprior,a,b);
0103 supbound = <a href="qnorm.html" class="code" title="function x = qnorm(p,m,s)">qnorm</a>(1-truncprior,a,b);
0104 stepsize = (supbound-infbound)/200;
0105 abscissa = infbound:stepsize:supbound;
0106 f = density(abscissa,a,b);
0107 <span class="keyword">if</span> strcmp(pltopt,<span class="string">'all'</span>);
0108 top = max([max(ff);max(f)]);
0109 <span class="keyword">end</span>;
0110 <span class="keyword">elseif</span> pshape(i) == 4; <span class="comment">%/* INVGAMMA PRIOR type 1 */</span>
0111 density = inline(<span class="string">'2*inv(gamma(nu/2))*(x.^(-nu-1))*((s/2)^(nu/2)).*exp(-s./(2*x.^2))'</span>,<span class="string">'x'</span>,<span class="string">'s'</span>,<span class="string">'nu'</span>);
0112 nu = p2(i);
0113 s = p1(i);
0114 a = nu/2;
0115 b = 2/s;
0116 infbound = 1/sqrt(<a href="mj_qgamma.html" class="code" title="function x = mj_qgamma(p,a)">mj_qgamma</a>(1-10*truncprior,a)*b);
0117 supbound = 1/sqrt(<a href="mj_qgamma.html" class="code" title="function x = mj_qgamma(p,a)">mj_qgamma</a>(10*truncprior,a)*b);
0118 stepsize = (supbound-infbound)/200;
0119 abscissa = infbound:stepsize:supbound;
0120 f = density(abscissa,s,nu);
0121 <span class="keyword">if</span> strcmp(pltopt,<span class="string">'all'</span>);
0122 top = max([max(ff);max(f)]);
0123 <span class="keyword">end</span>;
0124 <span class="keyword">elseif</span> pshape(i) == 5; <span class="comment">%/* UNIFORM PRIOR */</span>
0125 density = inline(<span class="string">'(x.^0)/(b-a)'</span>,<span class="string">'x'</span>,<span class="string">'a'</span>,<span class="string">'b'</span>);
0126 a = p1(i);
0127 b = p2(i);
0128 infbound = a;
0129 supbound = b;
0130 stepsize = (supbound-infbound)/200;
0131 abscissa = infbound:stepsize:supbound;
0132 f = density(abscissa,a,b);
0133 <span class="keyword">if</span> strcmp(pltopt,<span class="string">'all'</span>);
0134 top = max([max(ff);max(f)]);
0135 <span class="keyword">end</span>;
0136 <span class="keyword">elseif</span> pshape(i) == 6; <span class="comment">%/* INVGAMMA PRIOR type 2 */</span>
0137 density = inline(<span class="string">'inv(gamma(nu/2))*(x.^(-.5(nu+2)))*((s/2)^(nu/2)).*exp(-s./(2*x))'</span>,<span class="string">'x'</span>,<span class="string">'s'</span>,<span class="string">'nu'</span>);
0138 nu = p2(i);
0139 s = p1(i);
0140 a = nu/2;
0141 b = 2/s;
0142 infbound = 1/(<a href="qgamma.html" class="code" title="function x = qgamma(p,a)">qgamma</a>(1-truncprior,a)*b);
0143 supbound = 1/(<a href="qgamma.html" class="code" title="function x = qgamma(p,a)">qgamma</a>(truncprior,a)*b);
0144 stepsize = (supbound-infbound)/200;
0145 abscissa = infbound:stepsize:supbound;
0146 f = density(abscissa,s,nu);
0147 <span class="keyword">if</span> strcmp(pltopt,<span class="string">'all'</span>);
0148 top = max([max(ff);max(f)]);
0149 <span class="keyword">end</span>;
0150 <span class="keyword">end</span>;
0151 hold on;
0152 k = [1:length(f)];
0153 <span class="keyword">if</span> pshape(i) ~= 5
0154 [junk,k1] = max(f);
0155 <span class="keyword">if</span> k1 == 1 | k1 == length(f)
0156 k = find(f &lt; 10);
0157 <span class="keyword">end</span>
0158 <span class="keyword">end</span>
0159 hh = plot(abscissa(k),f(k),<span class="string">'-k'</span>,<span class="string">'linewidth'</span>,2);
0160 set(hh,<span class="string">'color'</span>,[0.7 0.7 0.7]);
0161 <span class="comment">%hold on; %% ?!</span>
0162 <span class="keyword">if</span> strcmp(pltopt,<span class="string">'all'</span>);
0163 plot( [xparam1(i) xparam1(i)], [0,top], <span class="string">'--g'</span>, <span class="string">'linewidth'</span>, 2);
0164 <span class="keyword">end</span>;
0165 title(nam,<span class="string">'Interpreter'</span>,<span class="string">'none'</span>);
0166 hold off;
0167 <span class="keyword">end</span>;
0168 drawnow
0169
0170 <span class="comment">% 12/01/03 MJ adapted from M. Ratto's version</span>
0171</pre></div>
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<title>Description of csminit</title>
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<div><a href="../index.html">Home</a> &gt; <a href="index.html">.</a> &gt; csminit.m</div>
<!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png">&nbsp;Master index</a></td>
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<h1>csminit
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>[fhat,xhat,fcount,retcode] = csminit(fcn,x0,f0,g0,badg,H0,...</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function [fhat,xhat,fcount,retcode] = csminit(fcn,x0,f0,g0,badg,H0,varargin) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> [fhat,xhat,fcount,retcode] = csminit(fcn,x0,f0,g0,badg,H0,...
P1,P2,P3,P4,P5,P6,P7,P8)
retcodes: 0, normal step. 5, largest step still improves too fast.
4,2 back and forth adjustment of stepsize didn't finish. 3, smallest
stepsize still improves too slow. 6, no improvement found. 1, zero
gradient.
---------------------
Modified 7/22/96 to omit variable-length P list, for efficiency and compilation.
Places where the number of P's need to be altered or the code could be returned to
its old form are marked with ARGLIST comments.
Fixed 7/17/93 to use inverse-hessian instead of hessian itself in bfgs
update.
Fixed 7/19/93 to flip eigenvalues of H to get better performance when
it's not psd.
tailstr = ')';
for i=nargin-6:-1:1
tailstr=[ ',P' num2str(i) tailstr];
end
ANGLE = .03;</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="csminwel.html" class="code" title="function [fh,xh,gh,H,itct,fcount,retcodeh] = csminwel(fcn,x0,H0,grad,crit,nit,varargin)">csminwel</a> [fhat,xhat,ghat,Hhat,itct,fcount,retcodehat] = csminwel(fcn,x0,H0,grad,crit,nit,varargin)</li><li><a href="mr_gstep.html" class="code" title="function [f0, x] = mr_gstep(func0,x,htol0,varargin)">mr_gstep</a> Copyright (C) 2005 Marco Ratto</li><li><a href="newrat.html" class="code" title="function [xparam1, hh, gg, fval, igg] = newrat(func0, x, hh, gg, igg, ftol0, nit, flagg, varargin)">newrat</a> </li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function [fhat,xhat,fcount,retcode] = csminit(fcn,x0,f0,g0,badg,H0,varargin)</a>
0002 <span class="comment">% [fhat,xhat,fcount,retcode] = csminit(fcn,x0,f0,g0,badg,H0,...</span>
0003 <span class="comment">% P1,P2,P3,P4,P5,P6,P7,P8)</span>
0004 <span class="comment">% retcodes: 0, normal step. 5, largest step still improves too fast.</span>
0005 <span class="comment">% 4,2 back and forth adjustment of stepsize didn't finish. 3, smallest</span>
0006 <span class="comment">% stepsize still improves too slow. 6, no improvement found. 1, zero</span>
0007 <span class="comment">% gradient.</span>
0008 <span class="comment">%---------------------</span>
0009 <span class="comment">% Modified 7/22/96 to omit variable-length P list, for efficiency and compilation.</span>
0010 <span class="comment">% Places where the number of P's need to be altered or the code could be returned to</span>
0011 <span class="comment">% its old form are marked with ARGLIST comments.</span>
0012 <span class="comment">%</span>
0013 <span class="comment">% Fixed 7/17/93 to use inverse-hessian instead of hessian itself in bfgs</span>
0014 <span class="comment">% update.</span>
0015 <span class="comment">%</span>
0016 <span class="comment">% Fixed 7/19/93 to flip eigenvalues of H to get better performance when</span>
0017 <span class="comment">% it's not psd.</span>
0018 <span class="comment">%</span>
0019 <span class="comment">%tailstr = ')';</span>
0020 <span class="comment">%for i=nargin-6:-1:1</span>
0021 <span class="comment">% tailstr=[ ',P' num2str(i) tailstr];</span>
0022 <span class="comment">%end</span>
0023 <span class="comment">%ANGLE = .03;</span>
0024 ANGLE = .005;
0025 <span class="comment">%THETA = .03;</span>
0026 THETA = .3; <span class="comment">%(0&lt;THETA&lt;.5) THETA near .5 makes long line searches, possibly fewer iterations.</span>
0027 FCHANGE = 1000;
0028 MINLAMB = 1e-9;
0029 <span class="comment">% fixed 7/15/94</span>
0030 <span class="comment">% MINDX = .0001;</span>
0031 <span class="comment">% MINDX = 1e-6;</span>
0032 MINDFAC = .01;
0033 fcount=0;
0034 lambda=1;
0035 xhat=x0;
0036 f=f0;
0037 fhat=f0;
0038 g = g0;
0039 gnorm = norm(g);
0040 <span class="comment">%</span>
0041 <span class="keyword">if</span> (gnorm &lt; 1.e-12) &amp; ~badg <span class="comment">% put ~badg 8/4/94</span>
0042 retcode =1;
0043 dxnorm=0;
0044 <span class="comment">% gradient convergence</span>
0045 <span class="keyword">else</span>
0046 <span class="comment">% with badg true, we don't try to match rate of improvement to directional</span>
0047 <span class="comment">% derivative. We're satisfied just to get some improvement in f.</span>
0048 <span class="comment">%</span>
0049 <span class="comment">%if(badg)</span>
0050 <span class="comment">% dx = -g*FCHANGE/(gnorm*gnorm);</span>
0051 <span class="comment">% dxnorm = norm(dx);</span>
0052 <span class="comment">% if dxnorm &gt; 1e12</span>
0053 <span class="comment">% disp('Bad, small gradient problem.')</span>
0054 <span class="comment">% dx = dx*FCHANGE/dxnorm;</span>
0055 <span class="comment">% end</span>
0056 <span class="comment">%else</span>
0057 <span class="comment">% Gauss-Newton step;</span>
0058 <span class="comment">%---------- Start of 7/19/93 mod ---------------</span>
0059 <span class="comment">%[v d] = eig(H0);</span>
0060 <span class="comment">%toc</span>
0061 <span class="comment">%d=max(1e-10,abs(diag(d)));</span>
0062 <span class="comment">%d=abs(diag(d));</span>
0063 <span class="comment">%dx = -(v.*(ones(size(v,1),1)*d'))*(v'*g);</span>
0064 <span class="comment">% toc</span>
0065 dx = -H0*g;
0066 <span class="comment">% toc</span>
0067 dxnorm = norm(dx);
0068 <span class="keyword">if</span> dxnorm &gt; 1e12
0069 disp(<span class="string">'Near-singular H problem.'</span>)
0070 dx = dx*FCHANGE/dxnorm;
0071 <span class="keyword">end</span>
0072 dfhat = dx'*g0;
0073 <span class="comment">%end</span>
0074 <span class="comment">%</span>
0075 <span class="comment">%</span>
0076 <span class="keyword">if</span> ~badg
0077 <span class="comment">% test for alignment of dx with gradient and fix if necessary</span>
0078 a = -dfhat/(gnorm*dxnorm);
0079 <span class="keyword">if</span> a&lt;ANGLE
0080 dx = dx - (ANGLE*dxnorm/gnorm+dfhat/(gnorm*gnorm))*g;
0081 dfhat = dx'*g;
0082 dxnorm = norm(dx);
0083 disp(sprintf(<span class="string">'Correct for low angle: %g'</span>,a))
0084 <span class="keyword">end</span>
0085 <span class="keyword">end</span>
0086 disp(sprintf(<span class="string">'Predicted improvement: %18.9f'</span>,-dfhat/2))
0087 <span class="comment">%</span>
0088 <span class="comment">% Have OK dx, now adjust length of step (lambda) until min and</span>
0089 <span class="comment">% max improvement rate criteria are met.</span>
0090 done=0;
0091 factor=3;
0092 shrink=1;
0093 lambdaMin=0;
0094 lambdaMax=inf;
0095 lambdaPeak=0;
0096 fPeak=f0;
0097 lambdahat=0;
0098 <span class="keyword">while</span> ~done
0099 <span class="keyword">if</span> size(x0,2)&gt;1
0100 dxtest=x0+dx'*lambda;
0101 <span class="keyword">else</span>
0102 dxtest=x0+dx*lambda;
0103 <span class="keyword">end</span>
0104 <span class="comment">% home</span>
0105 f = eval([fcn <span class="string">'(dxtest,varargin{:})'</span>]);
0106 <span class="comment">%ARGLIST</span>
0107 <span class="comment">%f = feval(fcn,dxtest,P1,P2,P3,P4,P5,P6,P7,P8,P9,P10,P11,P12,P13);</span>
0108 <span class="comment">% f = feval(fcn,x0+dx*lambda,P1,P2,P3,P4,P5,P6,P7,P8);</span>
0109 disp(sprintf(<span class="string">'lambda = %10.5g; f = %20.7f'</span>,lambda,f ))
0110 <span class="comment">%debug</span>
0111 <span class="comment">%disp(sprintf('Improvement too great? f0-f: %g, criterion: %g',f0-f,-(1-THETA)*dfhat*lambda))</span>
0112 <span class="keyword">if</span> f&lt;fhat
0113 fhat=f;
0114 xhat=dxtest;
0115 lambdahat = lambda;
0116 <span class="keyword">end</span>
0117 fcount=fcount+1;
0118 shrinkSignal = (~badg &amp; (f0-f &lt; max([-THETA*dfhat*lambda 0]))) | (badg &amp; (f0-f) &lt; 0) ;
0119 growSignal = ~badg &amp; ( (lambda &gt; 0) &amp; (f0-f &gt; -(1-THETA)*dfhat*lambda) );
0120 <span class="keyword">if</span> shrinkSignal &amp; ( (lambda&gt;lambdaPeak) | (lambda&lt;0) )
0121 <span class="keyword">if</span> (lambda&gt;0) &amp; ((~shrink) | (lambda/factor &lt;= lambdaPeak))
0122 shrink=1;
0123 factor=factor^.6;
0124 <span class="keyword">while</span> lambda/factor &lt;= lambdaPeak
0125 factor=factor^.6;
0126 <span class="keyword">end</span>
0127 <span class="comment">%if (abs(lambda)*(factor-1)*dxnorm &lt; MINDX) | (abs(lambda)*(factor-1) &lt; MINLAMB)</span>
0128 <span class="keyword">if</span> abs(factor-1)&lt;MINDFAC
0129 <span class="keyword">if</span> abs(lambda)&lt;4
0130 retcode=2;
0131 <span class="keyword">else</span>
0132 retcode=7;
0133 <span class="keyword">end</span>
0134 done=1;
0135 <span class="keyword">end</span>
0136 <span class="keyword">end</span>
0137 <span class="keyword">if</span> (lambda&lt;lambdaMax) &amp; (lambda&gt;lambdaPeak)
0138 lambdaMax=lambda;
0139 <span class="keyword">end</span>
0140 lambda=lambda/factor;
0141 <span class="keyword">if</span> abs(lambda) &lt; MINLAMB
0142 <span class="keyword">if</span> (lambda &gt; 0) &amp; (f0 &lt;= fhat)
0143 <span class="comment">% try going against gradient, which may be inaccurate</span>
0144 lambda = -lambda*factor^6
0145 <span class="keyword">else</span>
0146 <span class="keyword">if</span> lambda &lt; 0
0147 retcode = 6;
0148 <span class="keyword">else</span>
0149 retcode = 3;
0150 <span class="keyword">end</span>
0151 done = 1;
0152 <span class="keyword">end</span>
0153 <span class="keyword">end</span>
0154 <span class="keyword">elseif</span> (growSignal &amp; lambda&gt;0) | (shrinkSignal &amp; ((lambda &lt;= lambdaPeak) &amp; (lambda&gt;0)))
0155 <span class="keyword">if</span> shrink
0156 shrink=0;
0157 factor = factor^.6;
0158 <span class="comment">%if ( abs(lambda)*(factor-1)*dxnorm&lt; MINDX ) | ( abs(lambda)*(factor-1)&lt; MINLAMB)</span>
0159 <span class="keyword">if</span> abs(factor-1)&lt;MINDFAC
0160 <span class="keyword">if</span> abs(lambda)&lt;4
0161 retcode=4;
0162 <span class="keyword">else</span>
0163 retcode=7;
0164 <span class="keyword">end</span>
0165 done=1;
0166 <span class="keyword">end</span>
0167 <span class="keyword">end</span>
0168 <span class="keyword">if</span> ( f&lt;fPeak ) &amp; (lambda&gt;0)
0169 fPeak=f;
0170 lambdaPeak=lambda;
0171 <span class="keyword">if</span> lambdaMax&lt;=lambdaPeak
0172 lambdaMax=lambdaPeak*factor*factor;
0173 <span class="keyword">end</span>
0174 <span class="keyword">end</span>
0175 lambda=lambda*factor;
0176 <span class="keyword">if</span> abs(lambda) &gt; 1e20;
0177 retcode = 5;
0178 done =1;
0179 <span class="keyword">end</span>
0180 <span class="keyword">else</span>
0181 done=1;
0182 <span class="keyword">if</span> factor &lt; 1.2
0183 retcode=7;
0184 <span class="keyword">else</span>
0185 retcode=0;
0186 <span class="keyword">end</span>
0187 <span class="keyword">end</span>
0188 <span class="keyword">end</span>
0189 <span class="keyword">end</span>
0190 disp(sprintf(<span class="string">'Norm of dx %10.5g'</span>, dxnorm))</pre></div>
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<h1>csminwel
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>[fhat,xhat,ghat,Hhat,itct,fcount,retcodehat] = csminwel(fcn,x0,H0,grad,crit,nit,varargin)</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function [fh,xh,gh,H,itct,fcount,retcodeh] = csminwel(fcn,x0,H0,grad,crit,nit,varargin) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment">[fhat,xhat,ghat,Hhat,itct,fcount,retcodehat] = csminwel(fcn,x0,H0,grad,crit,nit,varargin)
fcn: string naming the objective function to be minimized
x0: initial value of the parameter vector
H0: initial value for the inverse Hessian. Must be positive definite.
grad: Either a string naming a function that calculates the gradient, or the null matrix.
If it's null, the program calculates a numerical gradient. In this case fcn must
be written so that it can take a matrix argument and produce a row vector of values.
crit: Convergence criterion. Iteration will cease when it proves impossible to improve the
function value by more than crit.
nit: Maximum number of iterations.
varargin: A list of optional length of additional parameters that get handed off to fcn each
time it is called.
Note that if the program ends abnormally, it is possible to retrieve the current x,
f, and H from the files g1.mat and H.mat that are written at each iteration and at each
hessian update, respectively. (When the routine hits certain kinds of difficulty, it
write g2.mat and g3.mat as well. If all were written at about the same time, any of them
may be a decent starting point. One can also start from the one with best function value.)</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="bfgsi.html" class="code" title="function H = bfgsi(H0,dg,dx)">bfgsi</a> H = bfgsi(H0,dg,dx)</li><li><a href="csminit.html" class="code" title="function [fhat,xhat,fcount,retcode] = csminit(fcn,x0,f0,g0,badg,H0,varargin)">csminit</a> [fhat,xhat,fcount,retcode] = csminit(fcn,x0,f0,g0,badg,H0,...</li><li><a href="numgrad.html" class="code" title="function [g, badg] = numgrad(fcn,x,varargin)">numgrad</a> function [g badg] = numgrad(fcn,xvarargin)</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="dynare_estimation.html" class="code" title="function dynare_estimation(var_list_)">dynare_estimation</a> </li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function [fh,xh,gh,H,itct,fcount,retcodeh] = csminwel(fcn,x0,H0,grad,crit,nit,varargin)</a>
0002 <span class="comment">%[fhat,xhat,ghat,Hhat,itct,fcount,retcodehat] = csminwel(fcn,x0,H0,grad,crit,nit,varargin)</span>
0003 <span class="comment">% fcn: string naming the objective function to be minimized</span>
0004 <span class="comment">% x0: initial value of the parameter vector</span>
0005 <span class="comment">% H0: initial value for the inverse Hessian. Must be positive definite.</span>
0006 <span class="comment">% grad: Either a string naming a function that calculates the gradient, or the null matrix.</span>
0007 <span class="comment">% If it's null, the program calculates a numerical gradient. In this case fcn must</span>
0008 <span class="comment">% be written so that it can take a matrix argument and produce a row vector of values.</span>
0009 <span class="comment">% crit: Convergence criterion. Iteration will cease when it proves impossible to improve the</span>
0010 <span class="comment">% function value by more than crit.</span>
0011 <span class="comment">% nit: Maximum number of iterations.</span>
0012 <span class="comment">% varargin: A list of optional length of additional parameters that get handed off to fcn each</span>
0013 <span class="comment">% time it is called.</span>
0014 <span class="comment">% Note that if the program ends abnormally, it is possible to retrieve the current x,</span>
0015 <span class="comment">% f, and H from the files g1.mat and H.mat that are written at each iteration and at each</span>
0016 <span class="comment">% hessian update, respectively. (When the routine hits certain kinds of difficulty, it</span>
0017 <span class="comment">% write g2.mat and g3.mat as well. If all were written at about the same time, any of them</span>
0018 <span class="comment">% may be a decent starting point. One can also start from the one with best function value.)</span>
0019 <span class="keyword">global</span> bayestopt_
0020 [nx,no]=size(x0);
0021 nx=max(nx,no);
0022 Verbose=1;
0023 NumGrad= isempty(grad);
0024 done=0;
0025 itct=0;
0026 fcount=0;
0027 snit=100;
0028 <span class="comment">%tailstr = ')';</span>
0029 <span class="comment">%stailstr = [];</span>
0030 <span class="comment">% Lines below make the number of Pi's optional. This is inefficient, though, and precludes</span>
0031 <span class="comment">% use of the matlab compiler. Without them, we use feval and the number of Pi's must be</span>
0032 <span class="comment">% changed with the editor for each application. Places where this is required are marked</span>
0033 <span class="comment">% with ARGLIST comments</span>
0034 <span class="comment">%for i=nargin-6:-1:1</span>
0035 <span class="comment">% tailstr=[ ',P' num2str(i) tailstr];</span>
0036 <span class="comment">% stailstr=[' P' num2str(i) stailstr];</span>
0037 <span class="comment">%end</span>
0038 f0 = feval(fcn,x0,varargin{:});
0039 <span class="comment">%ARGLIST</span>
0040 <span class="comment">%f0 = feval(fcn,x0,P1,P2,P3,P4,P5,P6,P7,P8,P9,P10,P11,P12,P13);</span>
0041 <span class="comment">% disp('first fcn in csminwel.m ----------------') % Jinill on 9/5/95</span>
0042 <span class="keyword">if</span> f0 &gt; 1e50, disp(<span class="string">'Bad initial parameter.'</span>), <span class="keyword">return</span>, <span class="keyword">end</span>
0043 <span class="keyword">if</span> NumGrad
0044 <span class="keyword">if</span> length(grad)==0
0045 [g badg] = <a href="numgrad.html" class="code" title="function [g, badg] = numgrad(fcn,x,varargin)">numgrad</a>(fcn,x0, varargin{:});
0046 <span class="comment">%ARGLIST</span>
0047 <span class="comment">%[g badg] = numgrad(fcn,x0,P1,P2,P3,P4,P5,P6,P7,P8,P9,P10,P11,P12,P13);</span>
0048 <span class="keyword">else</span>
0049 badg=any(find(grad==0));
0050 g=grad;
0051 <span class="keyword">end</span>
0052 <span class="comment">%numgrad(fcn,x0,P1,P2,P3,P4);</span>
0053 <span class="keyword">else</span>
0054 [g badg] = feval(grad,x0,varargin{:});
0055 <span class="comment">%ARGLIST</span>
0056 <span class="comment">%[g badg] = feval(grad,x0,P1,P2,P3,P4,P5,P6,P7,P8,P9,P10,P11,P12,P13);</span>
0057 <span class="keyword">end</span>
0058 retcode3=101;
0059 x=x0;
0060 f=f0;
0061 H=H0;
0062 cliff=0;
0063 <span class="keyword">while</span> ~done
0064 bayestopt_.penalty = f;
0065 g1=[]; g2=[]; g3=[];
0066 <span class="comment">%addition fj. 7/6/94 for control</span>
0067 disp(<span class="string">'-----------------'</span>)
0068 disp(<span class="string">'-----------------'</span>)
0069 <span class="comment">%disp('f and x at the beginning of new iteration')</span>
0070 disp(sprintf(<span class="string">'f at the beginning of new iteration, %20.10f'</span>,f))
0071 <span class="comment">%-----------Comment out this line if the x vector is long----------------</span>
0072 <span class="comment">% disp([sprintf('x = ') sprintf('%15.8g %15.8g %15.8g %15.8g\n',x)]);</span>
0073 <span class="comment">%-------------------------</span>
0074 itct=itct+1;
0075 [f1 x1 fc retcode1] = <a href="csminit.html" class="code" title="function [fhat,xhat,fcount,retcode] = csminit(fcn,x0,f0,g0,badg,H0,varargin)">csminit</a>(fcn,x,f,g,badg,H,varargin{:});
0076 <span class="comment">%ARGLIST</span>
0077 <span class="comment">%[f1 x1 fc retcode1] = csminit(fcn,x,f,g,badg,H,P1,P2,P3,P4,P5,P6,P7,...</span>
0078 <span class="comment">% P8,P9,P10,P11,P12,P13);</span>
0079 <span class="comment">% itct=itct+1;</span>
0080 fcount = fcount+fc;
0081 <span class="comment">% erased on 8/4/94</span>
0082 <span class="comment">% if (retcode == 1) | (abs(f1-f) &lt; crit)</span>
0083 <span class="comment">% done=1;</span>
0084 <span class="comment">% end</span>
0085 <span class="comment">% if itct &gt; nit</span>
0086 <span class="comment">% done = 1;</span>
0087 <span class="comment">% retcode = -retcode;</span>
0088 <span class="comment">% end</span>
0089 <span class="keyword">if</span> retcode1 ~= 1
0090 <span class="keyword">if</span> retcode1==2 | retcode1==4
0091 wall1=1; badg1=1;
0092 <span class="keyword">else</span>
0093 <span class="keyword">if</span> NumGrad
0094 [g1 badg1] = <a href="numgrad.html" class="code" title="function [g, badg] = numgrad(fcn,x,varargin)">numgrad</a>(fcn, x1,varargin{:});
0095 <span class="comment">%ARGLIST</span>
0096 <span class="comment">%[g1 badg1] = numgrad(fcn, x1,P1,P2,P3,P4,P5,P6,P7,P8,P9,...</span>
0097 <span class="comment">% P10,P11,P12,P13);</span>
0098 <span class="keyword">else</span>
0099 [g1 badg1] = feval(grad,x1,varargin{:});
0100 <span class="comment">%ARGLIST</span>
0101 <span class="comment">%[g1 badg1] = feval(grad, x1,P1,P2,P3,P4,P5,P6,P7,P8,P9,...</span>
0102 <span class="comment">% P10,P11,P12,P13);</span>
0103 <span class="keyword">end</span>
0104 wall1=badg1;
0105 <span class="comment">% g1</span>
0106 save g1 g1 x1 f1 varargin;
0107 <span class="comment">%ARGLIST</span>
0108 <span class="comment">%save g1 g1 x1 f1 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13;</span>
0109 <span class="keyword">end</span>
0110 <span class="keyword">if</span> wall1 <span class="comment">% &amp; (~done) by Jinill</span>
0111 <span class="comment">% Bad gradient or back and forth on step length. Possibly at</span>
0112 <span class="comment">% cliff edge. Try perturbing search direction.</span>
0113 <span class="comment">%</span>
0114 <span class="comment">%fcliff=fh;xcliff=xh;</span>
0115 Hcliff=H+diag(diag(H).*rand(nx,1));
0116 disp(<span class="string">'Cliff. Perturbing search direction.'</span>)
0117 [f2 x2 fc retcode2] = <a href="csminit.html" class="code" title="function [fhat,xhat,fcount,retcode] = csminit(fcn,x0,f0,g0,badg,H0,varargin)">csminit</a>(fcn,x,f,g,badg,Hcliff,varargin{:});
0118 <span class="comment">%ARGLIST</span>
0119 <span class="comment">%[f2 x2 fc retcode2] = csminit(fcn,x,f,g,badg,Hcliff,P1,P2,P3,P4,...</span>
0120 <span class="comment">% P5,P6,P7,P8,P9,P10,P11,P12,P13);</span>
0121 fcount = fcount+fc; <span class="comment">% put by Jinill</span>
0122 <span class="keyword">if</span> f2 &lt; f
0123 <span class="keyword">if</span> retcode2==2 | retcode2==4
0124 wall2=1; badg2=1;
0125 <span class="keyword">else</span>
0126 <span class="keyword">if</span> NumGrad
0127 [g2 badg2] = <a href="numgrad.html" class="code" title="function [g, badg] = numgrad(fcn,x,varargin)">numgrad</a>(fcn, x2,varargin{:});
0128 <span class="comment">%ARGLIST</span>
0129 <span class="comment">%[g2 badg2] = numgrad(fcn, x2,P1,P2,P3,P4,P5,P6,P7,P8,...</span>
0130 <span class="comment">% P9,P10,P11,P12,P13);</span>
0131 <span class="keyword">else</span>
0132 [g2 badg2] = feval(grad,x2,varargin{:});
0133 <span class="comment">%ARGLIST</span>
0134 <span class="comment">%[g2 badg2] = feval(grad,x2,P1,P2,P3,P4,P5,P6,P7,P8,...</span>
0135 <span class="comment">% P9,P10,P11,P12,P13);</span>
0136 <span class="keyword">end</span>
0137 wall2=badg2;
0138 <span class="comment">% g2</span>
0139 badg2
0140 save g2 g2 x2 f2 varargin
0141 <span class="comment">%ARGLIST</span>
0142 <span class="comment">%save g2 g2 x2 f2 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13;</span>
0143 <span class="keyword">end</span>
0144 <span class="keyword">if</span> wall2
0145 disp(<span class="string">'Cliff again. Try traversing'</span>)
0146 <span class="keyword">if</span> norm(x2-x1) &lt; 1e-13
0147 f3=f; x3=x; badg3=1;retcode3=101;
0148 <span class="keyword">else</span>
0149 gcliff=((f2-f1)/((norm(x2-x1))^2))*(x2-x1);
0150 <span class="keyword">if</span>(size(x0,2)&gt;1), gcliff=gcliff', <span class="keyword">end</span>
0151 [f3 x3 fc retcode3] = <a href="csminit.html" class="code" title="function [fhat,xhat,fcount,retcode] = csminit(fcn,x0,f0,g0,badg,H0,varargin)">csminit</a>(fcn,x,f,gcliff,0,eye(nx),varargin{:});
0152 <span class="comment">%ARGLIST</span>
0153 <span class="comment">%[f3 x3 fc retcode3] = csminit(fcn,x,f,gcliff,0,eye(nx),P1,P2,P3,...</span>
0154 <span class="comment">% P4,P5,P6,P7,P8,...</span>
0155 <span class="comment">% P9,P10,P11,P12,P13);</span>
0156 fcount = fcount+fc; <span class="comment">% put by Jinill</span>
0157 <span class="keyword">if</span> retcode3==2 | retcode3==4
0158 wall3=1; badg3=1;
0159 <span class="keyword">else</span>
0160 <span class="keyword">if</span> NumGrad
0161 [g3 badg3] = <a href="numgrad.html" class="code" title="function [g, badg] = numgrad(fcn,x,varargin)">numgrad</a>(fcn, x3,varargin{:});
0162 <span class="comment">%ARGLIST</span>
0163 <span class="comment">%[g3 badg3] = numgrad(fcn, x3,P1,P2,P3,P4,P5,P6,P7,P8,...</span>
0164 <span class="comment">% P9,P10,P11,P12,P13);</span>
0165 <span class="keyword">else</span>
0166 [g3 badg3] = feval(grad,x3,varargin{:});
0167 <span class="comment">%ARGLIST</span>
0168 <span class="comment">%[g3 badg3] = feval(grad,x3,P1,P2,P3,P4,P5,P6,P7,P8,...</span>
0169 <span class="comment">% P9,P10,P11,P12,P13);</span>
0170 <span class="keyword">end</span>
0171 wall3=badg3;
0172 <span class="comment">% g3</span>
0173 badg3
0174 save g3 g3 x3 f3 varargin;
0175 <span class="comment">%ARGLIST</span>
0176 <span class="comment">%save g3 g3 x3 f3 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13;</span>
0177 <span class="keyword">end</span>
0178 <span class="keyword">end</span>
0179 <span class="keyword">else</span>
0180 f3=f; x3=x; badg3=1; retcode3=101;
0181 <span class="keyword">end</span>
0182 <span class="keyword">else</span>
0183 f3=f; x3=x; badg3=1;retcode3=101;
0184 <span class="keyword">end</span>
0185 <span class="keyword">else</span>
0186 <span class="comment">% normal iteration, no walls, or else we're finished here.</span>
0187 f2=f; f3=f; badg2=1; badg3=1; retcode2=101; retcode3=101;
0188 <span class="keyword">end</span>
0189 <span class="keyword">else</span>
0190 f2=f;f3=f;f1=f;retcode2=retcode1;retcode3=retcode1;
0191 <span class="keyword">end</span>
0192 <span class="comment">%how to pick gh and xh</span>
0193 <span class="keyword">if</span> f3 &lt; f - crit &amp; badg3==0
0194 ih=3
0195 fh=f3;xh=x3;gh=g3;badgh=badg3;retcodeh=retcode3;
0196 <span class="keyword">elseif</span> f2 &lt; f - crit &amp; badg2==0
0197 ih=2
0198 fh=f2;xh=x2;gh=g2;badgh=badg2;retcodeh=retcode2;
0199 <span class="keyword">elseif</span> f1 &lt; f - crit &amp; badg1==0
0200 ih=1
0201 fh=f1;xh=x1;gh=g1;badgh=badg1;retcodeh=retcode1;
0202 <span class="keyword">else</span>
0203 [fh,ih] = min([f1,f2,f3]);
0204 disp(sprintf(<span class="string">'ih = %d'</span>,ih))
0205 <span class="comment">%eval(['xh=x' num2str(ih) ';'])</span>
0206 <span class="keyword">switch</span> ih
0207 <span class="keyword">case</span> 1
0208 xh=x1;
0209 <span class="keyword">case</span> 2
0210 xh=x2;
0211 <span class="keyword">case</span> 3
0212 xh=x3;
0213 <span class="keyword">end</span> <span class="comment">%case</span>
0214 <span class="comment">%eval(['gh=g' num2str(ih) ';'])</span>
0215 <span class="comment">%eval(['retcodeh=retcode' num2str(ih) ';'])</span>
0216 retcodei=[retcode1,retcode2,retcode3];
0217 retcodeh=retcodei(ih);
0218 <span class="keyword">if</span> exist(<span class="string">'gh'</span>)
0219 nogh=isempty(gh);
0220 <span class="keyword">else</span>
0221 nogh=1;
0222 <span class="keyword">end</span>
0223 <span class="keyword">if</span> nogh
0224 <span class="keyword">if</span> NumGrad
0225 [gh badgh] = <a href="numgrad.html" class="code" title="function [g, badg] = numgrad(fcn,x,varargin)">numgrad</a>(fcn,xh,varargin{:});
0226 <span class="keyword">else</span>
0227 [gh badgh] = feval(grad, xh,varargin{:});
0228 <span class="keyword">end</span>
0229 <span class="keyword">end</span>
0230 badgh=1;
0231 <span class="keyword">end</span>
0232 <span class="comment">%end of picking</span>
0233 <span class="comment">%ih</span>
0234 <span class="comment">%fh</span>
0235 <span class="comment">%xh</span>
0236 <span class="comment">%gh</span>
0237 <span class="comment">%badgh</span>
0238 stuck = (abs(fh-f) &lt; crit);
0239 <span class="keyword">if</span> (~badg)&amp;(~badgh)&amp;(~stuck)
0240 H = <a href="bfgsi.html" class="code" title="function H = bfgsi(H0,dg,dx)">bfgsi</a>(H,gh-g,xh-x);
0241 <span class="keyword">end</span>
0242 <span class="keyword">if</span> Verbose
0243 disp(<span class="string">'----'</span>)
0244 disp(sprintf(<span class="string">'Improvement on iteration %d = %18.9f'</span>,itct,f-fh))
0245 <span class="keyword">end</span>
0246 <span class="comment">% if Verbose</span>
0247 <span class="keyword">if</span> itct &gt; nit
0248 disp(<span class="string">'iteration count termination'</span>)
0249 done = 1;
0250 <span class="keyword">elseif</span> stuck
0251 disp(<span class="string">'improvement &lt; crit termination'</span>)
0252 done = 1;
0253 <span class="keyword">end</span>
0254 rc=retcodeh;
0255 <span class="keyword">if</span> rc == 1
0256 disp(<span class="string">'zero gradient'</span>)
0257 <span class="keyword">elseif</span> rc == 6
0258 disp(<span class="string">'smallest step still improving too slow, reversed gradient'</span>)
0259 <span class="keyword">elseif</span> rc == 5
0260 disp(<span class="string">'largest step still improving too fast'</span>)
0261 <span class="keyword">elseif</span> (rc == 4) | (rc==2)
0262 disp(<span class="string">'back and forth on step length never finished'</span>)
0263 <span class="keyword">elseif</span> rc == 3
0264 disp(<span class="string">'smallest step still improving too slow'</span>)
0265 <span class="keyword">elseif</span> rc == 7
0266 disp(<span class="string">'warning: possible inaccuracy in H matrix'</span>)
0267 <span class="keyword">end</span>
0268 <span class="comment">% end</span>
0269 f=fh;
0270 x=xh;
0271 g=gh;
0272 badg=badgh;
0273 <span class="keyword">end</span>
0274 <span class="comment">% what about making an m-file of 10 lines including numgrad.m</span>
0275 <span class="comment">% since it appears three times in csminwel.m</span></pre></div>
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<title>Description of cumplot</title>
<meta name="keywords" content="cumplot">
<meta name="description" content="function h =cumplot(x)">
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<h1>cumplot
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function h =cumplot(x)</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function h = cumplot(x); </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment">function h =cumplot(x)
Copyright (C) 2005 Marco Ratto</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="filt_mc_.html" class="code" title="function [rmse_MC, ixx] = filt_mc_(vvarvecm, loadSA, pfilt, alpha, alpha2, OutDir, istart, alphaPC)">filt_mc_</a> copyright Marco Ratto 2006</li><li><a href="stab_map_1.html" class="code" title="function [proba, dproba] = stab_map_1(lpmat, ibehaviour, inonbehaviour, aname, iplot, ipar, dirname)">stab_map_1</a> function [proba, dproba] = stab_map_1(lpmat, ibehaviour, inonbehaviour, aname, iplot, ipar, dirname)</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function h = cumplot(x);</a>
0002 <span class="comment">%function h =cumplot(x)</span>
0003 <span class="comment">% Copyright (C) 2005 Marco Ratto</span>
0004
0005
0006 n=length(x);
0007 x=[-inf; sort(x); Inf];
0008 y=[0:n n]./n;
0009 h0 = stairs(x,y);
0010 grid on,
0011
0012 <span class="keyword">if</span> nargout,
0013 h=h0;
0014 <span class="keyword">end</span></pre></div>
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<title>Description of datatomfile</title>
<meta name="keywords" content="datatomfile">
<meta name="description" content="Copyright (C) 2001 Michel Juillard">
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
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<h1>datatomfile
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>Copyright (C) 2001 Michel Juillard</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function datatomfile (s,var_list) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> Copyright (C) 2001 Michel Juillard
DYNATYPE : DYNATYPE ( [ 'filename' ] )
This optional command saves the simulation
results in a text file. The name of each
variable preceeds the corresponding results.
This command must follow SIMUL.</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <span class="comment">% Copyright (C) 2001 Michel Juillard</span>
0002 <span class="comment">%</span>
0003 <a name="_sub0" href="#_subfunctions" class="code">function datatomfile (s,var_list)</a>
0004 <span class="comment">% DYNATYPE : DYNATYPE ( [ 'filename' ] )</span>
0005 <span class="comment">% This optional command saves the simulation</span>
0006 <span class="comment">% results in a text file. The name of each</span>
0007 <span class="comment">% variable preceeds the corresponding results.</span>
0008 <span class="comment">% This command must follow SIMUL.</span>
0009
0010 <span class="keyword">global</span> M_ oo_
0011
0012 <span class="comment">%fid=fopen([s,'.m'],'w') ;</span>
0013 sm=[s,<span class="string">'.m'</span>];
0014 fid=fopen(sm,<span class="string">'w'</span>) ;
0015
0016 n = size(var_list,1);
0017 <span class="keyword">if</span> n == 0
0018 n = M_.endo_nbr;
0019 ivar = [1:n]';
0020 var_list = M_.endo_names;
0021 <span class="keyword">else</span>
0022 ivar=zeros(n,1);
0023 <span class="keyword">for</span> i=1:n
0024 i_tmp = strmatch(var_list(i,:),M_.endo_names,<span class="string">'exact'</span>);
0025 <span class="keyword">if</span> isempty(i_tmp)
0026 error ([<span class="string">'One of the specified variables does not exist'</span>]) ;
0027 <span class="keyword">else</span>
0028 ivar(i) = i_tmp;
0029 <span class="keyword">end</span>
0030 <span class="keyword">end</span>
0031 <span class="keyword">end</span>
0032
0033
0034 <span class="keyword">for</span> i = 1:n
0035 fprintf(fid,[M_.endo_names(ivar(i),:), <span class="string">'=['</span>],<span class="string">'\n'</span>) ;
0036 fprintf(fid,<span class="string">'\n'</span>) ;
0037 fprintf(fid,<span class="string">'%15.8g\n'</span>,oo_.endo_simul(ivar(i),:)') ;
0038 fprintf(fid,<span class="string">'\n'</span>) ;
0039 fprintf(fid,<span class="string">'];\n'</span>) ;
0040 fprintf(fid,<span class="string">'\n'</span>) ;
0041 <span class="keyword">end</span>
0042 fclose(fid) ;
0043</pre></div>
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<title>Description of dbeta</title>
<meta name="keywords" content="dbeta">
<meta name="description" content="DBETA The beta density function">
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<h1>dbeta
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>DBETA The beta density function</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function d = dbeta(x,a,b) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment">DBETA The beta density function
f = dbeta(x,a,b)</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="qbeta.html" class="code" title="function x = qbeta(p,a,b)">qbeta</a> QBETA The beta inverse distribution function</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function d = dbeta(x,a,b)</a>
0002 <span class="comment">%DBETA The beta density function</span>
0003 <span class="comment">%</span>
0004 <span class="comment">% f = dbeta(x,a,b)</span>
0005
0006 <span class="comment">% Anders Holtsberg, 18-11-93</span>
0007 <span class="comment">% Copyright (c) Anders Holtsberg</span>
0008
0009 <span class="keyword">if</span> any(any((a&lt;=0)|(b&lt;=0)))
0010 error(<span class="string">'Parameter a or b is nonpositive'</span>)
0011 <span class="keyword">end</span>
0012
0013 I = find((x&lt;0)|(x&gt;1));
0014
0015 d = x.^(a-1) .* (1-x).^(b-1) ./ beta(a,b);
0016 d(I) = 0*I;</pre></div>
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<title>Description of dcompare</title>
<meta name="keywords" content="dcompare">
<meta name="description" content="Copyright (C) 2001 Michel Juillard">
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
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<h1>dcompare
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>Copyright (C) 2001 Michel Juillard</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function dcompare(s1) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> Copyright (C) 2001 Michel Juillard</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="bseastr.html" class="code" title="function x = bseastr(s1,s2)">bseastr</a> Copyright (C) 2001 Michel Juillard</li><li><a href="ftest.html" class="code" title="function ftest (s1,s2)">ftest</a> Copyright (C) 2001 Michel Juillard</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <span class="comment">% Copyright (C) 2001 Michel Juillard</span>
0002 <span class="comment">%</span>
0003 <a name="_sub0" href="#_subfunctions" class="code">function dcompare(s1)</a>
0004
0005 <span class="keyword">global</span> options_ nvx nvy x y lag1
0006
0007 <a href="ftest.html" class="code" title="function ftest (s1,s2)">ftest</a>(s1,0) ;
0008
0009 i = [lag1(1):size(x,2)-lag1(2)+1]' ;
0010
0011 <span class="keyword">if</span> size(options_.smpl,1) == 1
0012 error([<span class="string">'DSAMPLE not specified.'</span>]) ;
0013 <span class="keyword">end</span>
0014
0015 <span class="keyword">if</span> options_.smpl(3) &gt; 0
0016 <span class="keyword">if</span> options_.smpl(3) == 2
0017 <span class="keyword">if</span> options_.smpl(1)&lt;0 | options_.smpl(2)&gt;size(x,2)-lag1(2)
0018 error (<span class="string">'Wrong sample.'</span>) ;
0019 <span class="keyword">end</span>
0020 i = [options_.smpl(1)+lag1(1):options_.smpl(2)+lag1(1)]' ;
0021 <span class="keyword">elseif</span> options_.smpl(3) == 1
0022 <span class="keyword">if</span> options_.smpl(1)&gt;size(x,2)-lag1(2)
0023 error (<span class="string">'Wrong sample.'</span>) ;
0024 <span class="keyword">end</span>
0025 i = [lag1(1):options_.smpl(1)+lag1(1)]' ;
0026 <span class="keyword">end</span>
0027 <span class="keyword">end</span>
0028
0029 j = <a href="bseastr.html" class="code" title="function x = bseastr(s1,s2)">bseastr</a>(nvx,nvy) ;
0030
0031 <span class="keyword">if</span> stop
0032 <span class="keyword">return</span> ;
0033 <span class="keyword">end</span>
0034
0035 z = mean(mean(abs(x(j,i)-y(j,i)))) ;
0036
0037 disp ([<span class="string">'The mean absolute difference between set '</span> s1(1,:) <span class="string">'and set '</span> s1(2,:)]) ;
0038 disp ([<span class="string">'is : '</span> num2str(z)]) ;
0039 <span class="keyword">return</span> ;
0040
0041</pre></div>
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<title>Description of dgamma</title>
<meta name="keywords" content="dgamma">
<meta name="description" content="DGAMMA The gamma density function">
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<h1>dgamma
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>DGAMMA The gamma density function</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function f = dgamma(x,a,b) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment">DGAMMA The gamma density function
f = dgamma(x,a)</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="qgamma.html" class="code" title="function x = qgamma(p,a)">qgamma</a> QGAMMA The gamma inverse distribution function</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function f = dgamma(x,a,b)</a>
0002 <span class="comment">%DGAMMA The gamma density function</span>
0003 <span class="comment">%</span>
0004 <span class="comment">% f = dgamma(x,a)</span>
0005
0006 <span class="comment">% Anders Holtsberg, 18-11-93</span>
0007 <span class="comment">% Copyright (c) Anders Holtsberg</span>
0008
0009 <span class="keyword">if</span> any(any(a&lt;=0))
0010 error(<span class="string">'Parameter a is wrong'</span>)
0011 <span class="keyword">end</span>
0012
0013 f = (x./b) .^ (a-1) .* exp(-x./b) ./ (b.*gamma(a));
0014 I0 = find(x&lt;0);
0015 f(I0) = zeros(size(I0));</pre></div>
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<title>Description of disp_dr</title>
<meta name="keywords" content="disp_dr">
<meta name="description" content="Copyright (C) 2001 Michel Juillard">
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
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<h1>disp_dr
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>Copyright (C) 2001 Michel Juillard</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function disp_dr(dr,order,var_list) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> Copyright (C) 2001 Michel Juillard</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="olr.html" class="code" title="function olr(var_list,olr_inst,obj_var,W)">olr</a> Copyright (C) 2001 Michel Juillard</li><li><a href="osr.html" class="code" title="function osr(var_list,params,W)">osr</a> Copyright (C) 2001 Michel Juillard</li><li><a href="stoch_simul.html" class="code" title="function info=stoch_simul(var_list)">stoch_simul</a> Copyright (C) 2001 Michel Juillard</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <span class="comment">% Copyright (C) 2001 Michel Juillard</span>
0002 <span class="comment">%</span>
0003 <a name="_sub0" href="#_subfunctions" class="code">function disp_dr(dr,order,var_list)</a>
0004 <span class="keyword">global</span> M_
0005
0006 nx =size(dr.ghx,2);
0007 nu =size(dr.ghu,2);
0008 k = find(dr.kstate(:,2) &lt;= M_.maximum_lag+1);
0009 klag = dr.kstate(k,[1 2]);
0010
0011 k1 = dr.order_var;
0012
0013 nvar = size(var_list,1);
0014 <span class="keyword">if</span> nvar == 0
0015 nvar = length(k1);
0016 ivar = [1:nvar];
0017 <span class="keyword">else</span>
0018 ivar=zeros(nvar,1);
0019 <span class="keyword">for</span> i=1:nvar
0020 i_tmp = strmatch(var_list(i,:),M_.endo_names(k1,:),<span class="string">'exact'</span>);
0021 <span class="keyword">if</span> isempty(i_tmp)
0022 disp(var_list(i,:));
0023 error ([<span class="string">'One of the variable specified does not exist'</span>]) ;
0024 <span class="keyword">else</span>
0025 ivar(i) = i_tmp;
0026 <span class="keyword">end</span>
0027 <span class="keyword">end</span>
0028 <span class="keyword">end</span>
0029 disp(<span class="string">'POLICY AND TRANSITION FUNCTIONS'</span>)
0030 <span class="comment">% variable names</span>
0031 str = <span class="string">' '</span>;
0032 <span class="keyword">for</span> i=1:nvar
0033 str = [str sprintf(<span class="string">'%16s'</span>,M_.endo_names(k1(ivar(i)),:))];
0034 <span class="keyword">end</span>
0035 disp(str);
0036 <span class="comment">%</span>
0037 <span class="comment">% constant</span>
0038 <span class="comment">%</span>
0039 str = <span class="string">'Constant '</span>;
0040 flag = 0;
0041 <span class="keyword">for</span> i=1:nvar
0042 x = dr.ys(k1(ivar(i)));
0043 <span class="keyword">if</span> order &gt; 1
0044 x = x + dr.ghs2(ivar(i))/2;
0045 <span class="keyword">end</span>
0046 <span class="keyword">if</span> abs(x) &gt; 1e-6
0047 flag = 1;
0048 str = [str sprintf(<span class="string">'%16.6f'</span>,x)];
0049 <span class="keyword">else</span>
0050 str = [str <span class="string">' 0'</span>];
0051 <span class="keyword">end</span>
0052 <span class="keyword">end</span>
0053 <span class="keyword">if</span> flag
0054 disp(str)
0055 <span class="keyword">end</span>
0056 <span class="keyword">if</span> order &gt; 1
0057 str = <span class="string">'(correction) '</span>;
0058 flag = 0;
0059 <span class="keyword">for</span> i=1:nvar
0060 x = dr.ghs2(ivar(i))/2;
0061 <span class="keyword">if</span> abs(x) &gt; 1e-6
0062 flag = 1;
0063 str = [str sprintf(<span class="string">'%16.6f'</span>,x)];
0064 <span class="keyword">else</span>
0065 str = [str <span class="string">' 0'</span>];
0066 <span class="keyword">end</span>
0067 <span class="keyword">end</span>
0068 <span class="keyword">if</span> flag
0069 disp(str)
0070 <span class="keyword">end</span>
0071 <span class="keyword">end</span>
0072 <span class="comment">%</span>
0073 <span class="comment">% ghx</span>
0074 <span class="comment">%</span>
0075 <span class="keyword">for</span> k=1:nx
0076 flag = 0;
0077 str1 = sprintf(<span class="string">'%s(%d)'</span>,M_.endo_names(k1(klag(k,1)),:),klag(k,2)-M_.maximum_lag-2);
0078 str = sprintf(<span class="string">'%-20s'</span>,str1);
0079 <span class="keyword">for</span> i=1:nvar
0080 x = dr.ghx(ivar(i),k);
0081 <span class="keyword">if</span> abs(x) &gt; 1e-6
0082 flag = 1;
0083 str = [str sprintf(<span class="string">'%16.6f'</span>,x)];
0084 <span class="keyword">else</span>
0085 str = [str <span class="string">' 0'</span>];
0086 <span class="keyword">end</span>
0087 <span class="keyword">end</span>
0088 <span class="keyword">if</span> flag
0089 disp(str)
0090 <span class="keyword">end</span>
0091 <span class="keyword">end</span>
0092 <span class="comment">%</span>
0093 <span class="comment">% ghu</span>
0094 <span class="comment">%</span>
0095 <span class="keyword">for</span> k=1:nu
0096 flag = 0;
0097 str = sprintf(<span class="string">'%-20s'</span>,M_.exo_names(k,:));
0098 <span class="keyword">for</span> i=1:nvar
0099 x = dr.ghu(ivar(i),k);
0100 <span class="keyword">if</span> abs(x) &gt; 1e-6
0101 flag = 1;
0102 str = [str sprintf(<span class="string">'%16.6f'</span>,x)];
0103 <span class="keyword">else</span>
0104 str = [str <span class="string">' 0'</span>];
0105 <span class="keyword">end</span>
0106 <span class="keyword">end</span>
0107 <span class="keyword">if</span> flag
0108 disp(str)
0109 <span class="keyword">end</span>
0110 <span class="keyword">end</span>
0111
0112 <span class="keyword">if</span> order &gt; 1
0113 <span class="comment">% ghxx</span>
0114 <span class="keyword">for</span> k = 1:nx
0115 <span class="keyword">for</span> j = 1:k
0116 flag = 0;
0117 str1 = sprintf(<span class="string">'%s(%d),%s(%d)'</span>,M_.endo_names(k1(klag(k,1)),:),klag(k,2)-M_.maximum_lag-2, <span class="keyword">...</span>
0118 M_.endo_names(k1(klag(j,1)),:),klag(j,2)-M_.maximum_lag-2);
0119 str = sprintf(<span class="string">'%-20s'</span>,str1);
0120 <span class="keyword">for</span> i=1:nvar
0121 <span class="keyword">if</span> k == j
0122 x = dr.ghxx(ivar(i),(k-1)*nx+j)/2;
0123 <span class="keyword">else</span>
0124 x = dr.ghxx(ivar(i),(k-1)*nx+j);
0125 <span class="keyword">end</span>
0126 <span class="keyword">if</span> abs(x) &gt; 1e-6
0127 flag = 1;
0128 str = [str sprintf(<span class="string">'%16.6f'</span>,x)];
0129 <span class="keyword">else</span>
0130 str = [str <span class="string">' 0'</span>];
0131 <span class="keyword">end</span>
0132 <span class="keyword">end</span>
0133 <span class="keyword">if</span> flag
0134 disp(str)
0135 <span class="keyword">end</span>
0136 <span class="keyword">end</span>
0137 <span class="keyword">end</span>
0138 <span class="comment">%</span>
0139 <span class="comment">% ghuu</span>
0140 <span class="comment">%</span>
0141 <span class="keyword">for</span> k = 1:nu
0142 <span class="keyword">for</span> j = 1:k
0143 flag = 0;
0144 str = sprintf(<span class="string">'%-20s'</span>,[M_.exo_names(k,:) <span class="string">','</span> M_.exo_names(j,:)] );
0145 <span class="keyword">for</span> i=1:nvar
0146 <span class="keyword">if</span> k == j
0147 x = dr.ghuu(ivar(i),(k-1)*nu+j)/2;
0148 <span class="keyword">else</span>
0149 x = dr.ghuu(ivar(i),(k-1)*nu+j);
0150 <span class="keyword">end</span>
0151 <span class="keyword">if</span> abs(x) &gt; 1e-6
0152 flag = 1;
0153 str = [str sprintf(<span class="string">'%16.6f'</span>,x)];
0154 <span class="keyword">else</span>
0155 str = [str <span class="string">' 0'</span>];
0156 <span class="keyword">end</span>
0157 <span class="keyword">end</span>
0158 <span class="keyword">if</span> flag
0159 disp(str)
0160 <span class="keyword">end</span>
0161 <span class="keyword">end</span>
0162 <span class="keyword">end</span>
0163 <span class="comment">%</span>
0164 <span class="comment">% ghxu</span>
0165 <span class="comment">%</span>
0166 <span class="keyword">for</span> k = 1:nx
0167 <span class="keyword">for</span> j = 1:nu
0168 flag = 0;
0169 str1 = sprintf(<span class="string">'%s(%d),%s'</span>,M_.endo_names(k1(klag(k,1)),:),klag(k,2)-M_.maximum_lag-2, <span class="keyword">...</span>
0170 M_.exo_names(j,:));
0171 str = sprintf(<span class="string">'%-20s'</span>,str1);
0172 <span class="keyword">for</span> i=1:nvar
0173 x = dr.ghxu(ivar(i),(k-1)*nu+j);
0174 <span class="keyword">if</span> abs(x) &gt; 1e-6
0175 flag = 1;
0176 str = [str sprintf(<span class="string">'%16.6f'</span>,x)];
0177 <span class="keyword">else</span>
0178 str = [str <span class="string">' 0'</span>];
0179 <span class="keyword">end</span>
0180 <span class="keyword">end</span>
0181 <span class="keyword">if</span> flag
0182 disp(str)
0183 <span class="keyword">end</span>
0184 <span class="keyword">end</span>
0185 <span class="keyword">end</span>
0186 <span class="keyword">end</span>
0187
0188 <span class="comment">% $$$ dr.ghx</span>
0189 <span class="comment">% $$$ dr.ghu</span>
0190 <span class="comment">% $$$ dr.ghxx</span>
0191 <span class="comment">% $$$ dr.ghuu</span>
0192 <span class="comment">% $$$ dr.ghxu</span>
0193
0194 <span class="comment">% 01/08/2001 MJ added test for order in printing quadratic terms</span>
0195 <span class="comment">% 02/21/2001 MJ pass all variable names through deblank()</span>
0196 <span class="comment">% 02/21/2001 MJ changed from f to g format to write numbers</span>
0197 <span class="comment">% 10/09/2002 MJ corrected error on constant whith subset of variables</span>
0198
0199
0200</pre></div>
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<html>
<head>
<title>Description of disp_moments</title>
<meta name="keywords" content="disp_moments">
<meta name="description" content="Copyright (C) 2001 Michel Juillard">
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
<meta name="generator" content="m2html &copy; 2003 Guillaume Flandin">
<meta name="robots" content="index, follow">
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<body>
<a name="_top"></a>
<div><a href="../index.html">Home</a> &gt; <a href="index.html">.</a> &gt; disp_moments.m</div>
<!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png">&nbsp;Master index</a></td>
<td align="right"><a href="index.html">Index for .&nbsp;<img alt=">" border="0" src="../right.png"></a></td></tr></table>-->
<h1>disp_moments
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>Copyright (C) 2001 Michel Juillard</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function disp_moments(y,var_list) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> Copyright (C) 2001 Michel Juillard</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a> </li><li><a href="table.html" class="code" title="function table(title,headers,labels,values,label_width,val_width,val_precis)">table</a> Copyright (C) 2002 Michel Juillard</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="olr.html" class="code" title="function olr(var_list,olr_inst,obj_var,W)">olr</a> Copyright (C) 2001 Michel Juillard</li><li><a href="osr.html" class="code" title="function osr(var_list,params,W)">osr</a> Copyright (C) 2001 Michel Juillard</li><li><a href="stoch_simul.html" class="code" title="function info=stoch_simul(var_list)">stoch_simul</a> Copyright (C) 2001 Michel Juillard</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <span class="comment">% Copyright (C) 2001 Michel Juillard</span>
0002 <span class="comment">%</span>
0003 <a name="_sub0" href="#_subfunctions" class="code">function disp_moments(y,var_list)</a>
0004 <span class="keyword">global</span> M_ options_ oo_
0005
0006 warning off
0007 nvar = size(var_list,1);
0008 <span class="keyword">if</span> nvar == 0
0009 nvar = M_.endo_nbr;
0010 ivar = [1:nvar]';
0011 <span class="keyword">else</span>
0012 ivar=zeros(nvar,1);
0013 <span class="keyword">for</span> i=1:nvar
0014 i_tmp = strmatch(var_list(i,:),M_.endo_names,<span class="string">'exact'</span>);
0015 <span class="keyword">if</span> isempty(i_tmp)
0016 error ([<span class="string">'One of the variable specified does not exist'</span>]) ;
0017 <span class="keyword">else</span>
0018 ivar(i) = i_tmp;
0019 <span class="keyword">end</span>
0020 <span class="keyword">end</span>
0021 <span class="keyword">end</span>
0022 y = y(ivar,options_.drop+M_.maximum_lag+1:end)';
0023
0024 m = mean(y);
0025 y = y - repmat(m,size(y,1),1);
0026 s2 = mean(y.*y);
0027 s = sqrt(s2);
0028 oo_.mean = m;
0029 oo_.var = y'*y/size(y,1);
0030
0031 labels = deblank(M_.endo_names(ivar,:));
0032
0033 <span class="keyword">if</span> options_.nomoments == 0
0034 z = [ m' s' s2' (mean(y.^3)./s2.^1.5)' (mean(y.^4)./(s2.*s2)-3)' ];
0035
0036 title=<span class="string">'MOMENTS OF SIMULATED VARIABLES'</span>;
0037 <span class="keyword">if</span> options_.hp_filter &gt; 1
0038 title = [title <span class="string">' (HP filter, lambda = '</span> <span class="keyword">...</span>
0039 int2str(options_.hp_filter) <span class="string">')'</span>];
0040 <span class="keyword">end</span>
0041 headers=strvcat(<span class="string">'VARIABLE'</span>,<span class="string">'MEAN'</span>,<span class="string">'STD. DEV.'</span>,<span class="string">'VARIANCE'</span>,<span class="string">'SKEWNESS'</span>, <span class="keyword">...</span>
0042 <span class="string">'KURTOSIS'</span>);
0043 <a href="table.html" class="code" title="function table(title,headers,labels,values,label_width,val_width,val_precis)">table</a>(title,headers,labels,z,size(labels,2)+2,16,6);
0044 <span class="keyword">end</span>
0045
0046 <span class="keyword">if</span> options_.nocorr == 0
0047 corr = (y'*y/size(y,1))./(s'*s);
0048 title = <span class="string">'CORRELATION OF SIMULATED VARIABLES'</span>;
0049 <span class="keyword">if</span> options_.hp_filter &gt; 1
0050 title = [title <span class="string">' (HP filter, lambda = '</span> <span class="keyword">...</span>
0051 int2str(options_.hp_filter) <span class="string">')'</span>];
0052 <span class="keyword">end</span>
0053 headers = strvcat(<span class="string">'VARIABLE'</span>,M_.endo_names(ivar,:));
0054 <a href="table.html" class="code" title="function table(title,headers,labels,values,label_width,val_width,val_precis)">table</a>(title,headers,labels,corr,size(labels,2)+2,8,4);
0055 <span class="keyword">end</span>
0056
0057 ar = options_.ar;
0058 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'ar'</span>,5);
0059 ar = options_.ar;
0060 <span class="keyword">if</span> ar &gt; 0
0061 autocorr = [];
0062 <span class="keyword">for</span> i=1:ar
0063 oo_.autocorr{i} = y(ar+1:<span class="keyword">end</span>,:)'*y(ar+1-i:end-i,:)./((size(y,1)-ar)*s'*s);
0064 autocorr = [ autocorr diag(oo_.autocorr{i}) ];
0065 <span class="keyword">end</span>
0066 title = <span class="string">'AUTOCORRELATION OF SIMULATED VARIABLES'</span>;
0067 <span class="keyword">if</span> options_.hp_filter &gt; 1
0068 title = [title <span class="string">' (HP filter, lambda = '</span> <span class="keyword">...</span>
0069 int2str(options_.hp_filter) <span class="string">')'</span>];
0070 <span class="keyword">end</span>
0071 headers = strvcat(<span class="string">'VARIABLE'</span>,int2str([1:ar]'));
0072 <a href="table.html" class="code" title="function table(title,headers,labels,values,label_width,val_width,val_precis)">table</a>(title,headers,labels,autocorr,size(labels,2)+2,8,4);
0073 <span class="keyword">end</span>
0074
0075 warning on
0076 <span class="comment">% 10/03/02 MJ corrected order std. dev var in printed report.</span>
0077 <span class="comment">% 01/02/03 MJ added correlation and autocorrelation</span>
0078 <span class="comment">% 01/19/03 MJ corrected variable name truncation</span>
0079 <span class="comment">% 02/18/03 MJ added subtitle for HP filter</span>
0080 <span class="comment">% 03/02/03 MJ added M_.maximum_lag to the number of entries of y</span>
0081 <span class="comment">% 04/28/03 MJ modified handling of options_</span>
0082 <span class="comment">% 06/23/03 MJ added warning off</span></pre></div>
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<title>Description of disp_th_moments</title>
<meta name="keywords" content="disp_th_moments">
<meta name="description" content="Copyright (C) 2001 Michel Juillard">
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
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<body>
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<!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png">&nbsp;Master index</a></td>
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<h1>disp_th_moments
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>Copyright (C) 2001 Michel Juillard</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function disp_th_moments(dr,var_list) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> Copyright (C) 2001 Michel Juillard</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="table.html" class="code" title="function table(title,headers,labels,values,label_width,val_width,val_precis)">table</a> Copyright (C) 2002 Michel Juillard</li><li><a href="th_autocovariances.html" class="code" title="function [Gamma_y,ivar]=th_autocovariances(dr,ivar)">th_autocovariances</a> Copyright (C) 2001 Michel Juillard</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="olr.html" class="code" title="function olr(var_list,olr_inst,obj_var,W)">olr</a> Copyright (C) 2001 Michel Juillard</li><li><a href="osr.html" class="code" title="function osr(var_list,params,W)">osr</a> Copyright (C) 2001 Michel Juillard</li><li><a href="stoch_simul.html" class="code" title="function info=stoch_simul(var_list)">stoch_simul</a> Copyright (C) 2001 Michel Juillard</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <span class="comment">% Copyright (C) 2001 Michel Juillard</span>
0002 <span class="comment">%</span>
0003 <a name="_sub0" href="#_subfunctions" class="code">function disp_th_moments(dr,var_list)</a>
0004 <span class="keyword">global</span> M_ oo_ options_
0005
0006 nvar = size(var_list,1);
0007 <span class="keyword">if</span> nvar == 0
0008 nvar = length(dr.order_var);
0009 ivar = [1:nvar]';
0010 <span class="keyword">else</span>
0011 ivar=zeros(nvar,1);
0012 <span class="keyword">for</span> i=1:nvar
0013 i_tmp = strmatch(var_list(i,:),M_.endo_names,<span class="string">'exact'</span>);
0014 <span class="keyword">if</span> isempty(i_tmp)
0015 error ([<span class="string">'One of the variable specified does not exist'</span>]) ;
0016 <span class="keyword">else</span>
0017 ivar(i) = i_tmp;
0018 <span class="keyword">end</span>
0019 <span class="keyword">end</span>
0020 <span class="keyword">end</span>
0021
0022 m = dr.ys(ivar);
0023
0024 oo_.gamma_y = <a href="th_autocovariances.html" class="code" title="function [Gamma_y,ivar]=th_autocovariances(dr,ivar)">th_autocovariances</a>(dr,ivar);
0025
0026 i1 = find(abs(diag(oo_.gamma_y{1})) &gt; 1e-12);
0027 s2 = diag(oo_.gamma_y{1});
0028 sd = sqrt(s2);
0029 <span class="keyword">if</span> options_.order == 2
0030 m = m+oo_.gamma_y{options_.ar+3};
0031 <span class="keyword">end</span>
0032
0033 z = [ m sd s2 ];
0034 oo_.mean = m;
0035 oo_.var = oo_.gamma_y{1};
0036
0037 lh = size(deblank(M_.endo_names(ivar,:)),2)+2;
0038 <span class="keyword">if</span> options_.nomoments == 0
0039 title=<span class="string">'THEORETICAL MOMENTS'</span>;
0040 <span class="keyword">if</span> options_.hp_filter == 1
0041 title = [title <span class="string">' (HP filter, lambda = '</span> int2str(options_.hp_filter) <span class="string">')'</span>];
0042 <span class="keyword">end</span>
0043 headers=strvcat(<span class="string">'VARIABLE'</span>,<span class="string">'MEAN'</span>,<span class="string">'STD. DEV.'</span>,<span class="string">'VARIANCE'</span>);
0044 <a href="table.html" class="code" title="function table(title,headers,labels,values,label_width,val_width,val_precis)">table</a>(title,headers,deblank(M_.endo_names(ivar,:)),z,lh,11,4);
0045 <span class="keyword">if</span> M_.exo_nbr &gt; 1
0046 disp(<span class="string">' '</span>)
0047 title=<span class="string">'VARIANCE DECOMPOSITION (in percent)'</span>;
0048 <span class="keyword">if</span> options_.hp_filter == 1
0049 title = [title <span class="string">' (HP filter, lambda = '</span> <span class="keyword">...</span>
0050 int2str(options_.hp_filter) <span class="string">')'</span>];
0051 <span class="keyword">end</span>
0052 headers = M_.exo_names;
0053 headers(M_.exo_names_orig_ord,:) = headers;
0054 headers = strvcat(<span class="string">' '</span>,headers);
0055 <a href="table.html" class="code" title="function table(title,headers,labels,values,label_width,val_width,val_precis)">table</a>(title,headers,deblank(M_.endo_names(ivar(i1),:)),100*oo_.gamma_y{options_.ar+2}(i1,:), <span class="keyword">...</span>
0056 lh,8,2);
0057 <span class="keyword">end</span>
0058 <span class="keyword">end</span>
0059
0060 <span class="keyword">if</span> options_.nocorr == 0
0061 disp(<span class="string">' '</span>)
0062 title=<span class="string">'MATRIX OF CORRELATIONS'</span>;
0063 <span class="keyword">if</span> options_.hp_filter == 1
0064 title = [title <span class="string">' (HP filter, lambda = '</span> int2str(options_.hp_filter) <span class="string">')'</span>];
0065 <span class="keyword">end</span>
0066 labels = deblank(M_.endo_names(ivar,:));
0067 headers = strvcat(<span class="string">'Variables'</span>,labels(i1,:));
0068 corr = oo_.gamma_y{1}(i1,i1)./(sd(i1)*sd(i1)');
0069 <a href="table.html" class="code" title="function table(title,headers,labels,values,label_width,val_width,val_precis)">table</a>(title,headers,labels(i1,:),corr,lh,8,4);
0070 <span class="keyword">end</span>
0071
0072 <span class="keyword">if</span> options_.ar &gt; 0
0073 disp(<span class="string">' '</span>)
0074 title=<span class="string">'COEFFICIENTS OF AUTOCORRELATION'</span>;
0075 <span class="keyword">if</span> options_.hp_filter == 1
0076 title = [title <span class="string">' (HP filter, lambda = '</span> int2str(options_.hp_filter) <span class="string">')'</span>];
0077 <span class="keyword">end</span>
0078 labels = deblank(M_.endo_names(ivar(i1),:));
0079 headers = strvcat(<span class="string">'Order '</span>,int2str([1:options_.ar]'));
0080 z=[];
0081 <span class="keyword">for</span> i=1:options_.ar
0082 oo_.autocorr{i} = oo_.gamma_y{i+1};
0083 z(:,i) = diag(oo_.gamma_y{i+1}(i1,i1));
0084 <span class="keyword">end</span>
0085 <a href="table.html" class="code" title="function table(title,headers,labels,values,label_width,val_width,val_precis)">table</a>(title,headers,labels,z,0,8,4);
0086 <span class="keyword">end</span>
0087
0088 <span class="comment">% 10/09/02 MJ</span>
0089 <span class="comment">% 10/18/02 MJ added th_autocovariances() and provided for lags on several</span>
0090 <span class="comment">% periods</span>
0091 <span class="comment">% 10/30/02 MJ added correlations and autocorrelations, uses table()</span>
0092 <span class="comment">% oo_.gamma_y is now a cell array.</span>
0093 <span class="comment">% 02/18/03 MJ added subtitles for HP filter</span>
0094 <span class="comment">% 05/01/03 MJ corrected options_.hp_filter</span>
0095 <span class="comment">% 05/21/03 MJ variance decomposition: test M_.exo_nbr &gt; 1</span>
0096 <span class="comment">% 05/21/03 MJ displays only variables with positive variance</span></pre></div>
<hr><address>Generated on Fri 16-Jun-2006 09:09:06 by <strong><a href="http://www.artefact.tk/software/matlab/m2html/">m2html</a></strong> &copy; 2003</address>
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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"
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<head>
<title>Description of dlognorm</title>
<meta name="keywords" content="dlognorm">
<meta name="description" content="DLOGNORM The log-normal density function">
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
<meta name="generator" content="m2html &copy; 2003 Guillaume Flandin">
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<h1>dlognorm
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>DLOGNORM The log-normal density function</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function f = dlognorm(x,lambda,zeta) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment">DLOGNORM The log-normal density function
f = dlognorm(x,lambda,zeta)</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="dnorm.html" class="code" title="function f = dnorm(x,m,s)">dnorm</a> DNORM The normal density function</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function f = dlognorm(x,lambda,zeta)</a>
0002 <span class="comment">%DLOGNORM The log-normal density function</span>
0003 <span class="comment">%</span>
0004 <span class="comment">% f = dlognorm(x,lambda,zeta)</span>
0005
0006 <span class="comment">% Copyright (c) Halfdan Grage, Anders Holtsberg</span>
0007
0008 <span class="keyword">if</span> any(any(zeta&lt;=0))
0009 error(<span class="string">'Parameter zeta is wrong'</span>)
0010 <span class="keyword">end</span>
0011
0012 neg = find(x &lt;= 0);
0013 [n1,n2] = size(neg);
0014 x(neg) = ones(n1,n2);
0015 f = <a href="dnorm.html" class="code" title="function f = dnorm(x,m,s)">dnorm</a>(log(x),lambda,zeta)./x;
0016 f(neg) = zeros(n1,n2);</pre></div>
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<title>Description of dnorm</title>
<meta name="keywords" content="dnorm">
<meta name="description" content="DNORM The normal density function">
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
<meta name="generator" content="m2html &copy; 2003 Guillaume Flandin">
<meta name="robots" content="index, follow">
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<h1>dnorm
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>DNORM The normal density function</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function f = dnorm(x,m,s) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment">DNORM The normal density function
f = dnorm(x,Mean,StandardDeviation)</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="dlognorm.html" class="code" title="function f = dlognorm(x,lambda,zeta)">dlognorm</a> DLOGNORM The log-normal density function</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function f = dnorm(x,m,s)</a>
0002 <span class="comment">%DNORM The normal density function</span>
0003 <span class="comment">%</span>
0004 <span class="comment">% f = dnorm(x,Mean,StandardDeviation)</span>
0005
0006 <span class="comment">% Anders Holtsberg, 18-11-93</span>
0007 <span class="comment">% Copyright (c) Anders Holtsberg</span>
0008
0009 <span class="keyword">if</span> nargin&lt;3, s=1; <span class="keyword">end</span>
0010 <span class="keyword">if</span> nargin&lt;2, m=0; <span class="keyword">end</span>
0011 f = exp(-0.5*((x-m)./s).^2)./(sqrt(2*pi)*s);</pre></div>
<hr><address>Generated on Fri 16-Jun-2006 09:09:06 by <strong><a href="http://www.artefact.tk/software/matlab/m2html/">m2html</a></strong> &copy; 2003</address>
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<title>Description of dr1</title>
<meta name="keywords" content="dr1">
<meta name="description" content="Copyright (C) 2001 Michel Juillard">
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
<meta name="generator" content="m2html &copy; 2003 Guillaume Flandin">
<meta name="robots" content="index, follow">
<link type="text/css" rel="stylesheet" href="../m2html.css">
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<h1>dr1
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>Copyright (C) 2001 Michel Juillard</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function [dr,info]=dr1(dr,task) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> Copyright (C) 2001 Michel Juillard</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="hessian.html" class="code" title="function hessian_mat = hessian(func,x,varargin)">hessian</a> Copyright (C) 2001 Michel Juillard</li><li><a href="qzdiv.html" class="code" title="function [A,B,Q,Z] = qzdiv(stake,A,B,Q,Z)">qzdiv</a> from Chris Sims web site</li><li><a href="resid.html" class="code" title="function resid(period)">resid</a> Copyright (C) 2001 Michel Juillard</li><li><a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a> </li><li><a href="set_state_space.html" class="code" title="function dr=set_state_space(dr)">set_state_space</a> Copyright (C) 2001 Michel Juillard</li><li><a href="sylvester3.html" class="code" title="function x=sylvester3(a,b,c,d)">sylvester3</a> solves a*x+b*x*c=d</li><li><a href="sylvester3a.html" class="code" title="function x=sylvester3a(x0,a,b,c,d)">sylvester3a</a> solves iteratively ax+bxc=d</li><li><a href="transition_matrix.html" class="code" title="function [A,B] = transition_matrix(dr)">transition_matrix</a> makes transition matrices out of ghx and ghu</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="dr2.html" class="code" title="function ghs2=dr2(ys,dr)">dr2</a> Copyright (C) 2001 Michel Juillard</li><li><a href="resol.html" class="code" title="function [dr,info]=resol(ys,check_flag)">resol</a> Copyright (C) 2001 Michel Juillard</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <span class="comment">% Copyright (C) 2001 Michel Juillard</span>
0002 <span class="comment">%</span>
0003 <a name="_sub0" href="#_subfunctions" class="code">function [dr,info]=dr1(dr,task)</a>
0004 <span class="keyword">global</span> M_ options_ oo_
0005
0006 <span class="keyword">global</span> olr_state
0007 <span class="comment">% info = 1: the model doesn't define current variables uniquely</span>
0008 <span class="comment">% info = 2: problem in mjdgges.dll info(2) contains error code</span>
0009 <span class="comment">% info = 3: BK order condition not satisfied info(2) contains &quot;distance&quot;</span>
0010 <span class="comment">% absence of stable trajectory</span>
0011 <span class="comment">% info = 4: BK order condition not satisfied info(2) contains &quot;distance&quot;</span>
0012 <span class="comment">% indeterminacy</span>
0013 <span class="comment">% info = 5: BK rank condition not satisfied</span>
0014
0015
0016
0017 info = 0;
0018
0019 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'loglinear'</span>,0);
0020 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'noprint'</span>,0);
0021 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'olr'</span>,0);
0022 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'olr_beta'</span>,1);
0023 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'qz_criterium'</span>,1.000001);
0024
0025 xlen = M_.maximum_lead + M_.maximum_lag + 1;
0026 klen = M_.maximum_lag + M_.maximum_lead + 1;
0027 iyv = M_.lead_lag_incidence';
0028 iyv = iyv(:);
0029 iyr0 = find(iyv) ;
0030 it_ = M_.maximum_lag + 1 ;
0031
0032 <span class="keyword">if</span> M_.exo_nbr == 0
0033 oo_.exo_steady_state = [] ;
0034 <span class="keyword">end</span>
0035
0036 tempex = oo_.exo_simul;
0037
0038 it_ = M_.maximum_lag + 1;
0039 <span class="keyword">if</span> options_.olr
0040 z = repmat(zeros(M_.endo_nbr,1),1,klen);
0041 <span class="keyword">else</span>
0042 z = repmat(dr.ys,1,klen);
0043 <span class="keyword">end</span>
0044 z = z(iyr0) ;
0045 <span class="keyword">if</span> options_.order == 1
0046 [junk,jacobia_] = feval([M_.fname <span class="string">'_dynamic'</span>],z,tempex);
0047 <span class="keyword">elseif</span> options_.order == 2
0048 [junk,jacobia_,<a href="hessian.html" class="code" title="function hessian_mat = hessian(func,x,varargin)">hessian</a>] = feval([M_.fname <span class="string">'_dynamic'</span>],z,<span class="keyword">...</span>
0049 [oo_.exo_simul <span class="keyword">...</span>
0050 oo_.exo_det_simul]);
0051 <span class="keyword">end</span>
0052
0053 oo_.exo_simul = tempex ;
0054 tempex = [];
0055
0056 <span class="comment">% expanding system for Optimal Linear Regulator</span>
0057 <span class="keyword">if</span> options_.olr
0058 bet = options_.olr_beta;
0059 jacobia1 = [];
0060 n_inst = size(options_.olr_inst,1);
0061
0062 <span class="keyword">if</span> ~isfield(olr_state_,<span class="string">'done'</span>)
0063 olr_state_.done = 1;
0064 olr_state_.old_M_.maximum_lag = M_.maximum_lag;
0065 olr_state_.old_M_.maximum_lead = M_.maximum_lead;
0066 olr_state_.old_M_.endo_nbr = M_.endo_nbr;
0067 olr_state_.old_M_.lead_lag_incidence = M_.lead_lag_incidence;
0068
0069 <span class="keyword">for</span> i=1:M_.endo_nbr
0070 temp = [<span class="string">'mult_'</span> int2str(i)];
0071 lgoo_.endo_simul = strvcat(lgoo_.endo_simul,temp);
0072 <span class="keyword">end</span>
0073 M_.endo_nbr = 2*M_.endo_nbr-n_inst;
0074 M_.maximum_lag = max(M_.maximum_lag,M_.maximum_lead);
0075 M_.maximum_lead = M_.maximum_lag;
0076 <span class="keyword">end</span>
0077 nj = olr_state_.old_M_.endo_nbr-n_inst;
0078 offset_min = M_.maximum_lag - olr_state_.old_M_.maximum_lag;
0079 offset_max = M_.maximum_lead - olr_state_.old_M_.maximum_lead;
0080 newiy = zeros(2*M_.maximum_lag+1,nj+olr_state_.old_M_.endo_nbr);
0081 jacobia_ = jacobia_(1:nj,:);
0082 <span class="keyword">for</span> i=1:2*M_.maximum_lag+1
0083 <span class="keyword">if</span> i &gt; offset_min &amp; i &lt;= 2*M_.maximum_lag+1-offset_max
0084 [junk,k1,k2] = find(olr_state_.old_M_.lead_lag_incidence(i-offset_min,:));
0085 <span class="keyword">if</span> i == M_.maximum_lag+1
0086 jacobia1 = [jacobia1 [jacobia_(:,k2); 2*options_.olr_w]];
0087 <span class="keyword">else</span>
0088 jacobia1 = [jacobia1 [jacobia_(:,k2); <span class="keyword">...</span>
0089 zeros(olr_state_.old_M_.endo_nbr,length(k1))]];
0090 <span class="keyword">end</span>
0091 newiy(i,k1) = ones(1,length(k1));
0092 <span class="keyword">end</span>
0093 i1 = 2*M_.maximum_lag+2-i;
0094 <span class="keyword">if</span> i1 &lt;= 2*M_.maximum_lag+1-offset_max &amp; i1 &gt; offset_min
0095 [junk,k1,k2] = find(olr_state_.old_M_.lead_lag_incidence(i1-offset_min,:));
0096 k3 = find(any(jacobia_(:,k2),2));
0097 x = zeros(olr_state_.old_M_.endo_nbr,length(k3));
0098 x(k1,:) = bet^(-i1+M_.maximum_lag+1)*jacobia_(k3,k2)';
0099 jacobia1 = [jacobia1 [zeros(nj,length(k3)); x]];
0100 newiy(i,k3+olr_state_.old_M_.endo_nbr) = ones(1,length(k3));
0101 <span class="keyword">end</span>
0102 <span class="keyword">end</span>
0103 jacobia1 = [jacobia1 [jacobia_(:,end-M_.exo_nbr+1:end); <span class="keyword">...</span>
0104 zeros(olr_state_.old_M_.endo_nbr, M_.exo_nbr)]];
0105 newiy = newiy';
0106 newiy = find(newiy(:));
0107 M_.lead_lag_incidence = zeros(M_.endo_nbr*(M_.maximum_lag+M_.maximum_lead+1),1);
0108 M_.lead_lag_incidence(newiy) = [1:length(newiy)]';
0109 M_.lead_lag_incidence =reshape(M_.lead_lag_incidence,M_.endo_nbr,M_.maximum_lag+M_.maximum_lead+1)';
0110 jacobia_ = jacobia1;
0111 clear jacobia1
0112 <span class="comment">% computes steady state</span>
0113 <a href="resid.html" class="code" title="function resid(period)">resid</a> = feval([M_.fname <span class="string">'_steady'</span>],zeros(olr_state_.old_M_.endo_nbr,1));
0114 <span class="keyword">if</span> <a href="resid.html" class="code" title="function resid(period)">resid</a>'*<a href="resid.html" class="code" title="function resid(period)">resid</a> &lt; 1e-12
0115 dr.ys =[dr.ys; zeros(nj,1)];
0116 <span class="keyword">else</span>
0117 AA = zeros(M_.endo_nbr,M_.endo_nbr);
0118 <span class="keyword">for</span> i=1:M_.maximum_lag+M_.maximum_lead+1
0119 [junk,k1,k2] = find(M_.lead_lag_incidence(i,:));
0120 AA(:,k1) = AA(:,k1)+jacobia_(:,k2);
0121 <span class="keyword">end</span>
0122 dr.ys = -AA\[<a href="resid.html" class="code" title="function resid(period)">resid</a>; zeros(nj,1)];
0123 <span class="keyword">end</span>
0124 <span class="keyword">end</span>
0125 <span class="comment">% end of code section for Optimal Linear Regulator</span>
0126
0127 klen = M_.maximum_lag + M_.maximum_lead + 1;
0128 dr=<a href="set_state_space.html" class="code" title="function dr=set_state_space(dr)">set_state_space</a>(dr);
0129 kstate = dr.kstate;
0130 kad = dr.kad;
0131 kae = dr.kae;
0132 nstatic = dr.nstatic;
0133 nfwrd = dr.nfwrd;
0134 npred = dr.npred;
0135 nboth = dr.nboth;
0136 order_var = dr.order_var;
0137 nd = size(kstate,1);
0138 nz = nnz(M_.lead_lag_incidence);
0139
0140 sdyn = M_.endo_nbr - nstatic;
0141
0142 k0 = M_.lead_lag_incidence(M_.maximum_lag+1,order_var);
0143 k1 = M_.lead_lag_incidence(find([1:klen] ~= M_.maximum_lag+1),:);
0144 b = jacobia_(:,k0);
0145
0146 <span class="keyword">if</span> M_.maximum_lead == 0; <span class="comment">% backward models</span>
0147 a = jacobia_(:,nonzeros(k1'));
0148 dr.ghx = zeros(size(a));
0149 m = 0;
0150 <span class="keyword">for</span> i=M_.maximum_lag:-1:1
0151 k = nonzeros(M_.lead_lag_incidence(i,order_var));
0152 dr.ghx(:,m+[1:length(k)]) = -b\a(:,k);
0153 m = m+length(k);
0154 <span class="keyword">end</span>
0155 <span class="keyword">if</span> M_.exo_nbr
0156 dr.ghu = -b\jacobia_(:,nz+1:end);
0157 <span class="keyword">end</span>
0158 dr.eigval = eig(<a href="transition_matrix.html" class="code" title="function [A,B] = transition_matrix(dr)">transition_matrix</a>(dr));
0159 dr.rank = 0;
0160 <span class="keyword">if</span> any(abs(dr.eigval) &gt; options_.qz_criterium)
0161 temp = sort(abs(dr.eigval));
0162 nba = nnz(abs(dr.eigval) &gt; options_.qz_criterium);
0163 temp = temp(nd-nba+1:nd)-1-options_.qz_criterium;
0164 info(1) = 3;
0165 info(2) = temp'*temp;
0166 <span class="keyword">end</span>
0167 <span class="keyword">return</span>;
0168 <span class="keyword">end</span>
0169
0170 <span class="comment">%forward--looking models</span>
0171 <span class="keyword">if</span> nstatic &gt; 0
0172 [Q,R] = qr(b(:,1:nstatic));
0173 aa = Q'*jacobia_;
0174 <span class="keyword">else</span>
0175 aa = jacobia_;
0176 <span class="keyword">end</span>
0177 a = aa(:,nonzeros(k1'));
0178 b = aa(:,k0);
0179 b10 = b(1:nstatic,1:nstatic);
0180 b11 = b(1:nstatic,nstatic+1:end);
0181 b2 = b(nstatic+1:<span class="keyword">end</span>,nstatic+1:end);
0182 <span class="keyword">if</span> any(isinf(a(:)))
0183 info = 1;
0184 <span class="keyword">return</span>
0185 <span class="keyword">end</span>
0186 <span class="keyword">if</span> M_.exo_nbr
0187 fu = aa(:,nz+(1:M_.exo_nbr));
0188 <span class="keyword">end</span>
0189 clear aa;
0190
0191 <span class="comment">% buildind D and E</span>
0192 d = zeros(nd,nd) ;
0193 e = d ;
0194
0195 k = find(kstate(:,2) &gt;= M_.maximum_lag+2 &amp; kstate(:,3));
0196 d(1:sdyn,k) = a(nstatic+1:<span class="keyword">end</span>,kstate(k,3)) ;
0197 k1 = find(kstate(:,2) == M_.maximum_lag+2);
0198 e(1:sdyn,k1) = -b2(:,kstate(k1,1)-nstatic);
0199 k = find(kstate(:,2) &lt;= M_.maximum_lag+1 &amp; kstate(:,4));
0200 e(1:sdyn,k) = -a(nstatic+1:<span class="keyword">end</span>,kstate(k,4)) ;
0201 k2 = find(kstate(:,2) == M_.maximum_lag+1);
0202 k2 = k2(~ismember(kstate(k2,1),kstate(k1,1)));
0203 d(1:sdyn,k2) = b2(:,kstate(k2,1)-nstatic);
0204
0205 <span class="keyword">if</span> ~isempty(kad)
0206 <span class="keyword">for</span> j = 1:size(kad,1)
0207 d(sdyn+j,kad(j)) = 1 ;
0208 e(sdyn+j,kae(j)) = 1 ;
0209 <span class="keyword">end</span>
0210 <span class="keyword">end</span>
0211
0212 <span class="keyword">if</span> ~exist(<span class="string">'mjdgges'</span>)
0213 <span class="comment">% using Chris Sim's routines</span>
0214 use_qzdiv = 1;
0215 [ss,tt,qq,w] = qz(e,d);
0216 [tt,ss,qq,w] = <a href="qzdiv.html" class="code" title="function [A,B,Q,Z] = qzdiv(stake,A,B,Q,Z)">qzdiv</a>(options_.qz_criterium,tt,ss,qq,w);
0217 ss1=diag(ss);
0218 tt1=diag(tt);
0219 warning_state = warning;
0220 warning off;
0221 dr.eigval = ss1./tt1 ;
0222 warning warning_state;
0223 nba = nnz(abs(dr.eigval) &gt; options_.qz_criterium);
0224 <span class="keyword">else</span>
0225 use_qzdiv = 0;
0226 [ss,tt,w,sdim,dr.eigval,info1] = mjdgges(e,d,options_.qz_criterium);
0227 <span class="keyword">if</span> info1
0228 info(1) = 2;
0229 info(2) = info1;
0230 <span class="keyword">return</span>
0231 <span class="keyword">end</span>
0232 nba = nd-sdim;
0233 <span class="keyword">end</span>
0234
0235 nyf = sum(kstate(:,2) &gt; M_.maximum_lag+1);
0236
0237 <span class="keyword">if</span> task == 1
0238 dr.rank = rank(w(1:nyf,nd-nyf+1:end));
0239 dr.eigval = eig(e,d);
0240 <span class="keyword">return</span>
0241 <span class="keyword">end</span>
0242
0243 <span class="keyword">if</span> nba ~= nyf
0244 temp = sort(abs(dr.eigval));
0245 <span class="keyword">if</span> nba &gt; nyf
0246 temp = temp(nd-nba+1:nd-nyf)-1-options_.qz_criterium;
0247 info(1) = 3;
0248 <span class="keyword">elseif</span> nba &lt; nyf;
0249 temp = temp(nd-nyf+1:nd-nba)-1-options_.qz_criterium;
0250 info(1) = 4;
0251 <span class="keyword">end</span>
0252 info(2) = temp'*temp;
0253 <span class="keyword">return</span>
0254 <span class="keyword">end</span>
0255
0256 np = nd - nyf;
0257 n2 = np + 1;
0258 n3 = nyf;
0259 n4 = n3 + 1;
0260 <span class="comment">% derivatives with respect to dynamic state variables</span>
0261 <span class="comment">% forward variables</span>
0262 w1 =w(1:n3,n2:nd);
0263 <span class="keyword">if</span> condest(w1) &gt; 1e9;
0264 info(1) = 5;
0265 info(2) = condest(w1);
0266 <span class="keyword">return</span>;
0267 <span class="keyword">else</span>
0268 gx = -w1'\w(n4:nd,n2:nd)';
0269 <span class="keyword">end</span>
0270
0271 <span class="comment">% predetermined variables</span>
0272 hx = w(1:n3,1:np)'*gx+w(n4:nd,1:np)';
0273 hx = (tt(1:np,1:np)*hx)\(ss(1:np,1:np)*hx);
0274
0275 k1 = find(kstate(n4:nd,2) == M_.maximum_lag+1);
0276 k2 = find(kstate(1:n3,2) == M_.maximum_lag+2);
0277 dr.ghx = [hx(k1,:); gx(k2(nboth+1:end),:)];
0278
0279 <span class="comment">%lead variables actually present in the model</span>
0280 j3 = nonzeros(kstate(:,3));
0281 j4 = find(kstate(:,3));
0282 <span class="comment">% derivatives with respect to exogenous variables</span>
0283 <span class="keyword">if</span> M_.exo_nbr
0284 a1 = b;
0285 aa1 = [];
0286 <span class="keyword">if</span> nstatic &gt; 0
0287 aa1 = a1(:,1:nstatic);
0288 <span class="keyword">end</span>
0289 dr.ghu = -[aa1 a(:,j3)*gx(j4,1:npred)+a1(:,nstatic+1:nstatic+ <span class="keyword">...</span>
0290 npred) a1(:,nstatic+npred+1:end)]\fu;
0291 <span class="keyword">end</span>
0292
0293 <span class="comment">% static variables</span>
0294 <span class="keyword">if</span> nstatic &gt; 0
0295 temp = -a(1:nstatic,j3)*gx(j4,:)*hx;
0296 j5 = find(kstate(n4:nd,4));
0297 temp(:,j5) = temp(:,j5)-a(1:nstatic,nonzeros(kstate(:,4)));
0298 temp = b10\(temp-b11*dr.ghx);
0299 dr.ghx = [temp; dr.ghx];
0300 temp = [];
0301 <span class="keyword">end</span>
0302
0303 <span class="keyword">if</span> options_.loglinear == 1
0304 k = find(dr.kstate(:,2) &lt;= M_.maximum_lag+1);
0305 klag = dr.kstate(k,[1 2]);
0306 k1 = dr.order_var;
0307
0308 dr.ghx = repmat(1./dr.ys(k1),1,size(dr.ghx,2)).*dr.ghx.* <span class="keyword">...</span>
0309 repmat(dr.ys(k1(klag(:,1)))',size(dr.ghx,1),1);
0310 dr.ghu = repmat(1./dr.ys(k1),1,size(dr.ghu,2)).*dr.ghu;
0311 <span class="keyword">end</span>
0312
0313 <span class="comment">%necessary when using Sims' routines</span>
0314 <span class="keyword">if</span> use_qzdiv
0315 gx = real(gx);
0316 hx = real(hx);
0317 dr.ghx = real(dr.ghx);
0318 dr.ghu = real(dr.ghu);
0319 <span class="keyword">end</span>
0320
0321 <span class="comment">%exogenous deterministic variables</span>
0322 <span class="keyword">if</span> M_.exo_det_nbr &gt; 0
0323 f1 = sparse(jacobia_(:,nonzeros(M_.lead_lag_incidence(M_.maximum_lag+2:<span class="keyword">end</span>,order_var))));
0324 f0 = sparse(jacobia_(:,nonzeros(M_.lead_lag_incidence(M_.maximum_lag+1,order_var))));
0325 fudet = sparse(jacobia_(:,nz+M_.exo_nbr+1:end));
0326 M1 = inv(f0+[zeros(M_.endo_nbr,nstatic) f1*gx zeros(M_.endo_nbr,nyf-nboth)]);
0327 M2 = M1*f1;
0328 dr.ghud = cell(M_.exo_det_length,1);
0329 dr.ghud{1} = -M1*fudet;
0330 <span class="keyword">for</span> i = 2:M_.exo_det_length
0331 dr.ghud{i} = -M2*dr.ghud{i-1}(end-nyf+1:<span class="keyword">end</span>,:);
0332 <span class="keyword">end</span>
0333 <span class="keyword">end</span>
0334
0335 <span class="keyword">if</span> options_.order == 1
0336 <span class="keyword">return</span>
0337 <span class="keyword">end</span>
0338
0339 <span class="comment">% Second order</span>
0340 <span class="comment">%tempex = oo_.exo_simul ;</span>
0341
0342 <span class="comment">%hessian = real(hessext('ff1_',[z; oo_.exo_steady_state]))' ;</span>
0343 kk = flipud(cumsum(flipud(M_.lead_lag_incidence(M_.maximum_lag+1:<span class="keyword">end</span>,order_var)),1));
0344 <span class="keyword">if</span> M_.maximum_lag &gt; 0
0345 kk = [cumsum(M_.lead_lag_incidence(1:M_.maximum_lag,order_var),1); kk];
0346 <span class="keyword">end</span>
0347 kk = kk';
0348 kk = find(kk(:));
0349 nk = size(kk,1) + M_.exo_nbr + M_.exo_det_nbr;
0350 k1 = M_.lead_lag_incidence(:,order_var);
0351 k1 = k1';
0352 k1 = k1(:);
0353 k1 = k1(kk);
0354 k2 = find(k1);
0355 kk1(k1(k2)) = k2;
0356 kk1 = [kk1 length(k1)+1:length(k1)+M_.exo_nbr+M_.exo_det_nbr];
0357 kk = reshape([1:nk^2],nk,nk);
0358 kk1 = kk(kk1,kk1);
0359 <span class="comment">%[junk,junk,hessian] = feval([M_.fname '_dynamic'],z, oo_.exo_steady_state);</span>
0360 <a href="hessian.html" class="code" title="function hessian_mat = hessian(func,x,varargin)">hessian</a>(:,kk1(:)) = <a href="hessian.html" class="code" title="function hessian_mat = hessian(func,x,varargin)">hessian</a>;
0361
0362 <span class="comment">%oo_.exo_simul = tempex ;</span>
0363 <span class="comment">%clear tempex</span>
0364
0365 n1 = 0;
0366 n2 = np;
0367 zx = zeros(np,np);
0368 zu=zeros(np,M_.exo_nbr);
0369 <span class="keyword">for</span> i=2:M_.maximum_lag+1
0370 k1 = sum(kstate(:,2) == i);
0371 zx(n1+1:n1+k1,n2-k1+1:n2)=eye(k1);
0372 n1 = n1+k1;
0373 n2 = n2-k1;
0374 <span class="keyword">end</span>
0375 kk = flipud(cumsum(flipud(M_.lead_lag_incidence(M_.maximum_lag+1:<span class="keyword">end</span>,order_var)),1));
0376 k0 = [1:M_.endo_nbr];
0377 gx1 = dr.ghx;
0378 hu = dr.ghu(nstatic+[1:npred],:);
0379 zx = [zx; gx1];
0380 zu = [zu; dr.ghu];
0381 <span class="keyword">for</span> i=1:M_.maximum_lead
0382 k1 = find(kk(i+1,k0) &gt; 0);
0383 zu = [zu; gx1(k1,1:npred)*hu];
0384 gx1 = gx1(k1,:)*hx;
0385 zx = [zx; gx1];
0386 kk = kk(:,k0);
0387 k0 = k1;
0388 <span class="keyword">end</span>
0389 zx=[zx; zeros(M_.exo_nbr,np);zeros(M_.exo_det_nbr,np)];
0390 zu=[zu; eye(M_.exo_nbr);zeros(M_.exo_det_nbr,M_.exo_nbr)];
0391 [n1,n2] = size(zx);
0392 <span class="keyword">if</span> n1*n1*n2*n2 &gt; 1e7
0393 rhs = zeros(M_.endo_nbr,n2*n2);
0394 k1 = 1;
0395 <span class="keyword">for</span> i1 = 1:n2
0396 <span class="keyword">for</span> i2 = 1:n2
0397 rhs(:,k1) = <a href="hessian.html" class="code" title="function hessian_mat = hessian(func,x,varargin)">hessian</a>*kron(zx(:,i1),zx(:,i2));
0398 k1 = k1 + 1;
0399 <span class="keyword">end</span>
0400 <span class="keyword">end</span>
0401 <span class="keyword">else</span>
0402 rhs = <a href="hessian.html" class="code" title="function hessian_mat = hessian(func,x,varargin)">hessian</a>*kron(zx,zx);
0403 <span class="keyword">end</span>
0404 rhs = -rhs;
0405
0406 <span class="comment">%lhs</span>
0407 n = M_.endo_nbr+sum(kstate(:,2) &gt; M_.maximum_lag+1 &amp; kstate(:,2) &lt; M_.maximum_lag+M_.maximum_lead+1);
0408 A = zeros(n,n);
0409 B = zeros(n,n);
0410 A(1:M_.endo_nbr,1:M_.endo_nbr) = jacobia_(:,M_.lead_lag_incidence(M_.maximum_lag+1,order_var));
0411 <span class="comment">% variables with the highest lead</span>
0412 k1 = find(kstate(:,2) == M_.maximum_lag+M_.maximum_lead+1);
0413 <span class="keyword">if</span> M_.maximum_lead &gt; 1
0414 k2 = find(kstate(:,2) == M_.maximum_lag+M_.maximum_lead);
0415 [junk,junk,k3] = intersect(kstate(k1,1),kstate(k2,1));
0416 <span class="keyword">else</span>
0417 k2 = [1:M_.endo_nbr];
0418 k3 = kstate(k1,1);
0419 <span class="keyword">end</span>
0420 <span class="comment">% Jacobian with respect to the variables with the highest lead</span>
0421 B(1:M_.endo_nbr,end-length(k2)+k3) = jacobia_(:,kstate(k1,3)+M_.endo_nbr);
0422 offset = M_.endo_nbr;
0423 k0 = [1:M_.endo_nbr];
0424 gx1 = dr.ghx;
0425 <span class="keyword">for</span> i=1:M_.maximum_lead-1
0426 k1 = find(kstate(:,2) == M_.maximum_lag+i+1);
0427 [k2,junk,k3] = find(kstate(k1,3));
0428 A(1:M_.endo_nbr,offset+k2) = jacobia_(:,k3+M_.endo_nbr);
0429 n1 = length(k1);
0430 A(offset+[1:n1],nstatic+[1:npred]) = -gx1(kstate(k1,1),1:npred);
0431 gx1 = gx1*hx;
0432 A(offset+[1:n1],offset+[1:n1]) = eye(n1);
0433 n0 = length(k0);
0434 E = eye(n0);
0435 <span class="keyword">if</span> i == 1
0436 [junk,junk,k4]=intersect(kstate(k1,1),[1:M_.endo_nbr]);
0437 <span class="keyword">else</span>
0438 [junk,junk,k4]=intersect(kstate(k1,1),kstate(k0,1));
0439 <span class="keyword">end</span>
0440 i1 = offset-n0+n1;
0441 B(offset+[1:n1],offset-n0+[1:n0]) = -E(k4,:);
0442 k0 = k1;
0443 offset = offset + n1;
0444 <span class="keyword">end</span>
0445 [junk,k1,k2] = find(M_.lead_lag_incidence(M_.maximum_lag+M_.maximum_lead+1,order_var));
0446 A(1:M_.endo_nbr,nstatic+1:nstatic+npred)=<span class="keyword">...</span>
0447 A(1:M_.endo_nbr,nstatic+[1:npred])+jacobia_(:,k2)*gx1(k1,1:npred);
0448 C = hx;
0449 D = [rhs; zeros(n-M_.endo_nbr,size(rhs,2))];
0450 dr.ghxx = gensylv(2,A,B,C,D);
0451 <span class="keyword">if</span> exist(<span class="string">'gensylv'</span>)
0452 dr.ghxx = gensylv(2,A,B,C,D);
0453 <span class="keyword">else</span>
0454 C = kron(hx,hx);
0455 x0 = <a href="sylvester3.html" class="code" title="function x=sylvester3(a,b,c,d)">sylvester3</a>(A,B,C,D);
0456 dr.ghxx = <a href="sylvester3a.html" class="code" title="function x=sylvester3a(x0,a,b,c,d)">sylvester3a</a>(x0,A,B,C,D);
0457 <span class="keyword">end</span>
0458
0459 <span class="comment">%ghxu</span>
0460 <span class="comment">%rhs</span>
0461 hu = dr.ghu(nstatic+1:nstatic+npred,:);
0462 <span class="comment">%kk = reshape([1:np*np],np,np);</span>
0463 <span class="comment">%kk = kk(1:npred,1:npred);</span>
0464 <span class="comment">%rhs = -hessian*kron(zx,zu)-f1*dr.ghxx(end-nyf+1:end,kk(:))*kron(hx(1:npred,:),hu(1:npred,:));</span>
0465 <span class="keyword">if</span> n1*n1*n2*M_.exo_nbr &gt; 1e7
0466 rhs = zeros(M_.endo_nbr,n2*M_.exo_nbr);
0467 k1 = 1;
0468 <span class="keyword">for</span> i1 = 1:n2
0469 <span class="keyword">for</span> i2 = 1:M_.exo_nbr
0470 rhs(:,k1) = <a href="hessian.html" class="code" title="function hessian_mat = hessian(func,x,varargin)">hessian</a>*kron(zx(:,i1),zu(:,i2));
0471 k1 = k1 + 1;
0472 <span class="keyword">end</span>
0473 <span class="keyword">end</span>
0474 <span class="keyword">else</span>
0475 rhs = <a href="hessian.html" class="code" title="function hessian_mat = hessian(func,x,varargin)">hessian</a>*kron(zx,zu);
0476 <span class="keyword">end</span>
0477 nyf1 = sum(kstate(:,2) == M_.maximum_lag+2);
0478 hu1 = [hu;zeros(np-npred,M_.exo_nbr)];
0479 <span class="comment">%B1 = [B(1:M_.endo_nbr,:);zeros(size(A,1)-M_.endo_nbr,size(B,2))];</span>
0480 rhs = -[rhs; zeros(n-M_.endo_nbr,size(rhs,2))]-B*dr.ghxx*kron(hx,hu1);
0481
0482
0483 <span class="comment">%lhs</span>
0484 dr.ghxu = A\rhs;
0485
0486 <span class="comment">%ghuu</span>
0487 <span class="comment">%rhs</span>
0488 kk = reshape([1:np*np],np,np);
0489 kk = kk(1:npred,1:npred);
0490 <span class="keyword">if</span> n1*n1*M_.exo_nbr*M_.exo_nbr &gt; 1e7
0491 rhs = zeros(M_.endo_nbr,M_.exo_nbr*M_.exo_nbr);
0492 k1 = 1;
0493 <span class="keyword">for</span> i1 = 1:M_.exo_nbr
0494 <span class="keyword">for</span> i2 = 1:M_.exo_nbr
0495 rhs(:,k1) = <a href="hessian.html" class="code" title="function hessian_mat = hessian(func,x,varargin)">hessian</a>*kron(zu(:,i1),zu(:,i2));
0496 k1 = k1 + 1;
0497 <span class="keyword">end</span>
0498 <span class="keyword">end</span>
0499 <span class="keyword">else</span>
0500 rhs = <a href="hessian.html" class="code" title="function hessian_mat = hessian(func,x,varargin)">hessian</a>*kron(zu,zu);
0501 <span class="keyword">end</span>
0502
0503 rhs = -[rhs; zeros(n-M_.endo_nbr,size(rhs,2))]-B*dr.ghxx*kron(hu1,hu1);
0504
0505 <span class="comment">%lhs</span>
0506 dr.ghuu = A\rhs;
0507
0508 dr.ghxx = dr.ghxx(1:M_.endo_nbr,:);
0509 dr.ghxu = dr.ghxu(1:M_.endo_nbr,:);
0510 dr.ghuu = dr.ghuu(1:M_.endo_nbr,:);
0511
0512
0513 <span class="comment">% dr.ghs2</span>
0514 <span class="comment">% derivatives of F with respect to forward variables</span>
0515 <span class="comment">% reordering predetermined variables in diminishing lag order</span>
0516 O1 = zeros(M_.endo_nbr,nstatic);
0517 O2 = zeros(M_.endo_nbr,M_.endo_nbr-nstatic-npred);
0518 LHS = jacobia_(:,M_.lead_lag_incidence(M_.maximum_lag+1,order_var));
0519 RHS = zeros(M_.endo_nbr,M_.exo_nbr^2);
0520 kk = find(kstate(:,2) == M_.maximum_lag+2);
0521 gu = dr.ghu;
0522 guu = dr.ghuu;
0523 Gu = [dr.ghu(nstatic+[1:npred],:); zeros(np-npred,M_.exo_nbr)];
0524 Guu = [dr.ghuu(nstatic+[1:npred],:); zeros(np-npred,M_.exo_nbr*M_.exo_nbr)];
0525 E = eye(M_.endo_nbr);
0526 M_.lead_lag_incidenceordered = flipud(cumsum(flipud(M_.lead_lag_incidence(M_.maximum_lag+1:<span class="keyword">end</span>,order_var)),1));
0527 <span class="keyword">if</span> M_.maximum_lag &gt; 0
0528 M_.lead_lag_incidenceordered = [cumsum(M_.lead_lag_incidence(1:M_.maximum_lag,order_var),1); M_.lead_lag_incidenceordered];
0529 <span class="keyword">end</span>
0530 M_.lead_lag_incidenceordered = M_.lead_lag_incidenceordered';
0531 M_.lead_lag_incidenceordered = M_.lead_lag_incidenceordered(:);
0532 k1 = find(M_.lead_lag_incidenceordered);
0533 M_.lead_lag_incidenceordered(k1) = [1:length(k1)]';
0534 M_.lead_lag_incidenceordered =reshape(M_.lead_lag_incidenceordered,M_.endo_nbr,M_.maximum_lag+M_.maximum_lead+1)';
0535 kh = reshape([1:nk^2],nk,nk);
0536 kp = sum(kstate(:,2) &lt;= M_.maximum_lag+1);
0537 E1 = [eye(npred); zeros(kp-npred,npred)];
0538 H = E1;
0539 hxx = dr.ghxx(nstatic+[1:npred],:);
0540 <span class="keyword">for</span> i=1:M_.maximum_lead
0541 <span class="keyword">for</span> j=i:M_.maximum_lead
0542 [junk,k2a,k2] = find(M_.lead_lag_incidence(M_.maximum_lag+j+1,order_var));
0543 [junk,k3a,k3] = find(M_.lead_lag_incidenceordered(M_.maximum_lag+j+1,:));
0544 RHS = RHS + jacobia_(:,k2)*guu(k2a,:)+<a href="hessian.html" class="code" title="function hessian_mat = hessian(func,x,varargin)">hessian</a>(:,kh(k3,k3))* <span class="keyword">...</span>
0545 kron(gu(k3a,:),gu(k3a,:));
0546 <span class="keyword">end</span>
0547
0548 <span class="comment">% LHS</span>
0549 [junk,k2a,k2] = find(M_.lead_lag_incidence(M_.maximum_lag+i+1,order_var));
0550 LHS = LHS + jacobia_(:,k2)*(E(k2a,:)+[O1(k2a,:) dr.ghx(k2a,:)*H O2(k2a,:)]);
0551
0552 <span class="keyword">if</span> i == M_.maximum_lead
0553 <span class="keyword">break</span>
0554 <span class="keyword">end</span>
0555
0556 kk = find(kstate(:,2) == M_.maximum_lag+i+1);
0557 gu = dr.ghx*Gu;
0558 GuGu = kron(Gu,Gu);
0559 guu = dr.ghx*Guu+dr.ghxx*GuGu;
0560 Gu = hx*Gu;
0561 Guu = hx*Guu;
0562 Guu(end-npred+1:<span class="keyword">end</span>,:) = Guu(end-npred+1:<span class="keyword">end</span>,:) + hxx*GuGu;
0563
0564 H = E1 + hx*H;
0565 <span class="keyword">end</span>
0566 RHS = RHS*M_.Sigma_e(:);
0567 dr.fuu = RHS;
0568 RHS = -RHS-dr.fbias;
0569 dr.ghs2 = LHS\RHS;
0570
0571 <span class="comment">% deterministic exogenous variables</span>
0572 <span class="keyword">if</span> M_.exo_det_nbr &gt; 0
0573 hud = dr.ghud{1}(nstatic+1:nstatic+npred,:);
0574 zud=[zeros(np,M_.exo_det_nbr);dr.ghud{1};gx(:,1:npred)*hud;zeros(M_.exo_nbr,M_.exo_det_nbr);eye(M_.exo_det_nbr)];
0575 R1 = <a href="hessian.html" class="code" title="function hessian_mat = hessian(func,x,varargin)">hessian</a>*kron(zx,zud);
0576 dr.ghxud = cell(M_.exo_det_length,1);
0577 kf = [M_.endo_nbr-nyf+1:M_.endo_nbr];
0578 kp = nstatic+[1:npred];
0579 dr.ghxud{1} = -M1*(R1+f1*dr.ghxx(kf,:)*kron(dr.ghx(kp,:),dr.ghud{1}(kp,:)));
0580 Eud = eye(M_.exo_det_nbr);
0581 <span class="keyword">for</span> i = 2:M_.exo_det_length
0582 hudi = dr.ghud{i}(kp,:);
0583 zudi=[zeros(np,M_.exo_det_nbr);dr.ghud{i};gx(:,1:npred)*hudi;zeros(M_.exo_nbr+M_.exo_det_nbr,M_.exo_det_nbr)];
0584 R2 = <a href="hessian.html" class="code" title="function hessian_mat = hessian(func,x,varargin)">hessian</a>*kron(zx,zudi);
0585 dr.ghxud{i} = -M2*(dr.ghxud{i-1}(kf,:)*kron(hx,Eud)+dr.ghxx(kf,:)*kron(dr.ghx(kp,:),dr.ghud{i}(kp,:)))-M1*R2;
0586 <span class="keyword">end</span>
0587 R1 = <a href="hessian.html" class="code" title="function hessian_mat = hessian(func,x,varargin)">hessian</a>*kron(zu,zud);
0588 dr.ghudud = cell(M_.exo_det_length,1);
0589 kf = [M_.endo_nbr-nyf+1:M_.endo_nbr];
0590
0591 dr.ghuud{1} = -M1*(R1+f1*dr.ghxx(kf,:)*kron(dr.ghu(kp,:),dr.ghud{1}(kp,:)));
0592 Eud = eye(M_.exo_det_nbr);
0593 <span class="keyword">for</span> i = 2:M_.exo_det_length
0594 hudi = dr.ghud{i}(kp,:);
0595 zudi=[zeros(np,M_.exo_det_nbr);dr.ghud{i};gx(:,1:npred)*hudi;zeros(M_.exo_nbr+M_.exo_det_nbr,M_.exo_det_nbr)];
0596 R2 = <a href="hessian.html" class="code" title="function hessian_mat = hessian(func,x,varargin)">hessian</a>*kron(zu,zudi);
0597 dr.ghuud{i} = -M2*dr.ghxud{i-1}(kf,:)*kron(hu,Eud)-M1*R2;
0598 <span class="keyword">end</span>
0599 R1 = <a href="hessian.html" class="code" title="function hessian_mat = hessian(func,x,varargin)">hessian</a>*kron(zud,zud);
0600 dr.ghudud = cell(M_.exo_det_length,M_.exo_det_length);
0601 dr.ghudud{1,1} = -M1*R1-M2*dr.ghxx(kf,:)*kron(hud,hud);
0602 <span class="keyword">for</span> i = 2:M_.exo_det_length
0603 hudi = dr.ghud{i}(nstatic+1:nstatic+npred,:);
0604 zudi=[zeros(np,M_.exo_det_nbr);dr.ghud{i};gx(:,1:npred)*hudi+dr.ghud{i-1}(kf,:);zeros(M_.exo_nbr+M_.exo_det_nbr,M_.exo_det_nbr)];
0605 R2 = <a href="hessian.html" class="code" title="function hessian_mat = hessian(func,x,varargin)">hessian</a>*kron(zudi,zudi);
0606 dr.ghudud{i,i} = -M2*(dr.ghudud{i-1,i-1}(kf,:)+<span class="keyword">...</span>
0607 2*dr.ghxud{i-1}(kf,:)*kron(hudi,Eud) <span class="keyword">...</span>
0608 +dr.ghxx(kf,:)*kron(hudi,hudi))-M1*R2;
0609 R2 = <a href="hessian.html" class="code" title="function hessian_mat = hessian(func,x,varargin)">hessian</a>*kron(zud,zudi);
0610 dr.ghudud{1,i} = -M2*(dr.ghxud{i-1}(kf,:)*kron(hud,Eud)+<span class="keyword">...</span>
0611 dr.ghxx(kf,:)*kron(hud,hudi))<span class="keyword">...</span>
0612 -M1*R2;
0613 <span class="keyword">for</span> j=2:i-1
0614 hudj = dr.ghud{j}(kp,:);
0615 zudj=[zeros(np,M_.exo_det_nbr);dr.ghud{j};gx(:,1:npred)*hudj;zeros(M_.exo_nbr+M_.exo_det_nbr,M_.exo_det_nbr)];
0616 R2 = <a href="hessian.html" class="code" title="function hessian_mat = hessian(func,x,varargin)">hessian</a>*kron(zudj,zudi);
0617 dr.ghudud{j,i} = -M2*(dr.ghudud{j-1,i-1}(kf,:)+dr.ghxud{j-1}(kf,:)* <span class="keyword">...</span>
0618 kron(hudi,Eud)+dr.ghxud{i-1}(kf,:)* <span class="keyword">...</span>
0619 kron(hudj,Eud)+dr.ghxx(kf,:)*kron(hudj,hudi))-M1*R2;
0620 <span class="keyword">end</span>
0621
0622 <span class="keyword">end</span>
0623 <span class="keyword">end</span>
0624 <span class="comment">% 01/08/2001 MJ put return on iorder == 1 after defining dr.kstate and dr.kdyn</span>
0625 <span class="comment">% 01/17/2001 MJ added dr.delta_s: correction factor for order = 2</span>
0626 <span class="comment">% 01/21/2001 FC correction of delta_s for more than 1 shock</span>
0627 <span class="comment">% 01/23/2001 MJ suppression of redundant sum() in delta_s formula</span>
0628 <span class="comment">% 02/22/2001 MJ stderr_ replaced by Sigma_e_</span>
0629 <span class="comment">% 08/24/2001 MJ changed the order of the variables, separates static</span>
0630 <span class="comment">% variables and handles only one instance of variables both</span>
0631 <span class="comment">% in lead and lag</span>
0632 <span class="comment">% 08/24/2001 MJ added sigma to state variables as in Schmitt-Grohe and</span>
0633 <span class="comment">% Uribe (2001)</span>
0634 <span class="comment">% 10/20/2002 MJ corrected lags on several periods bug</span>
0635 <span class="comment">% 10/30/2002 MJ corrected lags on several periods bug on static when some</span>
0636 <span class="comment">% intermediary lags are missing</span>
0637 <span class="comment">% 12/08/2002 MJ uses sylvester3 to solve for dr.ghxx</span>
0638 <span class="comment">% 01/01/2003 MJ added dr.fbias for iterative for dr_algo == 1</span>
0639 <span class="comment">% 02/21/2003 MJ corrected bug for models without lagged variables</span>
0640 <span class="comment">% 03/02/2003 MJ fixed second order for lag on several periods</span>
0641 <span class="comment">% 05/21/2003 MJ add check call argument and make computation for CHECK</span>
0642 <span class="comment">% 06/01/2003 MJ added a test for M_.maximum_lead &gt; 1 and order &gt; 1</span>
0643 <span class="comment">% 08/28/2003 MJ corrected bug in computation of 2nd order (ordering of</span>
0644 <span class="comment">% forward variable in LHS)</span>
0645 <span class="comment">% 08/29/2003 MJ use Sims routine if mjdgges isn't available</span>
0646
0647
0648
0649
0650
0651
0652</pre></div>
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<div><a href="../index.html">Home</a> &gt; <a href="index.html">.</a> &gt; dr11.m</div>
<!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png">&nbsp;Master index</a></td>
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<h1>dr11
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>Copyright (C) 2001 Michel Juillard</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function dr=dr11(iorder,dr,cheik) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> Copyright (C) 2001 Michel Juillard</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="qzdiv.html" class="code" title="function [A,B,Q,Z] = qzdiv(stake,A,B,Q,Z)">qzdiv</a> from Chris Sims web site</li><li><a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a> </li><li><a href="set_state_space.html" class="code" title="function dr=set_state_space(dr)">set_state_space</a> Copyright (C) 2001 Michel Juillard</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="resol1.html" class="code" title="function dr=resol1(ys,algo,linear,iorder)">resol1</a> Copyright (C) 2001 Michel Juillard</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <span class="comment">% Copyright (C) 2001 Michel Juillard</span>
0002 <span class="comment">%</span>
0003 <a name="_sub0" href="#_subfunctions" class="code">function dr=dr11(iorder,dr,cheik)</a>
0004
0005 <span class="keyword">global</span> M_ options_ oo_
0006 <span class="keyword">global</span> it_ stdexo_ means_ dr1_test_ bayestopt_
0007
0008 <span class="comment">% hack for Bayes</span>
0009 <span class="keyword">global</span> dr1_test_ bayestopt_
0010
0011 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'loglinear'</span>,0);
0012
0013 xlen = M_.maximum_lead + M_.maximum_lag + 1;
0014 klen = M_.maximum_lag + M_.maximum_lead + 1;
0015 iyv = transpose(M_.lead_lag_incidence);
0016 iyv = iyv(:);
0017 iyr0 = find(iyv) ;
0018 it_ = M_.maximum_lag + 1 ;
0019
0020
0021 <span class="keyword">if</span> M_.exo_nbr == 0
0022 oo_.exo_steady_state = [] ;
0023 <span class="keyword">end</span>
0024
0025 <span class="keyword">if</span> ~ M_.lead_lag_incidence(M_.maximum_lag+1,:) &gt; 0
0026 error (<span class="string">'Error in model specification: some variables don&quot;t appear as current'</span>) ;
0027 <span class="keyword">end</span>
0028
0029 <span class="keyword">if</span> ~cheik
0030 <span class="comment">% if xlen &gt; 1</span>
0031 <span class="comment">% error (['SS: stochastic exogenous variables must appear only at the' ...</span>
0032 <span class="comment">% ' current period. Use additional endogenous variables']) ;</span>
0033 <span class="comment">% end</span>
0034 <span class="keyword">end</span>
0035
0036 <span class="keyword">if</span> M_.maximum_lead &gt; 1 &amp; iorder &gt; 1
0037 error ([<span class="string">'Models with leads on more than one period can only be solved'</span> <span class="keyword">...</span>
0038 <span class="string">' at order 1'</span>])
0039 <span class="keyword">end</span>
0040
0041 dr=<a href="set_state_space.html" class="code" title="function dr=set_state_space(dr)">set_state_space</a>(dr);
0042 kstate = dr.kstate;
0043 kad = dr.kad;
0044 kae = dr.kae;
0045 nstatic = dr.nstatic;
0046 nfwrd = dr.nfwrd;
0047 npred = dr.npred;
0048 nboth = dr.nboth;
0049 order_var = dr.order_var;
0050 nd = size(kstate,1);
0051
0052 sdyn = M_.endo_nbr - nstatic;
0053
0054
0055 tempex = oo_.exo_simul;
0056
0057 it_ = M_.maximum_lag + 1;
0058 z = repmat(dr.ys,1,klen);
0059 z = z(iyr0) ;
0060 <span class="comment">%M_.jacobia=real(diffext('ff1_',[z; oo_.exo_steady_state])) ;</span>
0061 <span class="comment">%M_.jacobia=real(jacob_a('ff1_',[z; oo_.exo_steady_state])) ;</span>
0062 [junk,M_.jacobia] = feval([M_.fname <span class="string">'_dynamic'</span>],z,oo_.exo_simul);
0063 oo_.exo_simul = tempex ;
0064 tempex = [];
0065
0066 nz = size(z,1);
0067 k1 = M_.lead_lag_incidence(find([1:klen] ~= M_.maximum_lag+1),:);
0068 b = M_.jacobia(:,M_.lead_lag_incidence(M_.maximum_lag+1,order_var));
0069 a = b\M_.jacobia(:,nonzeros(k1'));
0070 <span class="keyword">if</span> any(isinf(a(:)))
0071 dr1_test_(1) = 5;
0072 dr1_test_(2) = bayestopt_.penalty;
0073 <span class="keyword">end</span>
0074 <span class="keyword">if</span> M_.exo_nbr
0075 fu = b\M_.jacobia(:,nz+1:end);
0076 <span class="keyword">end</span>
0077
0078 <span class="keyword">if</span> M_.maximum_lead == 0 &amp; M_.maximum_lag == 1; <span class="comment">% backward model with one lag</span>
0079 dr.ghx = -a;
0080 dr.ghu = -fu;
0081 <span class="keyword">return</span>;
0082 <span class="keyword">elseif</span> M_.maximum_lead == 0 &amp; M_.maximum_lag &gt; 1 <span class="comment">% backward model with lags on more than</span>
0083 <span class="comment">% one period</span>
0084 e = zeros(endo_nbr,nd);
0085 k = find(kstate(:,2) &lt;= M_.maximum_lag+1 &amp; kstate(:,4));
0086 e(:,k) = -a(:,kstate(k,4)) ;
0087 dr.ghx = e;
0088 dr.ghu = -fu;
0089 <span class="keyword">end</span>
0090
0091 <span class="comment">% buildind D and E</span>
0092 d = zeros(nd,nd) ;
0093 e = d ;
0094
0095 k = find(kstate(:,2) &gt;= M_.maximum_lag+2 &amp; kstate(:,3));
0096 d(1:sdyn,k) = a(nstatic+1:<span class="keyword">end</span>,kstate(k,3)) ;
0097 k1 = find(kstate(:,2) == M_.maximum_lag+2);
0098 a1 = eye(sdyn);
0099 e(1:sdyn,k1) = -a1(:,kstate(k1,1)-nstatic);
0100 k = find(kstate(:,2) &lt;= M_.maximum_lag+1 &amp; kstate(:,4));
0101 e(1:sdyn,k) = -a(nstatic+1:<span class="keyword">end</span>,kstate(k,4)) ;
0102 k2 = find(kstate(:,2) == M_.maximum_lag+1);
0103 k2 = k2(~ismember(kstate(k2,1),kstate(k1,1)));
0104 d(1:sdyn,k2) = a1(:,kstate(k2,1)-nstatic);
0105
0106 <span class="keyword">if</span> ~isempty(kad)
0107 <span class="keyword">for</span> j = 1:size(kad,1)
0108 d(sdyn+j,kad(j)) = 1 ;
0109 e(sdyn+j,kae(j)) = 1 ;
0110 <span class="keyword">end</span>
0111 <span class="keyword">end</span>
0112 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'qz_criterium'</span>,1.000001);
0113
0114 <span class="keyword">if</span> ~exist(<span class="string">'mjdgges'</span>)
0115 <span class="comment">% using Chris Sim's routines</span>
0116 use_qzdiv = 1;
0117 [ss,tt,qq,w] = qz(e,d);
0118 [tt,ss,qq,w] = <a href="qzdiv.html" class="code" title="function [A,B,Q,Z] = qzdiv(stake,A,B,Q,Z)">qzdiv</a>(options_.qz_criterium,tt,ss,qq,w);
0119 ss1=diag(ss);
0120 tt1=diag(tt);
0121 warning_state = warning;
0122 warning off;
0123 oo_.eigenvalues = ss1./tt1 ;
0124 warning warning_state;
0125 nba = nnz(abs(eigval) &gt; options_.qz_criterium);
0126 <span class="keyword">else</span>
0127 use_qzdiv = 0;
0128 [ss,tt,w,sdim,oo_.eigenvalues,info] = mjdgges(e,d,options_.qz_criterium);
0129 <span class="keyword">if</span> info &amp; info ~= nd+2;
0130 error([<span class="string">'ERROR'</span> info <span class="string">' in MJDGGES.DLL'</span>]);
0131 <span class="keyword">end</span>
0132 nba = nd-sdim;
0133 <span class="keyword">end</span>
0134
0135 nyf = sum(kstate(:,2) &gt; M_.maximum_lag+1);
0136
0137 <span class="keyword">if</span> cheik
0138 dr.rank = rank(w(1:nyf,nd-nyf+1:end));
0139 <span class="comment">% dr.eigval = oo_.eigenvalues;</span>
0140 <span class="keyword">return</span>
0141 <span class="keyword">end</span>
0142
0143 eigenvalues = sort(oo_.eigenvalues);
0144
0145 <span class="keyword">if</span> nba &gt; nyf;
0146 <span class="comment">% disp('Instability !');</span>
0147 dr1_test_(1) = 3; <span class="comment">%% More eigenvalues superior to unity than forward variables ==&gt; instability.</span>
0148 dr1_test_(2) = (abs(eigenvalues(nd-nba+1:nd-nyf))-1-1e-5)'*<span class="keyword">...</span>
0149 (abs(eigenvalues(nd-nba+1:nd-nyf))-1-1e-5);<span class="comment">% Distance to Blanchard-Khan conditions (penalty)</span>
0150 <span class="keyword">return</span>
0151 <span class="keyword">elseif</span> nba &lt; nyf;
0152 <span class="comment">% disp('Indeterminacy !');</span>
0153 dr1_test_(1) = 2; <span class="comment">%% ==&gt; Indeterminacy.</span>
0154 dr1_test_(2) = (abs(eigenvalues(nd-nyf+1:nd-nba))-1-1e-5)'*<span class="keyword">...</span>
0155 (abs(eigenvalues(nd-nyf+1:nd-nba))-1-1e-5);<span class="comment">% Distance to Blanchard-Khan conditions (penality)</span>
0156 <span class="comment">%% warning('DR1: Blanchard-Kahn conditions are not satisfied. Run CHEIK to learn more!');</span>
0157 <span class="keyword">return</span>
0158 <span class="keyword">end</span>
0159
0160 np = nd - nyf;
0161 n2 = np + 1;
0162 n3 = nyf;
0163 n4 = n3 + 1;
0164 <span class="comment">% derivatives with respect to dynamic state variables</span>
0165 <span class="comment">% forward variables</span>
0166
0167 <span class="keyword">if</span> condest(w(1:n3,n2:nd)) &gt; 1e9
0168 <span class="comment">% disp('Indeterminacy !!');</span>
0169 dr1_test_(1) = 2;
0170 dr1_test_(2) = 1;
0171 <span class="keyword">return</span>
0172 <span class="keyword">end</span>
0173
0174 warning_state = warning;
0175 lastwarn(<span class="string">''</span>);
0176 warning off;
0177 gx = -w(1:n3,n2:nd)'\w(n4:nd,n2:nd)';
0178
0179 <span class="keyword">if</span> length(lastwarn) &gt; 0;
0180 <span class="comment">% disp('Indeterminacy !!');</span>
0181 dr1_test_(1) = 2;
0182 dr1_test_(2) = 1;
0183 warning(warning_state);
0184 <span class="keyword">return</span>
0185 <span class="keyword">end</span>
0186
0187 <span class="comment">% predetermined variables</span>
0188 hx = w(1:n3,1:np)'*gx+w(n4:nd,1:np)';
0189 hx = (tt(1:np,1:np)*hx)\(ss(1:np,1:np)*hx);
0190
0191 lastwarn(<span class="string">''</span>);
0192 <span class="keyword">if</span> length(lastwarn) &gt; 0;
0193 <span class="comment">% disp('Singularity problem in dr11.m');</span>
0194 dr1_test_(1) = 2;
0195 dr1_test_(2) = 1;
0196 warning(warning_state);
0197 <span class="keyword">return</span>
0198 <span class="keyword">end</span>
0199
0200 k1 = find(kstate(n4:nd,2) == M_.maximum_lag+1);
0201 k2 = find(kstate(1:n3,2) == M_.maximum_lag+2);
0202 dr.ghx = [hx(k1,:); gx(k2(nboth+1:end),:)];
0203
0204 <span class="comment">%lead variables actually present in the model</span>
0205 j3 = nonzeros(kstate(:,3));
0206 j4 = find(kstate(:,3));
0207 <span class="comment">% derivatives with respect to exogenous variables</span>
0208 <span class="keyword">if</span> M_.exo_nbr
0209 a1 = eye(M_.endo_nbr);
0210 aa1 = [];
0211 <span class="keyword">if</span> nstatic &gt; 0
0212 aa1 = a1(:,1:nstatic);
0213 <span class="keyword">end</span>
0214 dr.ghu = -[aa1 a(:,j3)*gx(j4,1:npred)+a1(:,nstatic+1:nstatic+ <span class="keyword">...</span>
0215 npred) a1(:,nstatic+npred+1:end)]\fu;
0216
0217
0218 lastwarn(<span class="string">''</span>);
0219 <span class="keyword">if</span> length(lastwarn) &gt; 0;
0220 <span class="comment">% disp('Singularity problem in dr11.m');</span>
0221 dr1_test_(1) = 2;
0222 dr1_test_(2) = 1;
0223 <span class="keyword">return</span>
0224 <span class="keyword">end</span>
0225 <span class="keyword">end</span>
0226 warning(warning_state);
0227
0228 <span class="comment">% static variables</span>
0229 <span class="keyword">if</span> nstatic &gt; 0
0230 temp = -a(1:nstatic,j3)*gx(j4,:)*hx;
0231 j5 = find(kstate(n4:nd,4));
0232 temp(:,j5) = temp(:,j5)-a(1:nstatic,nonzeros(kstate(:,4)));
0233 dr.ghx = [temp; dr.ghx];
0234 temp = [];
0235 <span class="keyword">end</span>
0236
0237 <span class="keyword">if</span> options_.loglinear == 1
0238 k = find(dr.kstate(:,2) &lt;= M_.maximum_lag+1);
0239 klag = dr.kstate(k,[1 2]);
0240 k1 = dr.order_var;
0241
0242 dr.ghx = repmat(1./dr.ys(k1),1,size(dr.ghx,2)).*dr.ghx.* <span class="keyword">...</span>
0243 repmat(dr.ys(k1(klag(:,1)))',size(dr.ghx,1),1);
0244 dr.ghu = repmat(1./dr.ys(k1),1,size(dr.ghu,2)).*dr.ghu;
0245 <span class="keyword">end</span>
0246
0247 <span class="comment">% necessary when using Sims' routines</span>
0248 <span class="keyword">if</span> use_qzdiv
0249 gx = real(gx);
0250 hx = real(hx);
0251 dr.ghx = real(dr.ghx);
0252 dr.ghu = real(dr.ghu);
0253 <span class="keyword">end</span>
0254
0255</pre></div>
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<title>Description of dr2</title>
<meta name="keywords" content="dr2">
<meta name="description" content="Copyright (C) 2001 Michel Juillard">
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<h1>dr2
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>Copyright (C) 2001 Michel Juillard</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function ghs2=dr2(ys,dr) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> Copyright (C) 2001 Michel Juillard
function used for dr_algo == 1</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="dr1.html" class="code" title="function [dr,info]=dr1(dr,task)">dr1</a> Copyright (C) 2001 Michel Juillard</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <span class="comment">% Copyright (C) 2001 Michel Juillard</span>
0002 <span class="comment">%</span>
0003 <span class="comment">% function used for dr_algo == 1</span>
0004 <a name="_sub0" href="#_subfunctions" class="code">function ghs2=dr2(ys,dr)</a>
0005 <span class="keyword">global</span> M_
0006
0007 dr.ys = ys;
0008 fh = str2func([M_.fname <span class="string">'_static'</span>]);
0009 dr.fbias = 2*feval(fh,dr.ys);
0010 dr=<a href="dr1.html" class="code" title="function [dr,info]=dr1(dr,task)">dr1</a>(dr,0);
0011 ghs2 = dr.ghs2;</pre></div>
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<title>Description of draw_prior_density</title>
<meta name="keywords" content="draw_prior_density">
<meta name="description" content="stephane.adjemian@ens.fr [07-15-2004]">
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<!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png">&nbsp;Master index</a></td>
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<h1>draw_prior_density
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>stephane.adjemian@ens.fr [07-15-2004]</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function [x,f,abscissa,dens,binf,bsup] = draw_prior_density(indx); </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> stephane.adjemian@ens.fr [07-15-2004]</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="lpdfgam.html" class="code" title="function ldens = lpdfgam(x,a,b);">lpdfgam</a> log GAMMA PDF</li><li><a href="mj_qgamma.html" class="code" title="function x = mj_qgamma(p,a)">mj_qgamma</a> MJ_QGAMMA The gamma inverse distribution function</li><li><a href="qbeta.html" class="code" title="function x = qbeta(p,a,b)">qbeta</a> QBETA The beta inverse distribution function</li><li><a href="qgamma.html" class="code" title="function x = qgamma(p,a)">qgamma</a> QGAMMA The gamma inverse distribution function</li><li><a href="qnorm.html" class="code" title="function x = qnorm(p,m,s)">qnorm</a> QNORM The normal inverse distribution function</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="PlotPosteriorDistributions.html" class="code" title="function PlotPosteriorDistributions()">PlotPosteriorDistributions</a> stephane.adjemian@ens.fr [09-09-2005]</li><li><a href="plot_priors.html" class="code" title="function plot_priors">plot_priors</a> stephane.adjemian@cepremap.cnrs.fr [07-31-2004]</li><li><a href="posterior_distribution.html" class="code" title="function [borneinf,bornesup,x1,x2,f1,f2,top,nam,texnam] =posterior_distribution(indx,number_of_mh_files,first_mh_file,first_line,number_of_blocks,number_of_simulations,number_of_simulations_per_file,TeX);">posterior_distribution</a> stephane.adjemian@cepremap.cnrs.fr [07-15-2004]</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function [x,f,abscissa,dens,binf,bsup] = draw_prior_density(indx);</a>
0002 <span class="comment">% stephane.adjemian@ens.fr [07-15-2004]</span>
0003
0004 <span class="keyword">global</span> bayestopt_
0005
0006 pmean = bayestopt_.pmean;
0007 pshape = bayestopt_.pshape;
0008 p1 = bayestopt_.p1;
0009 p2 = bayestopt_.p2;
0010 p3 = bayestopt_.p3;
0011 p4 = bayestopt_.p4;
0012
0013 truncprior = 10^(-3);
0014
0015 <span class="keyword">if</span> pshape(indx) == 1 <span class="comment">%/* BETA Prior */</span>
0016 density = inline(<span class="string">'((bb-x).^(b-1)).*(x-aa).^(a-1)./(beta(a,b)*(bb-aa)^(a+b-1))'</span>,<span class="string">'x'</span>,<span class="string">'a'</span>,<span class="string">'b'</span>,<span class="string">'aa'</span>,<span class="string">'bb'</span>);
0017 mu = (p1(indx)-p3(indx))/(p4(indx)-p3(indx));
0018 stdd = p2(indx)/(p4(indx)-p3(indx));
0019 a = (1-mu)*mu^2/stdd^2 - mu;
0020 b = a*(1/mu-1);
0021 aa = p3(indx);
0022 bb = p4(indx);
0023 infbound = <a href="qbeta.html" class="code" title="function x = qbeta(p,a,b)">qbeta</a>(truncprior,a,b)*(bb-aa)+aa;
0024 supbound = <a href="qbeta.html" class="code" title="function x = qbeta(p,a,b)">qbeta</a>(1-truncprior,a,b)*(bb-aa)+aa;
0025 stepsize = (supbound-infbound)/200;
0026 abscissa = infbound:stepsize:supbound;
0027 dens = density(abscissa,a,b,aa,bb);
0028 <span class="keyword">elseif</span> pshape(indx) == 2 <span class="comment">%/* GAMMA PRIOR */</span>
0029 mu = p1(indx)-p3(indx);
0030 b = p2(indx)^2/mu;
0031 a = mu/b;
0032 infbound = <a href="mj_qgamma.html" class="code" title="function x = mj_qgamma(p,a)">mj_qgamma</a>(truncprior,a)*b;
0033 supbound = <a href="mj_qgamma.html" class="code" title="function x = mj_qgamma(p,a)">mj_qgamma</a>(1-truncprior,a)*b;
0034 stepsize = (supbound-infbound)/200;
0035 abscissa = infbound:stepsize:supbound;
0036 dens = exp(<a href="lpdfgam.html" class="code" title="function ldens = lpdfgam(x,a,b);">lpdfgam</a>(abscissa,a,b));
0037 abscissa = abscissa + p3(indx);
0038 <span class="keyword">elseif</span> pshape(indx) == 3 <span class="comment">%/* GAUSSIAN PRIOR */</span>
0039 density = inline(<span class="string">'inv(sqrt(2*pi)*b)*exp(-0.5*((x-a)/b).^2)'</span>,<span class="string">'x'</span>,<span class="string">'a'</span>,<span class="string">'b'</span>);
0040 a = p1(indx);
0041 b = p2(indx);
0042 infbound = <a href="qnorm.html" class="code" title="function x = qnorm(p,m,s)">qnorm</a>(truncprior,a,b);
0043 supbound = <a href="qnorm.html" class="code" title="function x = qnorm(p,m,s)">qnorm</a>(1-truncprior,a,b);
0044 stepsize = (supbound-infbound)/200;
0045 abscissa = infbound:stepsize:supbound;
0046 dens = density(abscissa,a,b);
0047 <span class="keyword">elseif</span> pshape(indx) == 4 <span class="comment">%/* INVGAMMA PRIOR type 1 */</span>
0048 density = inline(<span class="string">'2*inv(gamma(nu/2))*(x.^(-nu-1))*((s/2)^(nu/2)).*exp(-s./(2*x.^2))'</span>,<span class="string">'x'</span>,<span class="string">'s'</span>,<span class="string">'nu'</span>);
0049 nu = p2(indx);
0050 s = p1(indx);
0051 a = nu/2;
0052 b = 2/s;
0053 infbound = 1/sqrt(<a href="mj_qgamma.html" class="code" title="function x = mj_qgamma(p,a)">mj_qgamma</a>(1-10*truncprior,a)*b);
0054 supbound = 1/sqrt(<a href="mj_qgamma.html" class="code" title="function x = mj_qgamma(p,a)">mj_qgamma</a>(10*truncprior,a)*b);
0055 stepsize = (supbound-infbound)/200;
0056 abscissa = infbound:stepsize:supbound;
0057 dens = density(abscissa,s,nu);
0058 <span class="keyword">elseif</span> pshape(indx) == 5 <span class="comment">%/* UNIFORM PRIOR */</span>
0059 density = inline(<span class="string">'(x.^0)/(b-a)'</span>,<span class="string">'x'</span>,<span class="string">'a'</span>,<span class="string">'b'</span>);
0060 a = p1(indx);
0061 b = p2(indx);
0062 infbound = a;
0063 supbound = b;
0064 stepsize = (supbound-infbound)/200;
0065 abscissa = infbound:stepsize:supbound;
0066 dens = density(abscissa,a,b);
0067 <span class="keyword">elseif</span> pshape(indx) == 6 <span class="comment">%/* INVGAMMA PRIOR type 2 */</span>
0068 density = inline(<span class="string">'inv(gamma(nu/2))*(x.^(-.5(nu+2)))*((s/2)^(nu/2)).*exp(-s./(2*x))'</span>,<span class="string">'x'</span>,<span class="string">'s'</span>,<span class="string">'nu'</span>);
0069 nu = p2(indx);
0070 s = p1(indx);
0071 a = nu/2;
0072 b = 2/s;
0073 infbound = 1/(<a href="qgamma.html" class="code" title="function x = qgamma(p,a)">qgamma</a>(1-truncprior,a)*b);
0074 supbound = 1/(<a href="qgamma.html" class="code" title="function x = qgamma(p,a)">qgamma</a>(truncprior,a)*b);
0075 stepsize = (supbound-infbound)/200;
0076 abscissa = infbound:stepsize:supbound;
0077 dens = density(abscissa,s,nu);
0078 <span class="keyword">end</span>
0079
0080 k = [1:length(dens)];
0081 <span class="keyword">if</span> pshape(indx) ~= 5
0082 [junk,k1] = max(dens);
0083 <span class="keyword">if</span> k1 == 1 | k1 == length(dens)
0084 k = find(dens &lt; 10);
0085 <span class="keyword">end</span>
0086 <span class="keyword">end</span>
0087 binf = abscissa(k(1));
0088 bsup = abscissa(k(length(k)));
0089 x = abscissa(k);
0090 f = dens(k);</pre></div>
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<title>Description of dsample</title>
<meta name="keywords" content="dsample">
<meta name="description" content="Copyright (C) 2001 Michel Juillard">
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<h1>dsample
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>Copyright (C) 2001 Michel Juillard</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function dsample(s1,s2) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> Copyright (C) 2001 Michel Juillard
DSAMPLE : DSAMPLE(d1,d2)
This optional command permits to reduce the number of
periods considered in following output commands.
If only one argument is
provided, output is from period 1 to the period
specified in the DSAMPLE command. If two arguments are
present output is done for the interval between the
two periods.
DSAMPLE without arguments reset the sample to the one
specified by PERIODS</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <span class="comment">% Copyright (C) 2001 Michel Juillard</span>
0002 <span class="comment">%</span>
0003 <a name="_sub0" href="#_subfunctions" class="code">function dsample(s1,s2)</a>
0004 <span class="comment">% DSAMPLE : DSAMPLE(d1,d2)</span>
0005 <span class="comment">% This optional command permits to reduce the number of</span>
0006 <span class="comment">% periods considered in following output commands.</span>
0007 <span class="comment">% If only one argument is</span>
0008 <span class="comment">% provided, output is from period 1 to the period</span>
0009 <span class="comment">% specified in the DSAMPLE command. If two arguments are</span>
0010 <span class="comment">% present output is done for the interval between the</span>
0011 <span class="comment">% two periods.</span>
0012 <span class="comment">% DSAMPLE without arguments reset the sample to the one</span>
0013 <span class="comment">% specified by PERIODS</span>
0014
0015 <span class="keyword">global</span> options_
0016
0017 options_.smpl = zeros(2,1) ;
0018
0019 <span class="keyword">if</span> s1 &gt; options_.periods | s2 &gt; options_.periods
0020 t = [<span class="string">'DYNARE dsample error: one of the arguments is larger than the one'</span> <span class="keyword">...</span>
0021 <span class="string">' specified in PERIODS'</span>];
0022 error(t);
0023 <span class="keyword">end</span>
0024
0025 <span class="keyword">if</span> nargin == 0
0026 options_.smpl(1) = 1 ;
0027 options_.smpl(2) = options_.periods ;
0028 <span class="keyword">elseif</span> nargin == 1
0029 options_.smpl(1) = 1 ;
0030 options_.smpl(2) = s1 ;
0031 <span class="keyword">else</span>
0032 options_.smpl(1) = s1 ;
0033 options_.smpl(2) = s2 ;
0034 <span class="keyword">end</span>
0035
0036 <span class="comment">% 02/23/01 MJ added error checking</span></pre></div>
<hr><address>Generated on Fri 16-Jun-2006 09:09:06 by <strong><a href="http://www.artefact.tk/software/matlab/m2html/">m2html</a></strong> &copy; 2003</address>
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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"
"http://www.w3.org/TR/REC-html40/loose.dtd">
<html>
<head>
<title>Description of dy_date</title>
<meta name="keywords" content="dy_date">
<meta name="description" content="">
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
<meta name="generator" content="m2html &copy; 2003 Guillaume Flandin">
<meta name="robots" content="index, follow">
<link type="text/css" rel="stylesheet" href="../m2html.css">
</head>
<body>
<a name="_top"></a>
<div><a href="../index.html">Home</a> &gt; <a href="index.html">.</a> &gt; dy_date.m</div>
<!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png">&nbsp;Master index</a></td>
<td align="right"><a href="index.html">Index for .&nbsp;<img alt=">" border="0" src="../right.png"></a></td></tr></table>-->
<h1>dy_date
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong></strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function y=dy_date(year,period) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"></pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="initvalf_.html" class="code" title="function initvalf_(fname,period,varargin)">initvalf_</a> </li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function y=dy_date(year,period)</a>
0002 <span class="keyword">global</span> M_
0003
0004 y = M_.freq*(year-M_.start_date(1))+period-M_.start_date(2)+1;
0005
0006</pre></div>
<hr><address>Generated on Fri 16-Jun-2006 09:09:06 by <strong><a href="http://www.artefact.tk/software/matlab/m2html/">m2html</a></strong> &copy; 2003</address>
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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"
"http://www.w3.org/TR/REC-html40/loose.dtd">
<html>
<head>
<title>Description of dyn2vec</title>
<meta name="keywords" content="dyn2vec">
<meta name="description" content="Copyright (C) 2001 Michel Juillard">
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
<meta name="generator" content="m2html &copy; 2003 Guillaume Flandin">
<meta name="robots" content="index, follow">
<link type="text/css" rel="stylesheet" href="../m2html.css">
</head>
<body>
<a name="_top"></a>
<div><a href="../index.html">Home</a> &gt; <a href="index.html">.</a> &gt; dyn2vec.m</div>
<!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png">&nbsp;Master index</a></td>
<td align="right"><a href="index.html">Index for .&nbsp;<img alt=">" border="0" src="../right.png"></a></td></tr></table>-->
<h1>dyn2vec
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>Copyright (C) 2001 Michel Juillard</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function [z,zss]=dyn2vec(s1,s2); </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> Copyright (C) 2001 Michel Juillard</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="olr.html" class="code" title="function olr(var_list,olr_inst,obj_var,W)">olr</a> Copyright (C) 2001 Michel Juillard</li><li><a href="osr.html" class="code" title="function osr(var_list,params,W)">osr</a> Copyright (C) 2001 Michel Juillard</li><li><a href="simul.html" class="code" title="function simul(dr)">simul</a> Copyright (C) 2001 Michel Juillard</li><li><a href="stoch_simul.html" class="code" title="function info=stoch_simul(var_list)">stoch_simul</a> Copyright (C) 2001 Michel Juillard</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <span class="comment">% Copyright (C) 2001 Michel Juillard</span>
0002 <span class="comment">%</span>
0003 <a name="_sub0" href="#_subfunctions" class="code">function [z,zss]=dyn2vec(s1,s2);</a>
0004
0005 <span class="keyword">global</span> M_ oo_ options_
0006
0007 <span class="keyword">if</span> options_.smpl == 0
0008 k = [1:size(oo_.y_simul,2)];
0009 <span class="keyword">else</span>
0010 k = [M_.maximum_lag+options_.smpl(1):M_.maximum_lag+options_.smpl(2)];
0011 <span class="keyword">end</span>
0012
0013 <span class="keyword">if</span> nargin == 0
0014 <span class="keyword">if</span> nargout &gt; 0
0015 t = [<span class="string">'DYNARE dyn2vec error: the function doesn''t return values when'</span> <span class="keyword">...</span>
0016 <span class="string">' used without input argument'</span>];
0017 error(t);
0018 <span class="keyword">end</span>
0019 <span class="keyword">for</span> i=1:size(oo_.y_simul,1)
0020 assignin(<span class="string">'base'</span>,deblank(M_.endo_names(i,:)),oo_.y_simul(i,k)');
0021 <span class="keyword">end</span>
0022 <span class="keyword">return</span>
0023 <span class="keyword">else</span>
0024 j = strmatch(s1,M_.endo_names,<span class="string">'exact'</span>);
0025 <span class="keyword">if</span> ~ isempty(j)
0026 z = oo_.y_simul(j,k)';
0027 <span class="keyword">else</span>
0028 j = strmatch(s1,M_.exo_names,<span class="string">'exact'</span>);
0029 <span class="keyword">if</span> ~ isempty(j)
0030 <span class="keyword">if</span> options_.smpl == 0
0031 z = oo_.exo_simul(:,j);
0032 <span class="keyword">else</span>
0033 z = oo_.exo_simul(M_.maximum_lag+options_.smpl(1):M_.maximum_lag+options_.smpl(2));
0034 <span class="keyword">end</span>
0035 <span class="keyword">else</span>
0036 t = [<span class="string">'DYNARE dyn2vec error: variable '</span> deblank(s1(i,:)) <span class="string">' doesn''t'</span> <span class="keyword">...</span>
0037 <span class="string">' exist.'</span>] ;
0038 error (t) ;
0039 <span class="keyword">end</span>
0040 <span class="keyword">end</span>
0041 <span class="keyword">end</span>
0042
0043 <span class="keyword">if</span> nargout == 0
0044 <span class="keyword">if</span> nargin == 1
0045 assignin(<span class="string">'base'</span>,s1,z);
0046 <span class="keyword">elseif</span> nargin == 2
0047 assignin(<span class="string">'base'</span>,s2,z);
0048 <span class="keyword">end</span>
0049 <span class="keyword">else</span>
0050 zss=oo_.steady_state(j);
0051 <span class="keyword">end</span>
0052
0053 <span class="comment">% 02/23/01 MJ redone, incorporating FC's improvements</span>
0054 <span class="comment">% 08/24/01 MJ replaced globlize by internal assignin</span>
0055 <span class="comment">% 08/24/01 MJ added 'exact' to strmatch (thanks to David Vavra)</span>
0056 <span class="comment">% 01/31/03 MJ added provision for alternative name of variable</span>
0057
0058
0059</pre></div>
<hr><address>Generated on Fri 16-Jun-2006 09:09:06 by <strong><a href="http://www.artefact.tk/software/matlab/m2html/">m2html</a></strong> &copy; 2003</address>
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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"
"http://www.w3.org/TR/REC-html40/loose.dtd">
<html>
<head>
<title>Description of dynare</title>
<meta name="keywords" content="dynare">
<meta name="description" content="Copyright (C) 2001 Michel Juillard">
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
<meta name="generator" content="m2html &copy; 2003 Guillaume Flandin">
<meta name="robots" content="index, follow">
<link type="text/css" rel="stylesheet" href="../m2html.css">
</head>
<body>
<a name="_top"></a>
<div><a href="../index.html">Home</a> &gt; <a href="index.html">.</a> &gt; dynare.m</div>
<!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png">&nbsp;Master index</a></td>
<td align="right"><a href="index.html">Index for .&nbsp;<img alt=">" border="0" src="../right.png"></a></td></tr></table>-->
<h1>dynare
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>Copyright (C) 2001 Michel Juillard</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function dynare(fname, varargin) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> Copyright (C) 2001 Michel Juillard
DYNARE ( 'Filename' )
This command runs dynare with specified model file in argument
Filename.
The name of model file begins with an alphabetic character,
and has a filename extension of .mod or .dyn.
When extension is omitted, a model file with .mod extension
is processed.</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <span class="comment">% Copyright (C) 2001 Michel Juillard</span>
0002 <span class="comment">%</span>
0003 <a name="_sub0" href="#_subfunctions" class="code">function dynare(fname, varargin)</a>
0004 <span class="comment">% DYNARE ( 'Filename' )</span>
0005 <span class="comment">% This command runs dynare with specified model file in argument</span>
0006 <span class="comment">% Filename.</span>
0007 <span class="comment">% The name of model file begins with an alphabetic character,</span>
0008 <span class="comment">% and has a filename extension of .mod or .dyn.</span>
0009 <span class="comment">% When extension is omitted, a model file with .mod extension</span>
0010 <span class="comment">% is processed.</span>
0011
0012 <span class="keyword">if</span> ~isstr(fname)
0013 error (<span class="string">'The argument in DYNARE must be a text string.'</span>) ;
0014 <span class="keyword">end</span>
0015 <span class="comment">% Testing if file have extension</span>
0016 <span class="comment">% If no extension defalut .mod is added</span>
0017 <span class="keyword">if</span> isempty(strfind(fname,<span class="string">'.'</span>))
0018 fname1 = [fname <span class="string">'.dyn'</span>];
0019 d = dir(fname1);
0020 <span class="keyword">if</span> length(d) == 0
0021 fname1 = [fname <span class="string">'.mod'</span>];
0022 <span class="keyword">end</span>
0023 fname = fname1;
0024 <span class="comment">% Checking file extension</span>
0025 <span class="keyword">else</span>
0026 <span class="keyword">if</span> ~strcmp(upper(fname(size(fname,2)-3:size(fname,2))),<span class="string">'.MOD'</span>) <span class="keyword">...</span>
0027 &amp;&amp; ~strcmp(upper(fname(size(fname,2)-3:size(fname,2))),<span class="string">'.DYN'</span>)
0028 error (<span class="string">'Argument is a file name with .mod or .dyn extension'</span>);
0029 <span class="keyword">end</span>;
0030 <span class="keyword">end</span>;
0031 d = dir(fname);
0032 <span class="keyword">if</span> length(d) == 0
0033 disp([<span class="string">'DYNARE: can''t open '</span> fname])
0034 <span class="keyword">return</span>
0035 <span class="keyword">end</span>
0036 dynareroot = strrep(which(<span class="string">'dynare.m'</span>),<span class="string">'dynare.m'</span>,<span class="string">''</span>);
0037 command = [dynareroot <span class="string">'dynare_m '</span> fname] ;
0038 <span class="keyword">for</span> i=2:nargin
0039 command = [command <span class="string">' '</span> varargin{i-1}];
0040 <span class="keyword">end</span>
0041 [status, result] = system(command);
0042 <span class="keyword">if</span> status
0043 error(result)
0044 <span class="keyword">end</span>
0045
0046 <span class="keyword">if</span> ~ isempty(find(abs(fname) == 46))
0047 fname = fname(:,1:find(abs(fname) == 46)-1) ;
0048 <span class="keyword">end</span>
0049 evalin(<span class="string">'base'</span>,fname) ;
0050
0051
0052 <span class="comment">% MJ 2/9/99: replace clear function by clear ff_</span>
0053 <span class="comment">% MJ 4/7/00: change the path of dynare_m</span>
0054 <span class="comment">% MJ 02/26/01: replaced local variable x by fname</span>
0055 <span class="comment">% MJ 09/19/01: evaluates mod script in 'base' workspace</span></pre></div>
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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"
"http://www.w3.org/TR/REC-html40/loose.dtd">
<html>
<head>
<title>Description of dynare_MC</title>
<meta name="keywords" content="dynare_MC">
<meta name="description" content="">
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
<meta name="generator" content="m2html &copy; 2003 Guillaume Flandin">
<meta name="robots" content="index, follow">
<link type="text/css" rel="stylesheet" href="../m2html.css">
</head>
<body>
<a name="_top"></a>
<div><a href="../index.html">Home</a> &gt; <a href="index.html">.</a> &gt; dynare_MC.m</div>
<!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png">&nbsp;Master index</a></td>
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<h1>dynare_MC
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong></strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function dynare_MC(var_list_) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"></pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="DsgeLikelihood.html" class="code" title="function [fval,cost_flag,ys,trend_coeff,info] = DsgeLikelihood(xparam1,gend,data)">DsgeLikelihood</a> stephane.adjemian@cepremap.cnrs.fr [09-07-2004]</li><li><a href="DsgeSmoother.html" class="code" title="function [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK] = DsgeSmoother(xparam1,gend,Y)">DsgeSmoother</a> stephane.adjemian@cepremap.cnrs.fr [09-07-2004]</li><li><a href="prior_bounds.html" class="code" title="function bounds = prior_bounds(bayestopt)">prior_bounds</a> </li><li><a href="read_variables.html" class="code" title="function dyn_data_01=read_variables(file_name_01,var_names_01,dyn_data_01,xls_sheet,xls_range)">read_variables</a> Copyright (C) 2005 Michel Juillard</li><li><a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a> </li><li><a href="set_prior.html" class="code" title="function [xparam1,estim_params_,bayestopt_,lb,ub]=set_prior(estim_params_)">set_prior</a> </li><li><a href="set_state_space.html" class="code" title="function dr=set_state_space(dr)">set_state_space</a> Copyright (C) 2001 Michel Juillard</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="dynare_sensitivity.html" class="code" title="function x0=dynare_sensitivity()">dynare_sensitivity</a> copyright Marco Ratto 2006</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function dynare_MC(var_list_)</a>
0002
0003 <span class="keyword">global</span> M_ options_ oo_ estim_params_
0004 <span class="keyword">global</span> bayestopt_
0005
0006 options_.varlist = var_list_;
0007 options_.lgyidx2varobs = zeros(size(M_.endo_names,1),1);
0008 <span class="keyword">for</span> i = 1:size(M_.endo_names,1)
0009 tmp = strmatch(deblank(M_.endo_names(i,:)),options_.varobs,<span class="string">'exact'</span>);
0010 <span class="keyword">if</span> ~isempty(tmp)
0011 options_.lgyidx2varobs(i,1) = tmp;
0012 <span class="keyword">end</span>
0013 <span class="keyword">end</span>
0014
0015 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'first_obs'</span>,1);
0016 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'prefilter'</span>,0);
0017 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'presample'</span>,0);
0018 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'lik_algo'</span>,1);
0019 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'lik_init'</span>,1);
0020 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'nograph'</span>,0);
0021 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'mh_conf_sig'</span>,0.90);
0022 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'mh_replic'</span>,20000);
0023 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'mh_drop'</span>,0.5);
0024 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'mh_jscale'</span>,0.2);
0025 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'mh_init_scale'</span>,2*options_.mh_jscale);
0026 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'mode_file'</span>,<span class="string">''</span>);
0027 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'mode_compute'</span>,4);
0028 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'mode_check'</span>,0);
0029 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'prior_trunc'</span>,1e-10);
0030 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'mh_mode'</span>,1);
0031 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'mh_nblck'</span>,2);
0032 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'load_mh_file'</span>,0);
0033 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'nodiagnostic'</span>,0);
0034 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'loglinear'</span>,0);
0035 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'unit_root_vars'</span>,[]);
0036 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'XTick'</span>,[]);
0037 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'XTickLabel'</span>,[]);
0038 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'bayesian_irf'</span>,0);
0039 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'bayesian_th_moments'</span>,0);
0040 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'TeX'</span>,0);
0041 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'irf'</span>,0);
0042 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'relative_irf'</span>,0);
0043 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'order'</span>,1);
0044 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'ar'</span>,5);
0045 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'dr_algo'</span>,0);
0046 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'linear'</span>,0);
0047 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'drop'</span>,0);
0048 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'replic'</span>,1);
0049 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'hp_filter'</span>,0);
0050 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'forecast'</span>,0);
0051 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'smoother'</span>,0);
0052 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'moments_varendo'</span>,0);
0053 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'filtered_vars'</span>,0);
0054 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'kalman_algo'</span>,1);
0055 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'kalman_tol'</span>,10^(-12));
0056 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'posterior_mode_estimation'</span>,1);
0057 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'MaxNumberOfBytes'</span>,1e6);
0058 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'xls_sheet'</span>,<span class="string">''</span>);
0059 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'xls_range'</span>,<span class="string">''</span>);
0060
0061 <span class="comment">%% Add something to the parser ++&gt;</span>
0062 M_.dname = M_.fname; <span class="comment">% The user should be able to choose another name</span>
0063 <span class="comment">% for the directory...</span>
0064
0065
0066 pnames = [<span class="string">' '</span>;<span class="string">'beta '</span>;<span class="string">'gamm '</span>;<span class="string">'norm '</span>;<span class="string">'invg '</span>;<span class="string">'unif '</span>;<span class="string">'invg2'</span>];
0067 n_varobs = size(options_.varobs,1);
0068
0069 [xparam1,estim_params_,bayestopt_,lb,ub] = <a href="set_prior.html" class="code" title="function [xparam1,estim_params_,bayestopt_,lb,ub]=set_prior(estim_params_)">set_prior</a>(estim_params_);
0070
0071 options_.mh_replic = 0;
0072
0073 bounds = <a href="prior_bounds.html" class="code" title="function bounds = prior_bounds(bayestopt)">prior_bounds</a>(bayestopt_);
0074 bounds(:,1)=max(bounds(:,1),lb);
0075 bounds(:,2)=min(bounds(:,2),ub);
0076
0077 <span class="keyword">if</span> any(xparam1 &lt; bounds(:,1)) | any(xparam1 &gt; bounds(:,2))
0078 find(xparam1 &lt; bounds(:,1))
0079 find(xparam1 &gt; bounds(:,2))
0080 error(<span class="string">'Initial parameter values are outside parameter bounds'</span>)
0081 <span class="keyword">end</span>
0082 lb = bounds(:,1);
0083 ub = bounds(:,2);
0084 bayestopt_.lb = lb;
0085 bayestopt_.ub = ub;
0086
0087 <span class="keyword">if</span> ~isfield(options_,<span class="string">'trend_coeffs'</span>)
0088 bayestopt_.with_trend = 0;
0089 <span class="keyword">else</span>
0090 bayestopt_.with_trend = 1;
0091 bayestopt_.trend_coeff = {};
0092 trend_coeffs = options_.trend_coeffs;
0093 nt = length(trend_coeffs);
0094 <span class="keyword">for</span> i=1:n_varobs
0095 <span class="keyword">if</span> i &gt; length(trend_coeffs)
0096 bayestopt_.trend_coeff{i} = <span class="string">'0'</span>;
0097 <span class="keyword">else</span>
0098 bayestopt_.trend_coeff{i} = trend_coeffs{i};
0099 <span class="keyword">end</span>
0100 <span class="keyword">end</span>
0101 <span class="keyword">end</span>
0102
0103 bayestopt_.penalty = 1e8; <span class="comment">% penalty</span>
0104
0105 nvx = estim_params_.nvx;
0106 nvn = estim_params_.nvn;
0107 ncx = estim_params_.ncx;
0108 ncn = estim_params_.ncn;
0109 np = estim_params_.np ;
0110 nx = nvx+nvn+ncx+ncn+np;
0111
0112 <span class="comment">%% Static solver</span>
0113 <span class="keyword">if</span> exist([M_.fname <span class="string">'_steadystate'</span>])
0114 bayestopt_.static_solve = [M_.fname <span class="string">'_steadystate'</span>];
0115 <span class="keyword">else</span>
0116 bayestopt_.static_solve = <span class="string">'dynare_solve'</span>;
0117 <span class="keyword">end</span>
0118
0119 dr = <a href="set_state_space.html" class="code" title="function dr=set_state_space(dr)">set_state_space</a>([]);
0120
0121 <span class="comment">%% Initialization with unit-root variables</span>
0122 <span class="keyword">if</span> ~isempty(options_.unit_root_vars)
0123 n_ur = size(options_.unit_root_vars,1);
0124 i_ur = zeros(n_ur,1);
0125 <span class="keyword">for</span> i=1:n_ur
0126 i1 = strmatch(deblank(options_.unit_root_vars(i,:)),M_.endo_names(dr.order_var,:),<span class="string">'exact'</span>);
0127 <span class="keyword">if</span> isempty(i1)
0128 error(<span class="string">'Undeclared variable in unit_root_vars statement'</span>)
0129 <span class="keyword">end</span>
0130 i_ur(i) = i1;
0131 <span class="keyword">end</span>
0132 <span class="keyword">if</span> M_.maximum_lag &gt; 1
0133 l1 = flipud([cumsum(M_.lead_lag_incidence(1:M_.maximum_lag-1,dr.order_var),1);ones(1,M_.endo_nbr)]);
0134 n1 = nnz(l1);
0135 bayestopt_.Pinf = zeros(n1,n1);
0136 l2 = find(l1');
0137 l3 = zeros(M_.endo_nbr,M_.maximum_lag);
0138 l3(i_ur,:) = l1(:,i_ur)';
0139 l3 = l3(:);
0140 i_ur1 = find(l3(l2));
0141 i_stable = ones(M_.endo_nbr,1);
0142 i_stable(i_ur) = zeros(n_ur,1);
0143 i_stable = find(i_stable);
0144 bayestopt_.Pinf(i_ur1,i_ur1) = diag(ones(1,length(i_ur1)));
0145 bayestopt_.i_var_stable = i_stable;
0146 l3 = zeros(M_.endo_nbr,M_.maximum_lag);
0147 l3(i_stable,:) = l1(:,i_stable)';
0148 l3 = l3(:);
0149 bayestopt_.i_T_var_stable = find(l3(l2));
0150 <span class="keyword">else</span>
0151 n1 = M_.endo_nbr;
0152 bayestopt_.Pinf = zeros(n1,n1);
0153 bayestopt_.Pinf(i_ur,i_ur) = diag(ones(1,length(i_ur)));
0154 l1 = ones(M_.endo_nbr,1);
0155 l1(i_ur,:) = zeros(length(i_ur),1);
0156 bayestopt_.i_T_var_stable = find(l1);
0157 <span class="keyword">end</span>
0158 options_.lik_init = 3;
0159 <span class="keyword">end</span> <span class="comment">% if ~isempty(options_.unit_root_vars)</span>
0160
0161 <span class="keyword">if</span> isempty(options_.datafile)
0162 error(<span class="string">'ESTIMATION: datafile option is missing'</span>)
0163 <span class="keyword">end</span>
0164
0165 <span class="keyword">if</span> isempty(options_.varobs)
0166 error(<span class="string">'ESTIMATION: VAROBS is missing'</span>)
0167 <span class="keyword">end</span>
0168
0169
0170 <span class="comment">%% If jscale isn't specified for an estimated parameter, use</span>
0171 <span class="comment">%% global option options_.jscale, set to 0.2, by default</span>
0172 k = find(isnan(bayestopt_.jscale));
0173 bayestopt_.jscale(k) = options_.mh_jscale;
0174
0175 <span class="comment">%% Read and demean data</span>
0176 rawdata = <a href="read_variables.html" class="code" title="function dyn_data_01=read_variables(file_name_01,var_names_01,dyn_data_01,xls_sheet,xls_range)">read_variables</a>(options_.datafile,options_.varobs,[],options_.xls_sheet,options_.xls_range);
0177
0178 k = [];
0179 k1 = [];
0180 <span class="keyword">for</span> i=1:n_varobs
0181 k = [k strmatch(deblank(options_.varobs(i,:)),M_.endo_names(dr.order_var,:), <span class="keyword">...</span>
0182 <span class="string">'exact'</span>)];
0183 k1 = [k1 strmatch(deblank(options_.varobs(i,:)),M_.endo_names, <span class="string">'exact'</span>)];
0184 <span class="keyword">end</span>
0185
0186 bayestopt_.mf = k;
0187 bayestopt_.mfys = k1;
0188 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'nobs'</span>,size(rawdata,1)-options_.first_obs+1);
0189 gend = options_.nobs;
0190
0191 rawdata = rawdata(options_.first_obs:options_.first_obs+gend-1,:);
0192 <span class="keyword">if</span> options_.loglinear == 1
0193 rawdata = log(rawdata);
0194 <span class="keyword">end</span>
0195 <span class="keyword">if</span> options_.prefilter == 1
0196 bayestopt_.mean_varobs = mean(rawdata,1);
0197 data = transpose(rawdata-ones(gend,1)*bayestopt_.mean_varobs);
0198 <span class="keyword">else</span>
0199 data = transpose(rawdata);
0200 <span class="keyword">end</span>
0201
0202 <span class="keyword">if</span> ~isreal(rawdata)
0203 error([<span class="string">'There are complex values in the data. Probably a wrong'</span> <span class="keyword">...</span>
0204 <span class="string">' transformation'</span>])
0205 <span class="keyword">end</span>
0206
0207 nvx = estim_params_.nvx;
0208 nvn = estim_params_.nvn;
0209 ncx = estim_params_.ncx;
0210 ncn = estim_params_.ncn;
0211 np = estim_params_.np ;
0212 npar = nvx+nvn+ncx+ncn+np;
0213 offset = npar-np;
0214 fname_=M_.fname;
0215
0216 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'opt_gsa'</span>,1);
0217 options_gsa_ = options_.opt_gsa;
0218
0219 <span class="keyword">if</span> options_gsa_.pprior,
0220 load([fname_,<span class="string">'_prior'</span>])
0221 namfile=[fname_,<span class="string">'_prior'</span>];
0222 <span class="keyword">else</span>
0223 load([fname_,<span class="string">'_mc'</span>])
0224 namfile=[fname_,<span class="string">'_mc'</span>];
0225 <span class="keyword">end</span>
0226 load(options_.mode_file)
0227 <span class="comment">%%</span>
0228 <span class="comment">%%</span>
0229 <span class="comment">%%</span>
0230 x=[lpmat0(istable,:) lpmat(istable,:)];
0231 clear lpmat lpmat0 istable iunstable egg yys T
0232 B = size(x,1);
0233 <span class="keyword">if</span> M_.maximum_lag &gt; 1
0234 l1 = flipud([cumsum(M_.lead_lag_incidence(1:M_.maximum_lag-1,dr.order_var),1);ones(1,M_.endo_nbr)]);
0235 n1 = nnz(l1);
0236 <span class="keyword">else</span>
0237 n1 = M_.endo_nbr;
0238 <span class="keyword">end</span>
0239 stock_smooth = zeros(gend,n1,B);
0240 stock_filter = zeros(gend+1,n1,B);
0241 stock_ys = zeros(M_.endo_nbr,B);
0242 <span class="comment">%%</span>
0243 h = waitbar(0,<span class="string">'MC smoother ...'</span>);
0244 <span class="keyword">for</span> b=1:B
0245 deep = x(b,:);
0246 <span class="comment">%deep(1:offset) = xparam1(1:offset);</span>
0247 logpo2(b) = <a href="DsgeLikelihood.html" class="code" title="function [fval,cost_flag,ys,trend_coeff,info] = DsgeLikelihood(xparam1,gend,data)">DsgeLikelihood</a>(deep',gend,data);
0248 [atT,innov,measurement_error,filtered_state_vector,ys,trend_coeff] = <a href="DsgeSmoother.html" class="code" title="function [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK] = DsgeSmoother(xparam1,gend,Y)">DsgeSmoother</a>(deep,gend,data);
0249 stock_smooth(:,:,b)=atT';
0250 stock_filter(:,:,b)=filtered_state_vector';
0251 stock_ys(:,b)=ys;
0252 waitbar(b/B,h,[<span class="string">'MC smoother ...'</span>,num2str(b),<span class="string">'/'</span>,num2str(B)]);
0253 <span class="keyword">end</span>
0254 close(h)
0255
0256 save(namfile,<span class="string">'x'</span>,<span class="string">'logpo2'</span>,<span class="string">'stock_smooth'</span>,<span class="string">'stock_filter'</span>,<span class="string">'stock_ys'</span>,<span class="string">'-append'</span>)</pre></div>
<hr><address>Generated on Fri 16-Jun-2006 09:09:06 by <strong><a href="http://www.artefact.tk/software/matlab/m2html/">m2html</a></strong> &copy; 2003</address>
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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"
"http://www.w3.org/TR/REC-html40/loose.dtd">
<html>
<head>
<title>Description of dynare_resolve</title>
<meta name="keywords" content="dynare_resolve">
<meta name="description" content="">
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
<meta name="generator" content="m2html &copy; 2003 Guillaume Flandin">
<meta name="robots" content="index, follow">
<link type="text/css" rel="stylesheet" href="../m2html.css">
</head>
<body>
<a name="_top"></a>
<div><a href="../index.html">Home</a> &gt; <a href="index.html">.</a> &gt; dynare_resolve.m</div>
<!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png">&nbsp;Master index</a></td>
<td align="right"><a href="index.html">Index for .&nbsp;<img alt=">" border="0" src="../right.png"></a></td></tr></table>-->
<h1>dynare_resolve
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong></strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function [A,B,ys,info] = dynare_resolve() </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"></pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="kalman_transition_matrix.html" class="code" title="function [A,B] = kalman_transition_matrix(dr)">kalman_transition_matrix</a> makes transition matrices out of ghx and ghu for Kalman filter</li><li><a href="resol.html" class="code" title="function [dr,info]=resol(ys,check_flag)">resol</a> Copyright (C) 2001 Michel Juillard</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="DsgeLikelihood.html" class="code" title="function [fval,cost_flag,ys,trend_coeff,info] = DsgeLikelihood(xparam1,gend,data)">DsgeLikelihood</a> stephane.adjemian@cepremap.cnrs.fr [09-07-2004]</li><li><a href="DsgeSmoother.html" class="code" title="function [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK] = DsgeSmoother(xparam1,gend,Y)">DsgeSmoother</a> stephane.adjemian@cepremap.cnrs.fr [09-07-2004]</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function [A,B,ys,info] = dynare_resolve()</a>
0002 <span class="keyword">global</span> oo_
0003
0004 [oo_.dr,info] = <a href="resol.html" class="code" title="function [dr,info]=resol(ys,check_flag)">resol</a>(oo_.steady_state,0);
0005
0006 <span class="keyword">if</span> info(1) &gt; 0
0007 A = [];
0008 B = [];
0009 ys = [];
0010 <span class="keyword">return</span>
0011 <span class="keyword">end</span>
0012
0013 [A,B] = <a href="kalman_transition_matrix.html" class="code" title="function [A,B] = kalman_transition_matrix(dr)">kalman_transition_matrix</a>(oo_.dr);
0014 ys = oo_.dr.ys;</pre></div>
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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"
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<head>
<title>Description of dynare_sensitivity</title>
<meta name="keywords" content="dynare_sensitivity">
<meta name="description" content="copyright Marco Ratto 2006">
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
<meta name="generator" content="m2html &copy; 2003 Guillaume Flandin">
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<!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png">&nbsp;Master index</a></td>
<td align="right"><a href="index.html">Index for .&nbsp;<img alt=">" border="0" src="../right.png"></a></td></tr></table>-->
<h1>dynare_sensitivity
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>copyright Marco Ratto 2006</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function x0=dynare_sensitivity() </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> copyright Marco Ratto 2006</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="CheckPath.html" class="code" title="function DirectoryName = CheckPath(type)">CheckPath</a> 06-03-2005</li><li><a href="dynare_MC.html" class="code" title="function dynare_MC(var_list_)">dynare_MC</a> </li><li><a href="filt_mc_.html" class="code" title="function [rmse_MC, ixx] = filt_mc_(vvarvecm, loadSA, pfilt, alpha, alpha2, OutDir, istart, alphaPC)">filt_mc_</a> copyright Marco Ratto 2006</li><li><a href="read_variables.html" class="code" title="function dyn_data_01=read_variables(file_name_01,var_names_01,dyn_data_01,xls_sheet,xls_range)">read_variables</a> Copyright (C) 2005 Michel Juillard</li><li><a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a> </li><li><a href="stab_map_.html" class="code" title="function x0 = stab_map_(Nsam, fload, ksstat, alpha2, prepSA, pprior, ilptau, OutputDirectoryName)">stab_map_</a> </li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function x0=dynare_sensitivity()</a>
0002 <span class="comment">% copyright Marco Ratto 2006</span>
0003
0004 <span class="keyword">global</span> M_ options_ oo_ bayestopt_
0005
0006 fname_ = M_.fname;
0007 lgy_ = M_.endo_names;
0008 x0=[];
0009
0010 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'opt_gsa'</span>,1);
0011 options_gsa_ = options_.opt_gsa;
0012
0013 <span class="comment">% map stability</span>
0014 options_gsa_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_gsa_,<span class="string">'stab'</span>,1);
0015 options_gsa_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_gsa_,<span class="string">'redform'</span>,0);
0016 options_gsa_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_gsa_,<span class="string">'pprior'</span>,1);
0017 options_gsa_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_gsa_,<span class="string">'ppost'</span>,0);
0018 options_gsa_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_gsa_,<span class="string">'ilptau'</span>,1);
0019 options_gsa_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_gsa_,<span class="string">'Nsam'</span>,2048);
0020 options_gsa_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_gsa_,<span class="string">'load_stab'</span>,0);
0021 options_gsa_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_gsa_,<span class="string">'alpha2_stab'</span>,0.3);
0022 options_gsa_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_gsa_,<span class="string">'ksstat'</span>,0.1);
0023 options_gsa_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_gsa_,<span class="string">'load_mh'</span>,0);
0024
0025 OutputDirectoryName = <a href="CheckPath.html" class="code" title="function DirectoryName = CheckPath(type)">CheckPath</a>(<span class="string">'GSA'</span>);
0026
0027
0028 <span class="keyword">if</span> options_gsa_.stab &amp; ~options_gsa_.load_mh,
0029 x0 = <a href="stab_map_.html" class="code" title="function x0 = stab_map_(Nsam, fload, ksstat, alpha2, prepSA, pprior, ilptau, OutputDirectoryName)">stab_map_</a>(options_gsa_.Nsam, options_gsa_.load_stab, options_gsa_.ksstat, options_gsa_.alpha2_stab, <span class="keyword">...</span>
0030 options_gsa_.redform, options_gsa_.pprior, options_gsa_.ilptau, OutputDirectoryName);
0031 <span class="keyword">end</span>
0032
0033 <span class="comment">% reduced form</span>
0034 <span class="comment">% redform_map(namendo, namlagendo, namexo, icomp, pprior, ilog, threshold)</span>
0035 options_gsa_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_gsa_,<span class="string">'load_redform'</span>,0);
0036 options_gsa_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_gsa_,<span class="string">'logtrans_redform'</span>,0);
0037 options_gsa_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_gsa_,<span class="string">'threshold_redform'</span>,[]);
0038 options_gsa_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_gsa_,<span class="string">'ksstat_redform'</span>,0.1);
0039 options_gsa_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_gsa_,<span class="string">'alpha2_redform'</span>,0.3);
0040 options_gsa_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_gsa_,<span class="string">'namendo'</span>,[]);
0041 options_gsa_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_gsa_,<span class="string">'namlagendo'</span>,[]);
0042 options_gsa_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_gsa_,<span class="string">'namexo'</span>,[]);
0043
0044 <span class="keyword">if</span> options_gsa_.redform &amp; ~isempty(options_gsa_.namendo) &amp; ~options_gsa_.load_mh,
0045 redform_map(options_gsa_.namendo, options_gsa_.namlagendo, options_gsa_.namexo, <span class="keyword">...</span>
0046 options_gsa_.load_redform, options_gsa_.pprior, options_gsa_.logtrans_redform, <span class="keyword">...</span>
0047 options_gsa_.threshold_redform, options_gsa_.ksstat_redform, <span class="keyword">...</span>
0048 options_gsa_.alpha2_redform, OutputDirectoryName);
0049 <span class="keyword">end</span>
0050 <span class="comment">% RMSE mapping</span>
0051 <span class="comment">% function [rmse_MC, ixx] = filt_mc_(vvarvecm, loadSA, pfilt, alpha, alpha2)</span>
0052 options_gsa_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_gsa_,<span class="string">'rmse'</span>,0);
0053 options_gsa_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_gsa_,<span class="string">'var_rmse'</span>,options_.varobs);
0054 options_gsa_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_gsa_,<span class="string">'load_rmse'</span>,0);
0055 options_gsa_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_gsa_,<span class="string">'pfilt_rmse'</span>,0.1);
0056 options_gsa_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_gsa_,<span class="string">'istart_rmse'</span>,1);
0057 options_gsa_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_gsa_,<span class="string">'alpha_rmse'</span>,0.002);
0058 options_gsa_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_gsa_,<span class="string">'alpha2_rmse'</span>,0.5);
0059 options_.opt_gsa = options_gsa_;
0060 <span class="keyword">if</span> options_gsa_.rmse,
0061 <span class="keyword">if</span> options_gsa_.pprior
0062 a=load([OutputDirectoryName,<span class="string">'\'</span>,fname_,<span class="string">'_prior'</span>]);
0063 <span class="keyword">else</span>
0064 a=load([OutputDirectoryName,<span class="string">'\'</span>,fname_,<span class="string">'_mc'</span>]);
0065 <span class="keyword">end</span>
0066 <span class="keyword">if</span> ~isfield(a,<span class="string">'stock_filter'</span>),
0067 <a href="dynare_MC.html" class="code" title="function dynare_MC(var_list_)">dynare_MC</a>([],OutputDirectoryName);
0068 options_gsa_.load_rmse=0;
0069 <span class="keyword">end</span>
0070 <a href="filt_mc_.html" class="code" title="function [rmse_MC, ixx] = filt_mc_(vvarvecm, loadSA, pfilt, alpha, alpha2, OutDir, istart, alphaPC)">filt_mc_</a>(options_gsa_.var_rmse, options_gsa_.load_rmse, options_gsa_.pfilt_rmse, <span class="keyword">...</span>
0071 options_gsa_.alpha_rmse, options_gsa_.alpha2_rmse, OutputDirectoryName, <span class="keyword">...</span>
0072 options_gsa_.istart_rmse);
0073 <span class="keyword">end</span>
0074
0075
0076 options_gsa_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_gsa_,<span class="string">'glue'</span>,0);
0077 <span class="keyword">if</span> options_gsa_.glue,
0078 dr_ = oo_.dr;
0079 <span class="keyword">if</span> options_gsa_.ppost
0080 load([OutputDirectoryName,<span class="string">'\'</span>,fname_,<span class="string">'_post'</span>]);
0081 DirectoryName = <a href="CheckPath.html" class="code" title="function DirectoryName = CheckPath(type)">CheckPath</a>(<span class="string">'metropolis'</span>);
0082 <span class="keyword">else</span>
0083 <span class="keyword">if</span> options_gsa_.pprior
0084 load([OutputDirectoryName,<span class="string">'\'</span>,fname_,<span class="string">'_prior'</span>]);
0085 <span class="keyword">else</span>
0086 load([OutputDirectoryName,<span class="string">'\'</span>,fname_,<span class="string">'_mc'</span>]);
0087 <span class="keyword">end</span>
0088 <span class="keyword">end</span>
0089 nruns=size(x,1);
0090 gend = options_.nobs;
0091 rawdata = <a href="read_variables.html" class="code" title="function dyn_data_01=read_variables(file_name_01,var_names_01,dyn_data_01,xls_sheet,xls_range)">read_variables</a>(options_.datafile,options_.varobs,[],options_.xls_sheet,options_.xls_range);
0092 rawdata = rawdata(options_.first_obs:options_.first_obs+gend-1,:);
0093 <span class="keyword">if</span> options_.loglinear == 1
0094 rawdata = log(rawdata);
0095 <span class="keyword">end</span>
0096 <span class="keyword">if</span> options_.prefilter == 1
0097 data = transpose(rawdata-ones(gend,1)*bayestopt_.mean_varobs);
0098 <span class="keyword">else</span>
0099 data = transpose(rawdata);
0100 <span class="keyword">end</span>
0101
0102 Obs.data = data;
0103 Obs.time = [1:gend];
0104 Obs.num = gend;
0105 <span class="keyword">for</span> j=1:size(options_.varobs,1)
0106 Obs.name{j} = deblank(options_.varobs(j,:));
0107 vj=deblank(options_.varobs(j,:));
0108
0109 jxj = strmatch(vj,lgy_(dr_.order_var,:),<span class="string">'exact'</span>);
0110 js = strmatch(vj,lgy_,<span class="string">'exact'</span>);
0111 <span class="keyword">if</span> ~options_gsa_.ppost
0112 y0=zeros(gend+1,nruns);
0113 nb = size(stock_filter,3);
0114 y0 = squeeze(stock_filter(:,jxj,:)) + <span class="keyword">...</span>
0115 kron(stock_ys(js,:),ones(size(stock_filter,1),1));
0116 Out(j).data = y0';
0117 Out(j).time = [1:size(y0,1)];
0118 <span class="keyword">else</span>
0119 Out(j).data = jxj;
0120 Out(j).time = [pwd,<span class="string">'\'</span>,DirectoryName];
0121 <span class="keyword">end</span>
0122 Out(j).name = vj;
0123 Out(j).ini = <span class="string">'yes'</span>;
0124 Lik(j).name = [<span class="string">'rmse_'</span>,vj];
0125 Lik(j).ini = <span class="string">'yes'</span>;
0126 Lik(j).isam = 1;
0127 Lik(j).data = rmse_MC(:,j)';
0128
0129 <span class="keyword">if</span> ~options_gsa_.ppost
0130 y0 = squeeze(stock_smooth(:,jxj,:)) + <span class="keyword">...</span>
0131 kron(stock_ys(js,:),ones(size(stock_smooth,1),1));
0132 Out1(j).name = vj;
0133 Out1(j).ini = <span class="string">'yes'</span>;
0134 Out1(j).time = [1:size(y0,1)];
0135 Out1(j).data = y0';
0136 <span class="keyword">else</span>
0137 Out1=Out;
0138 <span class="keyword">end</span>
0139 ismoo(j)=jxj;
0140
0141 <span class="keyword">end</span>
0142 jsmoo = size(options_.varobs,1);
0143 <span class="keyword">for</span> j=1:M_.endo_nbr,
0144 <span class="keyword">if</span> ~ismember(j,ismoo),
0145 jsmoo=jsmoo+1;
0146 vj=deblank(M_.endo_names(dr_.order_var(j),:));
0147 <span class="keyword">if</span> ~options_gsa_.ppost
0148 y0 = squeeze(stock_smooth(:,j,:)) + <span class="keyword">...</span>
0149 kron(stock_ys(j,:),ones(size(stock_smooth,1),1));
0150 Out1(jsmoo).time = [1:size(y0,1)];
0151 Out1(jsmoo).data = y0';
0152 <span class="keyword">else</span>
0153 Out1(jsmoo).data = j;
0154 Out1(jsmoo).time = [pwd,<span class="string">'\'</span>,DirectoryName];
0155 <span class="keyword">end</span>
0156 Out1(jsmoo).name = vj;
0157 Out1(jsmoo).ini = <span class="string">'yes'</span>;
0158 <span class="keyword">end</span>
0159 <span class="keyword">end</span>
0160 tit(M_.exo_names_orig_ord,:) = M_.exo_names;
0161 <span class="keyword">for</span> j=1:M_.exo_nbr,
0162 Exo(j).name = deblank(tit(j,:));
0163 <span class="keyword">end</span>
0164 <span class="keyword">if</span> ~options_gsa_.ppost
0165 Lik(size(options_.varobs,1)+1).name = <span class="string">'logpo'</span>;
0166 Lik(size(options_.varobs,1)+1).ini = <span class="string">'yes'</span>;
0167 Lik(size(options_.varobs,1)+1).isam = 1;
0168 Lik(size(options_.varobs,1)+1).data = -logpo2;
0169 <span class="keyword">end</span>
0170 Sam.name = bayestopt_.name;
0171 Sam.dim = [size(x) 0];
0172 Sam.data = [x];
0173
0174 Rem.id = <span class="string">'Original'</span>;
0175 Rem.ind= [1:size(x,1)];
0176
0177 <span class="keyword">if</span> options_gsa_.ppost
0178 Info.dynare=M_.fname;
0179 Out=Out1;
0180 save([OutputDirectoryName,<span class="string">'\'</span>,fname_,<span class="string">'_post_glue'</span>], <span class="string">'Out'</span>, <span class="string">'Sam'</span>, <span class="string">'Lik'</span>, <span class="string">'Obs'</span>, <span class="string">'Rem'</span>,<span class="string">'Info'</span>, <span class="string">'Exo'</span>)
0181 <span class="comment">%save([fname_,'_post_glue_smooth'], 'Out', 'Sam', 'Lik', 'Obs', 'Rem','Info')</span>
0182
0183 <span class="keyword">else</span>
0184 <span class="keyword">if</span> options_gsa_.pprior
0185 save([OutputDirectoryName,<span class="string">'\'</span>,fname_,<span class="string">'_prior_glue'</span>], <span class="string">'Out'</span>, <span class="string">'Sam'</span>, <span class="string">'Lik'</span>, <span class="string">'Obs'</span>, <span class="string">'Rem'</span>)
0186 Out=Out1;
0187 save([OutputDirectoryName,<span class="string">'\'</span>,fname_,<span class="string">'_prior_glue_smooth'</span>], <span class="string">'Out'</span>, <span class="string">'Sam'</span>, <span class="string">'Lik'</span>, <span class="string">'Obs'</span>, <span class="string">'Rem'</span>)
0188 <span class="keyword">else</span>
0189 save([OutputDirectoryName,<span class="string">'\'</span>,fname_,<span class="string">'_mc_glue'</span>], <span class="string">'Out'</span>, <span class="string">'Sam'</span>, <span class="string">'Lik'</span>, <span class="string">'Obs'</span>, <span class="string">'Rem'</span>)
0190 Out=Out1;
0191 save([OutputDirectoryName,<span class="string">'\'</span>,fname_,<span class="string">'_mc_glue_smooth'</span>], <span class="string">'Out'</span>, <span class="string">'Sam'</span>, <span class="string">'Lik'</span>, <span class="string">'Obs'</span>, <span class="string">'Rem'</span>)
0192 <span class="keyword">end</span>
0193 <span class="keyword">end</span>
0194
0195 <span class="keyword">end</span></pre></div>
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<title>Description of dynare_solve</title>
<meta name="keywords" content="dynare_solve">
<meta name="description" content="Copyright (C) 2001 Michel Juillard">
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<div><a href="../index.html">Home</a> &gt; <a href="index.html">.</a> &gt; dynare_solve.m</div>
<!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png">&nbsp;Master index</a></td>
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<h1>dynare_solve
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>Copyright (C) 2001 Michel Juillard</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function [x,cheik] = dynare_solve(func,x,jacobian_flag,varargin) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> Copyright (C) 2001 Michel Juillard</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a> </li><li><a href="solve1.html" class="code" title="function [x,check] = solve1(func,x,j1,j2,jacobian_flag,varargin)">solve1</a> Copyright (C) 2001 Michel Juillard</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="olr1.html" class="code" title="function dr = olr1(ys,algo,olr_inst,bet,obj_var,W)">olr1</a> Copyright (C) 2001 Michel Juillard</li><li><a href="osr1.html" class="code" title="function oo_.dr=osr1(params,weights)">osr1</a> </li><li><a href="resol.html" class="code" title="function [dr,info]=resol(ys,check_flag)">resol</a> Copyright (C) 2001 Michel Juillard</li><li><a href="resol1.html" class="code" title="function dr=resol1(ys,algo,linear,iorder)">resol1</a> Copyright (C) 2001 Michel Juillard</li><li><a href="simult_.html" class="code" title="function y_=simult_(y0,dr,ex_,iorder)">simult_</a> Copyright (C) 2001 Michel Juillard</li><li><a href="steady_.html" class="code" title="function steady_()">steady_</a> Copyright (C) 2001 Michel Juillard</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <span class="comment">% Copyright (C) 2001 Michel Juillard</span>
0002 <span class="comment">%</span>
0003 <a name="_sub0" href="#_subfunctions" class="code">function [x,cheik] = dynare_solve(func,x,jacobian_flag,varargin)</a>
0004 <span class="keyword">global</span> options_
0005
0006 options_ = <a href="set_default_option.html" class="code" title="function options=set_default_option(options,field,default)">set_default_option</a>(options_,<span class="string">'solve_algo'</span>,2);
0007 cheik = 0;
0008 <span class="keyword">if</span> options_.solve_algo == 0
0009 <span class="keyword">if</span> ~isempty(which(<span class="string">'fsolve'</span>)) &amp; sscanf(version(<span class="string">'-release'</span>),<span class="string">'%d'</span>) &gt;= 13;
0010 options=optimset(<span class="string">'fsolve'</span>);
0011 options.MaxFunEvals = 20000;
0012 options.TolFun=1e-8;
0013 options.Display = <span class="string">'off'</span>;
0014 <span class="keyword">if</span> jacobian_flag
0015 options.Jacobian = <span class="string">'on'</span>;
0016 <span class="keyword">else</span>
0017 options.Jacobian = <span class="string">'on'</span>;
0018 <span class="keyword">end</span>
0019 [x,fval,exitval,output] = fsolve(func,x,options,varargin{:});
0020 <span class="keyword">if</span> exitval &gt; 0
0021 cheik = 0;
0022 <span class="keyword">else</span>
0023 cheik = 1;
0024 <span class="keyword">end</span>
0025 <span class="keyword">return</span>
0026 <span class="keyword">else</span>
0027 options_.solve_algo = 1;
0028 <span class="keyword">end</span>
0029 <span class="keyword">end</span>
0030
0031 <span class="keyword">if</span> options_.solve_algo == 1
0032 nn = size(x,1) ;
0033 [x,cheik]=<a href="solve1.html" class="code" title="function [x,check] = solve1(func,x,j1,j2,jacobian_flag,varargin)">solve1</a>(func,x,1:nn,1:nn,jacobian_flag,varargin{:});
0034 <span class="keyword">elseif</span> options_.solve_algo == 2
0035 nn = size(x,1) ;
0036 tolf = eps^(2/3) ;
0037
0038 <span class="keyword">if</span> jacobian_flag
0039 [fvec,fjac] = feval(func,x,varargin{:});
0040 <span class="keyword">else</span>
0041 fvec = feval(func,x,varargin{:});
0042 fjac = zeros(nn,nn) ;
0043 <span class="keyword">end</span>
0044
0045 i = find(~isfinite(fvec));
0046
0047 <span class="keyword">if</span> ~isempty(i)
0048 disp([<span class="string">'STEADY: numerical initial values incompatible with the following'</span> <span class="keyword">...</span>
0049 <span class="string">' equations'</span>])
0050 disp(i')
0051 error(<span class="string">'exiting ...'</span>)
0052 <span class="keyword">end</span>
0053
0054 f = 0.5*fvec'*fvec ;
0055
0056 <span class="keyword">if</span> max(abs(fvec)) &lt; 0.01*tolf
0057 <span class="keyword">return</span> ;
0058 <span class="keyword">end</span>
0059
0060 <span class="keyword">if</span> ~jacobian_flag
0061 fjac = zeros(nn,nn) ;
0062 dh = max(abs(x),options_.gstep*ones(nn,1))*eps^(1/3);
0063 <span class="keyword">for</span> j = 1:nn
0064 xdh = x ;
0065 xdh(j) = xdh(j)+dh(j) ;
0066 fjac(:,j) = (feval(func,xdh,varargin{:}) - fvec)./dh(j) ;
0067 <span class="keyword">end</span>
0068 <span class="keyword">end</span>
0069
0070 [j1,j2,r,s] = dmperm(fjac);
0071
0072 <span class="keyword">for</span> i=length(r)-1:-1:1
0073 [x,cheik]=<a href="solve1.html" class="code" title="function [x,check] = solve1(func,x,j1,j2,jacobian_flag,varargin)">solve1</a>(func,x,j1(r(i):r(i+1)-1),j2(r(i):r(i+1)-1),jacobian_flag,varargin{:});
0074 <span class="keyword">if</span> cheik
0075 error(sprintf(<span class="string">'Solve block = %d check = %d\n'</span>,i,cheik));
0076 <span class="keyword">end</span>
0077 <span class="keyword">end</span>
0078 [x,cheik]=<a href="solve1.html" class="code" title="function [x,check] = solve1(func,x,j1,j2,jacobian_flag,varargin)">solve1</a>(func,x,1:nn,1:nn,jacobian_flag,varargin{:});
0079
0080 <span class="keyword">end</span>
0081 <span class="comment">% fvec1 = feval(func,x,varargin{:})</span>
0082
0083 <span class="comment">% 08/28/03 MJ add a final call to solve1 for solve_algo == 1 in case</span>
0084 <span class="comment">% initvals generates 'false' zeros in the Jacobian</span>
0085</pre></div>
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<title>Description of dynasave</title>
<meta name="keywords" content="dynasave">
<meta name="description" content="Copyright (C) 2001 Michel Juillard">
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
<meta name="generator" content="m2html &copy; 2003 Guillaume Flandin">
<meta name="robots" content="index, follow">
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<h1>dynasave
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>Copyright (C) 2001 Michel Juillard</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function dynasave(s,var_list) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> Copyright (C) 2001 Michel Juillard
DYNASAVE : DYNASAVE ( [ 'filename' ] )
This optional command saves the simulation results
in a .MAT file.</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <span class="comment">% Copyright (C) 2001 Michel Juillard</span>
0002 <span class="comment">%</span>
0003 <a name="_sub0" href="#_subfunctions" class="code">function dynasave(s,var_list)</a>
0004 <span class="comment">% DYNASAVE : DYNASAVE ( [ 'filename' ] )</span>
0005 <span class="comment">% This optional command saves the simulation results</span>
0006 <span class="comment">% in a .MAT file.</span>
0007
0008 <span class="keyword">global</span> M_ oo_
0009
0010 n = size(var_list,1);
0011 <span class="keyword">if</span> n == 0
0012 n = M_.endo_nbr;
0013 ivar = [1:n]';
0014 var_list = M_.endo_names;
0015 <span class="keyword">else</span>
0016 ivar=zeros(n,1);
0017 <span class="keyword">for</span> i=1:n
0018 i_tmp = strmatch(var_list(i,:),M_.endo_names,<span class="string">'exact'</span>);
0019 <span class="keyword">if</span> isempty(i_tmp)
0020 error ([<span class="string">'One of the specified variables does not exist'</span>]) ;
0021 <span class="keyword">else</span>
0022 ivar(i) = i_tmp;
0023 <span class="keyword">end</span>
0024 <span class="keyword">end</span>
0025 <span class="keyword">end</span>
0026
0027
0028 <span class="comment">% dyn2vec(var_list(1),var_list(1));</span>
0029 eval([var_list(1) <span class="string">'=oo_.y_simul(ivar(1),:)'';'</span>])
0030 eval([<span class="string">'save '</span> s <span class="string">' '</span> var_list(1) <span class="string">' -mat'</span>])
0031 <span class="keyword">for</span> i = 2:n
0032 <span class="comment">% dyn2vec(var_list(i),var_list(i));</span>
0033 eval([var_list(i) <span class="string">'=oo_.y_simul(ivar(i),:)'';'</span>])
0034 eval([<span class="string">'save '</span> s <span class="string">' '</span> var_list(i) <span class="string">' -append -mat'</span>])
0035 <span class="keyword">end</span>
0036
0037</pre></div>
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<head>
<title>Description of dynatype</title>
<meta name="keywords" content="dynatype">
<meta name="description" content="Copyright (C) 2001 Michel Juillard">
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
<meta name="generator" content="m2html &copy; 2003 Guillaume Flandin">
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<h1>dynatype
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>Copyright (C) 2001 Michel Juillard</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function dynatype (s,var_list) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> Copyright (C) 2001 Michel Juillard
DYNATYPE : DYNATYPE ( [ 'filename' ] )
This optional command saves the simulation
results in a text file. The name of each
variable preceeds the corresponding results.
This command must follow SIMUL.</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <span class="comment">% Copyright (C) 2001 Michel Juillard</span>
0002 <span class="comment">%</span>
0003 <a name="_sub0" href="#_subfunctions" class="code">function dynatype (s,var_list)</a>
0004 <span class="comment">% DYNATYPE : DYNATYPE ( [ 'filename' ] )</span>
0005 <span class="comment">% This optional command saves the simulation</span>
0006 <span class="comment">% results in a text file. The name of each</span>
0007 <span class="comment">% variable preceeds the corresponding results.</span>
0008 <span class="comment">% This command must follow SIMUL.</span>
0009
0010 <span class="keyword">global</span> M_ oo_
0011
0012 fid=fopen(s,<span class="string">'w'</span>) ;
0013
0014 n = size(var_list,1);
0015 <span class="keyword">if</span> n == 0
0016 n = M_.endo_nbr;
0017 ivar = [1:n]';
0018 var_list = M_.endo_names;
0019 <span class="keyword">else</span>
0020 ivar=zeros(n,1);
0021 <span class="keyword">for</span> i=1:n
0022 i_tmp = strmatch(var_list(i,:),M_.endo_names,<span class="string">'exact'</span>);
0023 <span class="keyword">if</span> isempty(i_tmp)
0024 error ([<span class="string">'One of the specified variables does not exist'</span>]) ;
0025 <span class="keyword">else</span>
0026 ivar(i) = i_tmp;
0027 <span class="keyword">end</span>
0028 <span class="keyword">end</span>
0029 <span class="keyword">end</span>
0030
0031
0032 <span class="keyword">for</span> i = 1:n
0033 fprintf(fid,M_.endo_names(ivar(i),:),<span class="string">'\n'</span>) ;
0034 fprintf(fid,<span class="string">'\n'</span>) ;
0035 fprintf(fid,<span class="string">'%15.8g\n'</span>,oo_.y_simul(ivar(i),:)') ;
0036 <span class="keyword">end</span>
0037 fclose(fid) ;
0038
0039 <span class="keyword">return</span> ;</pre></div>
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<title>Description of f_var</title>
<meta name="keywords" content="f_var">
<meta name="description" content="">
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
<meta name="generator" content="m2html &copy; 2003 Guillaume Flandin">
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<h1>f_var
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong></strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function b=f_var(x,a,nx) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"></pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="calib_obj.html" class="code" title="function f=calib_obj(M_.Sigma_e,A,ghu1,ghx,ghu,targets,var_weights,iy,nar)">calib_obj</a> targets and iy order: 1) variances 2) correlations</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function b=f_var(x,a,nx)</a>
0002 x=reshape(x,nx,nx);
0003 b=x-a*x*a';
0004 b=b(:);</pre></div>
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<title>Description of fbeta</title>
<meta name="keywords" content="fbeta">
<meta name="description" content="must restrict p2 such that a>0 and b>0 ....">
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<h1>fbeta
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>must restrict p2 such that a>0 and b>0 ....</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function e = fbeta(p2,p,p1,perc) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> must restrict p2 such that a&gt;0 and b&gt;0 ....</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="p2toperc.html" class="code" title="function y=p2toperc(x)">p2toperc</a> </li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function e = fbeta(p2,p,p1,perc)</a>
0002 <span class="comment">% must restrict p2 such that a&gt;0 and b&gt;0 ....</span>
0003 a = (1-p1)*p1^2/p2^2 - p1;
0004 b = a*(1/p1 - 1);
0005
0006 e = p - pbeta(perc,a,b);</pre></div>
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<title>Description of ff1_</title>
<meta name="keywords" content="ff1_">
<meta name="description" content="Copyright (C) 2001 Michel Juillard">
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
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<h1>ff1_
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>Copyright (C) 2001 Michel Juillard</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function y=ff1_(x) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> Copyright (C) 2001 Michel Juillard</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <span class="comment">% Copyright (C) 2001 Michel Juillard</span>
0002 <span class="comment">%</span>
0003 <a name="_sub0" href="#_subfunctions" class="code">function y=ff1_(x)</a>
0004 <span class="keyword">global</span> it_ M_ oo_
0005
0006 n1 = size(x,1) - M_.exo_nbr;
0007 oo_.exo_simul(it_+M_.maximum_lag-M_.maximum_lag,:) = x(n1+1:end)';
0008 fh = str2func([M_.fname <span class="string">'_static'</span>]);
0009 y=feval(fh,x(1:n1),oo_.exo_simul);
0010
0011
0012</pre></div>
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<title>Description of ffill</title>
<meta name="keywords" content="ffill">
<meta name="description" content="Copyright (C) 2001 Michel Juillard">
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<h1>ffill
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>Copyright (C) 2001 Michel Juillard</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function [a,b] = ffill(x,ixc,y) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> Copyright (C) 2001 Michel Juillard</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="simk.html" class="code" title="function simk">simk</a> Copyright (C) 2001 Michel Juillard</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <span class="comment">% Copyright (C) 2001 Michel Juillard</span>
0002 <span class="comment">%</span>
0003 <a name="_sub0" href="#_subfunctions" class="code">function [a,b] = ffill(x,ixc,y)</a>
0004
0005 xc = size(x,1) ;
0006
0007 <span class="keyword">if</span> isempty(y)
0008 b = [ixc; 0];
0009 a = [x zeros(size(x,1),1)];
0010 <span class="keyword">else</span>
0011 yc = size(y,1) ;
0012 b = [ixc;yc] ;
0013
0014 <span class="keyword">if</span> xc &gt; yc
0015 a = [x [y;zeros(xc-yc,size(y,2))]] ;
0016 <span class="keyword">elseif</span> yc &gt; xc
0017 a = [[x;zeros(yc-xc,size(x,2))] y] ;
0018 <span class="keyword">else</span>
0019 a = [x y] ;
0020 <span class="keyword">end</span>
0021
0022 <span class="keyword">end</span>
0023
0024 <span class="comment">% 2001/09/1 MJ corrected for absent lags</span></pre></div>
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<title>Description of fgamma</title>
<meta name="keywords" content="fgamma">
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<h1>fgamma
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong></strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function e = fgamma(p2,p,p1,perc) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"></pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="pgamma.html" class="code" title="function F = pgamma(x,a)">pgamma</a> PGAMMA The gamma distribution function</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="p2toperc.html" class="code" title="function y=p2toperc(x)">p2toperc</a> </li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function e = fgamma(p2,p,p1,perc)</a>
0002 b = p2^2/p1;
0003 a = p1/b;
0004 e = p - <a href="pgamma.html" class="code" title="function F = pgamma(x,a)">pgamma</a>(perc,a,b);</pre></div>
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<title>Description of figamm</title>
<meta name="keywords" content="figamm">
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<h1>figamm
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong></strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function e = figamm(p2,p,p1,perc) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"></pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="pgamma.html" class="code" title="function F = pgamma(x,a)">pgamma</a> PGAMMA The gamma distribution function</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="p2toperc.html" class="code" title="function y=p2toperc(x)">p2toperc</a> </li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function e = figamm(p2,p,p1,perc)</a>
0002 e = p - <a href="pgamma.html" class="code" title="function F = pgamma(x,a)">pgamma</a>(1/perc,p2/2,2/p1);</pre></div>
<hr><address>Generated on Fri 16-Jun-2006 09:09:06 by <strong><a href="http://www.artefact.tk/software/matlab/m2html/">m2html</a></strong> &copy; 2003</address>
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<title>Description of filt_mc_</title>
<meta name="keywords" content="filt_mc_">
<meta name="description" content="copyright Marco Ratto 2006">
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<h1>filt_mc_
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>copyright Marco Ratto 2006</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function [rmse_MC, ixx] = filt_mc_(vvarvecm, loadSA, pfilt, alpha, alpha2, OutDir, istart, alphaPC) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> copyright Marco Ratto 2006</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="CheckPath.html" class="code" title="function DirectoryName = CheckPath(type)">CheckPath</a> 06-03-2005</li><li><a href="DsgeSmoother.html" class="code" title="function [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK] = DsgeSmoother(xparam1,gend,Y)">DsgeSmoother</a> stephane.adjemian@cepremap.cnrs.fr [09-07-2004]</li><li><a href="cumplot.html" class="code" title="function h = cumplot(x);">cumplot</a> function h =cumplot(x)</li><li><a href="kernel_density_estimate.html" class="code" title="function [abscissa,f] = kernel_density_estimate(data,number_of_grid_points,bandwidth,kernel_function)">kernel_density_estimate</a> % This function aims at estimating a continuous density. A kernel density</li><li><a href="mh_optimal_bandwidth.html" class="code" title="function optimal_bandwidth = mh_optimal_bandwidth(data,n,bandwidth,kernel_function)">mh_optimal_bandwidth</a> % This function gives the optimal bandwidth parameter of a kernel estimator</li><li><a href="priordens.html" class="code" title="function lnprior = priordens(para, pshape, p1, p2, p3, p4)">priordens</a> This procedure computes a prior density for</li><li><a href="set_all_parameters.html" class="code" title="function set_all_parameters(xparam1)">set_all_parameters</a> </li><li><a href="smirnov.html" class="code" title="function [H,prob,d] = smirnov(x1 , x2 , alpha, iflag )">smirnov</a> Smirnov test for 2 distributions</li><li><a href="stab_map_2.html" class="code" title="function stab_map_2(x,alpha2,fnam, dirname)">stab_map_2</a> function stab_map_2(x,alpha2,istab,fnam)</li><li><a href="steady_.html" class="code" title="function steady_()">steady_</a> Copyright (C) 2001 Michel Juillard</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="dynare_sensitivity.html" class="code" title="function x0=dynare_sensitivity()">dynare_sensitivity</a> copyright Marco Ratto 2006</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function [rmse_MC, ixx] = filt_mc_(vvarvecm, loadSA, pfilt, alpha, alpha2, OutDir, istart, alphaPC)</a>
0002 <span class="comment">% copyright Marco Ratto 2006</span>
0003 <span class="keyword">global</span> bayestopt_ estim_params_ M_ options_ oo_
0004
0005 <span class="keyword">if</span> nargin&lt;1 | isempty(vvarvecm),
0006 vvarvecm = options_.varobs;
0007 <span class="keyword">end</span>
0008 <span class="keyword">if</span> nargin&lt;2,
0009 loadSA=0;
0010 <span class="keyword">end</span>
0011 <span class="keyword">if</span> nargin&lt;3 | isempty(pfilt),
0012 pfilt=0.1; <span class="comment">% cut the best 10% of runs</span>
0013 <span class="keyword">end</span>
0014 <span class="keyword">if</span> nargin&lt;4 | isempty(alpha),
0015 alpha=0.002;
0016 <span class="keyword">end</span>
0017 <span class="keyword">if</span> nargin&lt;5 | isempty(alpha2),
0018 alpha2=0.5;
0019 <span class="keyword">end</span>
0020 <span class="keyword">if</span> nargin&lt;7 | isempty(istart),
0021 istart=1;
0022 <span class="keyword">end</span>
0023 <span class="keyword">if</span> nargin&lt;8,
0024 alphaPC=0.5;
0025 <span class="keyword">end</span>
0026
0027 fname_ = M_.fname;
0028 lgy_ = M_.endo_names;
0029 dr_ = oo_.dr;
0030
0031 disp(<span class="string">' '</span>)
0032 disp(<span class="string">' '</span>)
0033 disp(<span class="string">'Starting sensitivity analysis'</span>)
0034 disp(<span class="string">'for the fit of EACH observed series ...'</span>)
0035 disp(<span class="string">' '</span>)
0036 disp(<span class="string">'Deleting old SA figures...'</span>)
0037 a=dir([OutDir,<span class="string">'\*.*'</span>]);
0038 <span class="keyword">if</span> options_.opt_gsa.ppost,
0039 tmp=[<span class="string">'_SA_fit_post'</span>];
0040 <span class="keyword">else</span>
0041 <span class="keyword">if</span> options_.opt_gsa.pprior
0042 tmp=[<span class="string">'_SA_fit_prior'</span>];
0043 <span class="keyword">else</span>
0044 tmp=[<span class="string">'_SA_fit_mc'</span>];
0045 <span class="keyword">end</span>
0046 <span class="keyword">end</span>
0047 <span class="keyword">for</span> j=1:length(a),
0048 <span class="keyword">if</span> strmatch([fname_,tmp],a(j).name),
0049 disp(a(j).name)
0050 delete([OutDir,<span class="string">'\'</span>,a(j).name])
0051 <span class="keyword">end</span>,
0052 <span class="keyword">end</span>
0053 disp(<span class="string">'done !'</span>)
0054
0055
0056 nshock=estim_params_.nvx + estim_params_.nvn + estim_params_.ncx + estim_params_.ncn;
0057 npar=estim_params_.np;
0058 <span class="keyword">for</span> j=1:npar+nshock,
0059 <span class="keyword">if</span> j&gt;nshock
0060 <span class="keyword">if</span> isfield(oo_,<span class="string">'posterior_mode'</span>),
0061 xparam1(j)=oo_.posterior_mode.parameters.(bayestopt_.name{j});
0062 <span class="keyword">end</span>
0063 <span class="keyword">if</span> isfield(oo_,<span class="string">'posterior_mean'</span>),
0064 xparam1_mean(j)=oo_.posterior_mean.parameters.(bayestopt_.name{j});
0065 <span class="keyword">end</span>
0066 <span class="keyword">else</span>
0067 <span class="keyword">if</span> isfield(oo_,<span class="string">'posterior_mode'</span>),
0068 xparam1(j)=oo_.posterior_mode.shocks_std.(bayestopt_.name{j});
0069 <span class="keyword">end</span>
0070 <span class="keyword">if</span> isfield(oo_,<span class="string">'posterior_mean'</span>),
0071 xparam1_mean(j)=oo_.posterior_mean.shocks_std.(bayestopt_.name{j});
0072 <span class="keyword">end</span>
0073 <span class="keyword">end</span>
0074 <span class="keyword">end</span>
0075
0076 <span class="keyword">if</span> options_.opt_gsa.ppost,
0077 fnamtmp=[fname_,<span class="string">'_post'</span>];
0078 DirectoryName = <a href="CheckPath.html" class="code" title="function DirectoryName = CheckPath(type)">CheckPath</a>(<span class="string">'metropolis'</span>);
0079 <span class="keyword">else</span>
0080 <span class="keyword">if</span> options_.opt_gsa.pprior
0081 fnamtmp=[fname_,<span class="string">'_prior'</span>];
0082 <span class="keyword">else</span>
0083 fnamtmp=[fname_,<span class="string">'_mc'</span>];
0084 <span class="keyword">end</span>
0085 <span class="keyword">end</span>
0086 <span class="keyword">if</span> ~loadSA,
0087 <span class="keyword">if</span> exist(<span class="string">'xparam1'</span>,<span class="string">'var'</span>)
0088 <a href="set_all_parameters.html" class="code" title="function set_all_parameters(xparam1)">set_all_parameters</a>(xparam1);
0089 <a href="steady_.html" class="code" title="function steady_()">steady_</a>;
0090 ys_mode=oo_.steady_state;
0091 <span class="keyword">end</span>
0092 <span class="keyword">if</span> exist(<span class="string">'xparam1_mean'</span>,<span class="string">'var'</span>)
0093 <a href="set_all_parameters.html" class="code" title="function set_all_parameters(xparam1)">set_all_parameters</a>(xparam1_mean);
0094 <a href="steady_.html" class="code" title="function steady_()">steady_</a>;
0095 ys_mean=oo_.steady_state;
0096 <span class="keyword">end</span>
0097 eval(options_.datafile)
0098 <span class="keyword">if</span> ~options_.opt_gsa.ppost
0099 load([OutDir,<span class="string">'\'</span>,fnamtmp]);
0100 <span class="keyword">else</span>
0101 load([DirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_data.mat'</span>]);
0102 filfilt = dir([DirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_filter*.mat'</span>]);
0103 filparam = dir([DirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_param*.mat'</span>]);
0104 x=[];
0105 logpo2=[];
0106 sto_ys=[];
0107 <span class="keyword">for</span> j=1:length(filparam),
0108 <span class="comment">%load([DirectoryName '/' M_.fname '_param',int2str(j),'.mat']);</span>
0109 <span class="keyword">if</span> isempty(strmatch([M_.fname <span class="string">'_param_irf'</span>],filparam(j).name))
0110 load([DirectoryName <span class="string">'/'</span> filparam(j).name]);
0111 x=[x; stock];
0112 logpo2=[logpo2; stock_logpo];
0113 sto_ys=[sto_ys; stock_ys];
0114 clear stock stock_logpo stock_ys;
0115 <span class="keyword">end</span>
0116 <span class="keyword">end</span>
0117 logpo2=-logpo2;
0118 <span class="keyword">end</span>
0119 nruns=size(x,1);
0120 nfilt=floor(pfilt*nruns);
0121 disp(<span class="string">' '</span>)
0122 disp(<span class="string">'Computing RMSE''s...'</span>)
0123 fobs = options_.first_obs;
0124 nobs=options_.nobs;
0125 <span class="keyword">for</span> i=1:size(vvarvecm,1),
0126 vj=deblank(vvarvecm(i,:));
0127 <span class="keyword">if</span> options_.prefilter == 1
0128 eval([vj,<span class="string">'='</span>,vj,<span class="string">'-bayestopt_.mean_varobs(i);'</span>])
0129 <span class="keyword">end</span>
0130
0131 jxj = strmatch(vj,lgy_(dr_.order_var,:),<span class="string">'exact'</span>);
0132 js = strmatch(vj,lgy_,<span class="string">'exact'</span>);
0133 <span class="keyword">if</span> exist(<span class="string">'xparam1'</span>,<span class="string">'var'</span>)
0134 eval([<span class="string">'rmse_mode(i) = sqrt(mean(('</span>,vj,<span class="string">'(fobs-1+istart:fobs-1+nobs)-oo_.steady_state(js)-oo_.FilteredVariables.'</span>,vj,<span class="string">'(istart:end-1)).^2));'</span>])
0135 <span class="keyword">end</span>
0136 y0=zeros(nobs+1,nruns);
0137 <span class="keyword">if</span> options_.opt_gsa.ppost
0138 nbb=0;
0139 <span class="keyword">for</span> j=1:length(filfilt),
0140 load([DirectoryName <span class="string">'/'</span> M_.fname <span class="string">'_filter'</span>,num2str(j),<span class="string">'.mat'</span>]);
0141 nb = size(stock,4);
0142 y0(:,nbb+1:nbb+nb)=squeeze(stock(1,jxj,:,:)); + <span class="keyword">...</span>
0143 kron(sto_ys(nbb+1:nbb+nb,js)',ones(size(stock,3),1));
0144 <span class="comment">%y0(:,:,size(y0,3):size(y0,3)+size(stock,3))=stock;</span>
0145 nbb=nbb+nb;
0146 clear stock;
0147 <span class="keyword">end</span>
0148 <span class="keyword">else</span>
0149 y0 = squeeze(stock_filter(:,jxj,:)) + <span class="keyword">...</span>
0150 kron(stock_ys(js,:),ones(size(stock_filter,1),1));
0151 <span class="keyword">end</span>
0152 y0M=mean(y0,2);
0153 <span class="keyword">for</span> j=1:nruns,
0154 eval([<span class="string">'rmse_MC(j,i) = sqrt(mean(('</span>,vj,<span class="string">'(fobs-1+istart:fobs-1+nobs)-y0(istart:end-1,j)).^2));'</span>])
0155 <span class="keyword">end</span>
0156 <span class="keyword">if</span> exist(<span class="string">'xparam1_mean'</span>,<span class="string">'var'</span>)
0157 <span class="comment">%eval(['rmse_pmean(i) = sqrt(mean((',vj,'(fobs-1+istart:fobs-1+nobs)-y0M(istart:end-1)).^2));'])</span>
0158 [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK] = <a href="DsgeSmoother.html" class="code" title="function [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK] = DsgeSmoother(xparam1,gend,Y)">DsgeSmoother</a>(xparam1_mean,stock_gend,stock_data);
0159 y0 = ahat(jxj,:)' + <span class="keyword">...</span>
0160 kron(ys_mean(js,:),ones(size(ahat,2),1));
0161 eval([<span class="string">'rmse_pmean(i) = sqrt(mean(('</span>,vj,<span class="string">'(fobs-1+istart:fobs-1+nobs)-y0(istart:end-1)).^2));'</span>])
0162 <span class="keyword">end</span>
0163 <span class="keyword">end</span>
0164 clear stock_filter;
0165 <span class="keyword">for</span> j=1:nruns,
0166 lnprior(j,1) = <a href="priordens.html" class="code" title="function lnprior = priordens(para, pshape, p1, p2, p3, p4)">priordens</a>(x(j,:),bayestopt_.pshape,bayestopt_.p1,bayestopt_.p2,bayestopt_.p3,bayestopt_.p4);
0167 <span class="keyword">end</span>
0168 likelihood=logpo2(:)+lnprior(:);
0169 disp(<span class="string">'... done!'</span>)
0170
0171 <span class="keyword">if</span> options_.opt_gsa.ppost
0172 save([OutDir,<span class="string">'\'</span>,fnamtmp], <span class="string">'x'</span>, <span class="string">'logpo2'</span>, <span class="string">'likelihood'</span>, <span class="string">'rmse_MC'</span>, <span class="string">'rmse_mode'</span>,<span class="string">'rmse_pmean'</span>)
0173 <span class="keyword">else</span>
0174 save([OutDir,<span class="string">'\'</span>,fnamtmp], <span class="string">'likelihood'</span>, <span class="string">'rmse_MC'</span>, <span class="string">'rmse_mode'</span>,<span class="string">'rmse_pmean'</span>,<span class="string">'-append'</span>)
0175 <span class="keyword">end</span>
0176 <span class="keyword">else</span>
0177 load([OutDir,<span class="string">'\'</span>,fnamtmp],<span class="string">'x'</span>,<span class="string">'logpo2'</span>,<span class="string">'likelihood'</span>,<span class="string">'rmse_MC'</span>,<span class="string">'rmse_mode'</span>,<span class="string">'rmse_pmean'</span>);
0178 lnprior=likelihood(:)-logpo2(:);
0179 nruns=size(x,1);
0180 nfilt=floor(pfilt*nruns);
0181 <span class="keyword">end</span>
0182 <span class="comment">% smirnov tests</span>
0183 nfilt0=nfilt*ones(size(vvarvecm,1),1);
0184 logpo2=logpo2(:);
0185 <span class="keyword">if</span> ~options_.opt_gsa.ppost
0186 [dum, ipost]=sort(logpo2);
0187 <span class="keyword">end</span>
0188 <span class="keyword">for</span> i=1:size(vvarvecm,1),
0189 [dum, ixx(:,i)]=sort(rmse_MC(:,i));
0190 <span class="keyword">if</span> options_.opt_gsa.ppost,
0191 <span class="comment">%nfilt0(i)=length(find(rmse_MC(:,i)&lt;rmse_pmean(i)));</span>
0192 rmse_txt=rmse_pmean;
0193 <span class="keyword">else</span>
0194 <span class="keyword">if</span> options_.opt_gsa.pprior,
0195 rmse_txt=rmse_mode;
0196 <span class="keyword">else</span>
0197 <span class="comment">%nfilt0(i)=length(find(rmse_MC(:,i)&lt;rmse_pmean(i)));</span>
0198 rmse_txt=rmse_pmean;
0199 <span class="keyword">end</span>
0200 <span class="keyword">end</span>
0201 <span class="keyword">for</span> j=1:npar+nshock,
0202 [H,P,KSSTAT] = <a href="smirnov.html" class="code" title="function [H,prob,d] = smirnov(x1 , x2 , alpha, iflag )">smirnov</a>(x(ixx(nfilt0(i)+1:<span class="keyword">end</span>,i),j),x(ixx(1:nfilt0(i),i),j), alpha);
0203 [H1,P1,KSSTAT1] = <a href="smirnov.html" class="code" title="function [H,prob,d] = smirnov(x1 , x2 , alpha, iflag )">smirnov</a>(x(ixx(nfilt0(i)+1:<span class="keyword">end</span>,i),j),x(ixx(1:nfilt0(i),i),j),alpha,1);
0204 [H2,P2,KSSTAT2] = <a href="smirnov.html" class="code" title="function [H,prob,d] = smirnov(x1 , x2 , alpha, iflag )">smirnov</a>(x(ixx(nfilt0(i)+1:<span class="keyword">end</span>,i),j),x(ixx(1:nfilt0(i),i),j),alpha,-1);
0205 <span class="keyword">if</span> H1 &amp; H2==0,
0206 SS(j,i)=1;
0207 <span class="keyword">elseif</span> H1==0,
0208 SS(j,i)=-1;
0209 <span class="keyword">else</span>
0210 SS(j,i)=0;
0211 <span class="keyword">end</span>
0212 PP(j,i)=P;
0213 <span class="keyword">end</span>
0214 <span class="keyword">end</span>
0215 figure(<span class="string">'name'</span>,<span class="string">'prior'</span>)
0216 <span class="keyword">for</span> i=1:size(vvarvecm,1),
0217 subplot(3,3,i)
0218 h=<a href="cumplot.html" class="code" title="function h = cumplot(x);">cumplot</a>(lnprior(ixx(1:nfilt0(i),i)));
0219 set(h,<span class="string">'color'</span>,<span class="string">'red'</span>)
0220 hold on, <a href="cumplot.html" class="code" title="function h = cumplot(x);">cumplot</a>(lnprior)
0221 h=<a href="cumplot.html" class="code" title="function h = cumplot(x);">cumplot</a>(lnprior(ixx(nfilt0(i)+1:<span class="keyword">end</span>,i)));
0222 set(h,<span class="string">'color'</span>,<span class="string">'green'</span>)
0223 title(vvarvecm(i,:))
0224 <span class="keyword">end</span>
0225 <span class="keyword">if</span> options_.opt_gsa.ppost
0226 saveas(gcf,[OutDir,<span class="string">'\'</span>,fname_,<span class="string">'_SA_fit_post_lnprior'</span>])
0227 eval([<span class="string">'print -depsc2 '</span> OutDir <span class="string">'\'</span> fname_ <span class="string">'_SA_fit_post_lnprior'</span>]);
0228 eval([<span class="string">'print -dpdf '</span> OutDir <span class="string">'\'</span> fname_ <span class="string">'_SA_fit_post_lnprior'</span>]);
0229 <span class="keyword">else</span>
0230 <span class="keyword">if</span> options_.opt_gsa.pprior
0231 saveas(gcf,[OutDir,<span class="string">'\'</span>,fname_,<span class="string">'_SA_fit_prior_lnprior'</span>])
0232 eval([<span class="string">'print -depsc2 '</span> OutDir <span class="string">'\'</span> fname_ <span class="string">'_SA_fit_prior_lnprior'</span>]);
0233 eval([<span class="string">'print -dpdf '</span> OutDir <span class="string">'\'</span> fname_ <span class="string">'_SA_fit_prior_lnprior'</span>]);
0234 <span class="keyword">else</span>
0235 saveas(gcf,[OutDir,<span class="string">'\'</span>,fname_,<span class="string">'_SA_fit_mc_lnprior'</span>])
0236 eval([<span class="string">'print -depsc2 '</span> OutDir <span class="string">'\'</span> fname_ <span class="string">'_SA_fit_mc_lnprior'</span>]);
0237 eval([<span class="string">'print -dpdf '</span> OutDir <span class="string">'\'</span> fname_ <span class="string">'_SA_fit_mc_lnprior'</span>]);
0238 <span class="keyword">end</span>
0239 <span class="keyword">end</span>
0240 close(gcf)
0241 figure(<span class="string">'name'</span>,<span class="string">'likelihood'</span>)
0242 <span class="keyword">for</span> i=1:size(vvarvecm,1),
0243 subplot(3,3,i)
0244 h=<a href="cumplot.html" class="code" title="function h = cumplot(x);">cumplot</a>(likelihood(ixx(1:nfilt0(i),i)));
0245 set(h,<span class="string">'color'</span>,<span class="string">'red'</span>)
0246 hold on, h=<a href="cumplot.html" class="code" title="function h = cumplot(x);">cumplot</a>(likelihood);
0247 h=<a href="cumplot.html" class="code" title="function h = cumplot(x);">cumplot</a>(likelihood(ixx(nfilt0(i)+1:<span class="keyword">end</span>,i)));
0248 set(h,<span class="string">'color'</span>,<span class="string">'green'</span>)
0249 title(vvarvecm(i,:))
0250 <span class="keyword">end</span>
0251 <span class="keyword">if</span> options_.opt_gsa.ppost
0252 saveas(gcf,[OutDir,<span class="string">'\'</span>,fname_,<span class="string">'_SA_fit_post_lnlik'</span>])
0253 eval([<span class="string">'print -depsc2 '</span> OutDir <span class="string">'\'</span> fname_ <span class="string">'_SA_fit_post_lnlik'</span>]);
0254 eval([<span class="string">'print -dpdf '</span> OutDir <span class="string">'\'</span> fname_ <span class="string">'_SA_fit_post_lnlik'</span>]);
0255 <span class="keyword">else</span>
0256 <span class="keyword">if</span> options_.opt_gsa.pprior
0257 saveas(gcf,[OutDir,<span class="string">'\'</span>,fname_,<span class="string">'_SA_fit_prior_lnlik'</span>])
0258 eval([<span class="string">'print -depsc2 '</span> OutDir <span class="string">'\'</span> fname_ <span class="string">'_SA_fit_prior_lnlik'</span>]);
0259 eval([<span class="string">'print -dpdf '</span> OutDir <span class="string">'\'</span> fname_ <span class="string">'_SA_fit_prior_lnlik'</span>]);
0260 <span class="keyword">else</span>
0261 saveas(gcf,[OutDir,<span class="string">'\'</span>,fname_,<span class="string">'_SA_fit_mc_lnlik'</span>])
0262 eval([<span class="string">'print -depsc2 '</span> OutDir <span class="string">'\'</span> fname_ <span class="string">'_SA_fit_mc_lnlik'</span>]);
0263 eval([<span class="string">'print -dpdf '</span> OutDir <span class="string">'\'</span> fname_ <span class="string">'_SA_fit_mc_lnlik'</span>]);
0264 <span class="keyword">end</span>
0265 <span class="keyword">end</span>
0266 close(gcf)
0267 figure(<span class="string">'name'</span>,<span class="string">'posterior'</span>)
0268 <span class="keyword">for</span> i=1:size(vvarvecm,1),
0269 subplot(3,3,i)
0270 h=<a href="cumplot.html" class="code" title="function h = cumplot(x);">cumplot</a>(logpo2(ixx(1:nfilt0(i),i)));
0271 set(h,<span class="string">'color'</span>,<span class="string">'red'</span>)
0272 hold on, h=<a href="cumplot.html" class="code" title="function h = cumplot(x);">cumplot</a>(logpo2);
0273 h=<a href="cumplot.html" class="code" title="function h = cumplot(x);">cumplot</a>(logpo2(ixx(nfilt0(i)+1:<span class="keyword">end</span>,i)));
0274 set(h,<span class="string">'color'</span>,<span class="string">'green'</span>)
0275 title(vvarvecm(i,:))
0276 <span class="keyword">end</span>
0277 <span class="keyword">if</span> options_.opt_gsa.ppost
0278 saveas(gcf,[OutDir,<span class="string">'\'</span>,fname_,<span class="string">'_SA_fit_post_lnpost'</span>])
0279 eval([<span class="string">'print -depsc2 '</span> OutDir <span class="string">'\'</span> fname_ <span class="string">'_SA_fit_post_lnpost'</span>]);
0280 eval([<span class="string">'print -dpdf '</span> OutDir <span class="string">'\'</span> fname_ <span class="string">'_SA_fit_post_lnpost'</span>]);
0281 <span class="keyword">else</span>
0282 <span class="keyword">if</span> options_.opt_gsa.pprior
0283 saveas(gcf,[OutDir,<span class="string">'\'</span>,fname_,<span class="string">'_SA_fit_prior_lnpost'</span>])
0284 eval([<span class="string">'print -depsc2 '</span> OutDir <span class="string">'\'</span> fname_ <span class="string">'_SA_fit_prior_lnpost'</span>]);
0285 eval([<span class="string">'print -dpdf '</span> OutDir <span class="string">'\'</span> fname_ <span class="string">'_SA_fit_prior_lnpost'</span>]);
0286 <span class="keyword">else</span>
0287 saveas(gcf,[OutDir,<span class="string">'\'</span>,fname_,<span class="string">'_SA_fit_mc_lnpost'</span>])
0288 eval([<span class="string">'print -depsc2 '</span> OutDir <span class="string">'\'</span> fname_ <span class="string">'_SA_fit_mc_lnpost'</span>]);
0289 eval([<span class="string">'print -dpdf '</span> OutDir <span class="string">'\'</span> fname_ <span class="string">'_SA_fit_mc_lnpost'</span>]);
0290 <span class="keyword">end</span>
0291 <span class="keyword">end</span>
0292 close(gcf)
0293
0294 param_names=<span class="string">''</span>;
0295 <span class="keyword">for</span> j=1:npar+nshock,
0296 param_names=str2mat(param_names, bayestopt_.name{j});
0297 <span class="keyword">end</span>
0298 param_names=param_names(2:<span class="keyword">end</span>,:);
0299
0300 disp(<span class="string">' '</span>)
0301 disp(<span class="string">'RMSE over the MC sample:'</span>)
0302 disp(<span class="string">' min yr RMSE max yr RMSE'</span>)
0303 <span class="keyword">for</span> j=1:size(vvarvecm,1),
0304 disp([vvarvecm(j,:), sprintf(<span class="string">'%15.5g'</span>,[(min(rmse_MC(:,j))) [(max(rmse_MC(:,j)))]])])
0305 <span class="keyword">end</span>
0306 invar = find( std(rmse_MC)./mean(rmse_MC)&lt;=0.0001 );
0307 <span class="keyword">if</span> ~isempty(invar)
0308 disp(<span class="string">' '</span>)
0309 disp(<span class="string">' '</span>)
0310 disp(<span class="string">'RMSE is not varying significantly over the MC sample for the following variables:'</span>)
0311 disp(vvarvecm(invar,:))
0312 disp(<span class="string">'These variables are excluded from SA'</span>)
0313 disp(<span class="string">'[Unless you treat these series as exogenous, there is something wrong in your estimation !]'</span>)
0314 <span class="keyword">end</span>
0315 ivar = find( std(rmse_MC)./mean(rmse_MC)&gt;0.0001 );
0316 vvarvecm=vvarvecm(ivar,:);
0317 rmse_MC=rmse_MC(:,ivar);
0318
0319 disp(<span class="string">' '</span>)
0320 <span class="keyword">if</span> options_.opt_gsa.ppost==0 &amp; options_.opt_gsa.pprior,
0321 disp([<span class="string">'Sample filtered the '</span>,num2str(pfilt*100),<span class="string">'% best RMSE''s for each observed series ...'</span> ])
0322 <span class="keyword">else</span>
0323 disp([<span class="string">'Sample filtered the best RMSE''s smaller than RMSE at the posterior mean ...'</span> ])
0324 <span class="keyword">end</span>
0325 <span class="comment">% figure, boxplot(rmse_MC)</span>
0326 <span class="comment">% set(gca,'xticklabel',vvarvecm)</span>
0327 <span class="comment">% saveas(gcf,[fname_,'_SA_RMSE'])</span>
0328
0329 disp(<span class="string">' '</span>)
0330 disp(<span class="string">' '</span>)
0331 disp(<span class="string">'RMSE ranges after filtering:'</span>)
0332 <span class="keyword">if</span> options_.opt_gsa.ppost==0 &amp; options_.opt_gsa.pprior,
0333 disp([<span class="string">' best '</span>,num2str(pfilt*100),<span class="string">'% filtered remaining 90%'</span>])
0334 disp([<span class="string">' min max min max posterior mode'</span>])
0335 <span class="keyword">else</span>
0336 disp([<span class="string">' best filtered remaining '</span>])
0337 disp([<span class="string">' min max min max posterior mean'</span>])
0338 <span class="keyword">end</span>
0339 <span class="keyword">for</span> j=1:size(vvarvecm,1),
0340 disp([vvarvecm(j,:), sprintf(<span class="string">'%15.5g'</span>,[min(rmse_MC(ixx(1:nfilt0(j),j),j)) <span class="keyword">...</span>
0341 max(rmse_MC(ixx(1:nfilt0(j),j),j)) <span class="keyword">...</span>
0342 min(rmse_MC(ixx(nfilt0(j)+1:<span class="keyword">end</span>,j),j)) <span class="keyword">...</span>
0343 max(rmse_MC(ixx(nfilt0(j)+1:<span class="keyword">end</span>,j),j)) <span class="keyword">...</span>
0344 rmse_txt(j)])])
0345 <span class="comment">% disp([vvarvecm(j,:), sprintf('%15.5g',[min(logpo2(ixx(1:nfilt,j))) ...</span>
0346 <span class="comment">% max(logpo2(ixx(1:nfilt,j))) ...</span>
0347 <span class="comment">% min(logpo2(ixx(nfilt+1:end,j))) ...</span>
0348 <span class="comment">% max(logpo2(ixx(nfilt+1:end,j)))])])</span>
0349 <span class="keyword">end</span>
0350
0351 <span class="comment">%stab_map_1(x, ipost(1:nfilt), ipost(nfilt+1:end), 'SA_post', 1);</span>
0352 <span class="comment">%stab_map_2(x(ipost(1:nfilt),:),alpha2,'SA_post', 1);</span>
0353 <span class="comment">% for i=1:size(vvarvecm,1),</span>
0354 <span class="comment">% aname=['SA_fit_ALL_',deblank(vvarvecm(i,:))];</span>
0355 <span class="comment">% stab_map_1(x, ixx(1:nfilt,i), ixx(nfilt+1:end,i), aname, 1);</span>
0356 <span class="comment">% close all</span>
0357 <span class="comment">% end</span>
0358
0359 SP=zeros(npar+nshock,size(vvarvecm,1));
0360 <span class="keyword">for</span> j=1:size(vvarvecm,1),
0361 ns=find(PP(:,j)&lt;alpha);
0362 SP(ns,j)=ones(size(ns));
0363 SS(:,j)=SS(:,j).*SP(:,j);
0364 <span class="keyword">end</span>
0365
0366 <span class="keyword">for</span> j=1:npar+nshock, <span class="comment">%estim_params_.np,</span>
0367 nsp(j)=length(find(SP(j,:)));
0368 <span class="keyword">end</span>
0369 snam0=param_names(find(nsp==0),:);
0370 snam1=param_names(find(nsp==1),:);
0371 snam2=param_names(find(nsp&gt;1),:);
0372 snam=param_names(find(nsp&gt;0),:);
0373 <span class="comment">% snam0=bayestopt_.name(find(nsp==0));</span>
0374 <span class="comment">% snam1=bayestopt_.name(find(nsp==1));</span>
0375 <span class="comment">% snam2=bayestopt_.name(find(nsp&gt;1));</span>
0376 <span class="comment">% snam=bayestopt_.name(find(nsp&gt;0));</span>
0377 nsnam=(find(nsp&gt;1));
0378
0379 disp(<span class="string">' '</span>)
0380 disp(<span class="string">' '</span>)
0381 disp(<span class="string">'These parameters do not affect significantly the fit of ANY observed series:'</span>)
0382 disp(snam0)
0383 disp(<span class="string">' '</span>)
0384 disp(<span class="string">'These parameters affect ONE single observed series:'</span>)
0385 disp(snam1)
0386 disp(<span class="string">' '</span>)
0387 disp(<span class="string">'These parameters affect MORE THAN ONE observed series: trade off exists!'</span>)
0388 disp(snam2)
0389
0390
0391 <span class="comment">%pnam=bayestopt_.name(end-estim_params_.np+1:end);</span>
0392 pnam=bayestopt_.name;
0393
0394 <span class="comment">% plot trade-offs</span>
0395 a00=jet(size(vvarvecm,1));
0396 <span class="keyword">for</span> ix=1:ceil(length(nsnam)/6),
0397 figure,
0398 <span class="keyword">for</span> j=1+6*(ix-1):min(size(snam2,1),6*ix),
0399 subplot(2,3,j-6*(ix-1))
0400 <span class="comment">%h0=cumplot(x(:,nsnam(j)+nshock));</span>
0401 h0=<a href="cumplot.html" class="code" title="function h = cumplot(x);">cumplot</a>(x(:,nsnam(j)));
0402 <span class="comment">%set(h0,'color',[1 1 1])</span>
0403 hold on,
0404 np=find(SP(nsnam(j),:));
0405 <span class="comment">%a0=jet(nsp(nsnam(j)));</span>
0406 a0=a00(np,:);
0407 <span class="keyword">for</span> i=1:nsp(nsnam(j)), <span class="comment">%size(vvarvecm,1),</span>
0408 <span class="comment">%h0=cumplot(x(ixx(1:nfilt,np(i)),nsnam(j)+nshock));</span>
0409 h0=<a href="cumplot.html" class="code" title="function h = cumplot(x);">cumplot</a>(x(ixx(1:nfilt0(np(i)),np(i)),nsnam(j)));
0410 set(h0,<span class="string">'color'</span>,a0(i,:))
0411 <span class="keyword">end</span>
0412 ydum=get(gca,<span class="string">'ylim'</span>);
0413 <span class="comment">%xdum=xparam1(nshock+nsnam(j));</span>
0414 xdum=xparam1(nsnam(j));
0415 h1=plot([xdum xdum],ydum);
0416 set(h1,<span class="string">'color'</span>,[0.85 0.85 0.85],<span class="string">'linewidth'</span>,2)
0417 h0=legend(str2mat(<span class="string">'base'</span>,vvarvecm(np,:)),0);
0418 set(h0,<span class="string">'fontsize'</span>,6)
0419 <span class="comment">%h0=legend({'base',vnam{np}}',0);</span>
0420 xlabel(<span class="string">''</span>)
0421 set(findobj(get(h0,<span class="string">'children'</span>),<span class="string">'type'</span>,<span class="string">'text'</span>),<span class="string">'interpreter'</span>,<span class="string">'none'</span>)
0422 title([pnam{nsnam(j)}],<span class="string">'interpreter'</span>,<span class="string">'none'</span>)
0423 <span class="keyword">if</span> options_.opt_gsa.ppost
0424 saveas(gcf,[OutDir,<span class="string">'\'</span>,fname_,<span class="string">'_SA_fit_post_'</span>,num2str(ix)])
0425 eval([<span class="string">'print -depsc2 '</span> OutDir <span class="string">'\'</span> fname_ <span class="string">'_SA_fit_post_'</span> int2str(ix)]);
0426 eval([<span class="string">'print -dpdf '</span> OutDir <span class="string">'\'</span> fname_ <span class="string">'_SA_fit_post_'</span> int2str(ix)]);
0427 <span class="keyword">else</span>
0428 <span class="keyword">if</span> options_.opt_gsa.pprior
0429 saveas(gcf,[OutDir,<span class="string">'\'</span>,fname_,<span class="string">'_SA_fit_prior_'</span>,num2str(ix)])
0430 eval([<span class="string">'print -depsc2 '</span> OutDir <span class="string">'\'</span> fname_ <span class="string">'_SA_fit_prior_'</span> int2str(ix)]);
0431 eval([<span class="string">'print -dpdf '</span> OutDir <span class="string">'\'</span> fname_ <span class="string">'_SA_fit_prior_'</span> int2str(ix)]);
0432 <span class="keyword">else</span>
0433 saveas(gcf,[OutDir,<span class="string">'\'</span>,fname_,<span class="string">'_SA_fit_mc_'</span>,num2str(ix)])
0434 eval([<span class="string">'print -depsc2 '</span> OutDir <span class="string">'\'</span> fname_ <span class="string">'_SA_fit_mc_'</span> int2str(ix)]);
0435 eval([<span class="string">'print -dpdf '</span> OutDir <span class="string">'\'</span> fname_ <span class="string">'_SA_fit_mc_'</span> int2str(ix)]);
0436 <span class="keyword">end</span>
0437 <span class="keyword">end</span>
0438 <span class="keyword">end</span>
0439 <span class="keyword">end</span>
0440 close all
0441
0442 <span class="keyword">for</span> j=1:size(SP,2),
0443 nsx(j)=length(find(SP(:,j)));
0444 <span class="keyword">end</span>
0445
0446 number_of_grid_points = 2^9; <span class="comment">% 2^9 = 512 !... Must be a power of two.</span>
0447 bandwidth = 0; <span class="comment">% Rule of thumb optimal bandwidth parameter.</span>
0448 kernel_function = <span class="string">'gaussian'</span>; <span class="comment">% Gaussian kernel for Fast Fourrier Transform approximaton.</span>
0449 <span class="comment">%kernel_function = 'uniform'; % Gaussian kernel for Fast Fourrier Transform approximaton.</span>
0450
0451 <span class="keyword">for</span> ix=1:ceil(length(nsnam)/6),
0452 figure,
0453 <span class="keyword">for</span> j=1+6*(ix-1):min(size(snam2,1),6*ix),
0454 subplot(2,3,j-6*(ix-1))
0455 optimal_bandwidth = <a href="mh_optimal_bandwidth.html" class="code" title="function optimal_bandwidth = mh_optimal_bandwidth(data,n,bandwidth,kernel_function)">mh_optimal_bandwidth</a>(x(:,nsnam(j)),size(x,1),bandwidth,kernel_function);
0456 [x1,f1] = <a href="kernel_density_estimate.html" class="code" title="function [abscissa,f] = kernel_density_estimate(data,number_of_grid_points,bandwidth,kernel_function)">kernel_density_estimate</a>(x(:,nsnam(j)),number_of_grid_points,<span class="keyword">...</span>
0457 optimal_bandwidth,kernel_function);
0458 h0 = plot(x1, f1);
0459 hold on,
0460 np=find(SP(nsnam(j),:));
0461 <span class="comment">%a0=jet(nsp(nsnam(j)));</span>
0462 a0=a00(np,:);
0463 <span class="keyword">for</span> i=1:nsp(nsnam(j)), <span class="comment">%size(vvarvecm,1),</span>
0464 optimal_bandwidth = <a href="mh_optimal_bandwidth.html" class="code" title="function optimal_bandwidth = mh_optimal_bandwidth(data,n,bandwidth,kernel_function)">mh_optimal_bandwidth</a>(x(ixx(1:nfilt0(np(i)),np(i)),nsnam(j)),nfilt,bandwidth,kernel_function);
0465 [x1,f1] = <a href="kernel_density_estimate.html" class="code" title="function [abscissa,f] = kernel_density_estimate(data,number_of_grid_points,bandwidth,kernel_function)">kernel_density_estimate</a>(x(ixx(1:nfilt0(np(i)),np(i)),nsnam(j)),number_of_grid_points,<span class="keyword">...</span>
0466 optimal_bandwidth,kernel_function);
0467 h0 = plot(x1, f1);
0468 set(h0,<span class="string">'color'</span>,a0(i,:))
0469 <span class="keyword">end</span>
0470 ydum=get(gca,<span class="string">'ylim'</span>);
0471 set(gca,<span class="string">'ylim'</span>,[0 ydum(2)]);
0472 <span class="comment">%xdum=xparam1(nshock+nsnam(j));</span>
0473 xdum=xparam1(nsnam(j));
0474 h1=plot([xdum xdum],[0 ydum(2)]);
0475 set(h1,<span class="string">'color'</span>,[0.85 0.85 0.85],<span class="string">'linewidth'</span>,2)
0476 h0=legend(str2mat(<span class="string">'base'</span>,vvarvecm(np,:)),0);
0477 set(h0,<span class="string">'fontsize'</span>,6)
0478 <span class="comment">%h0=legend({'base',vnam{np}}',0);</span>
0479 xlabel(<span class="string">''</span>)
0480 set(findobj(get(h0,<span class="string">'children'</span>),<span class="string">'type'</span>,<span class="string">'text'</span>),<span class="string">'interpreter'</span>,<span class="string">'none'</span>)
0481 title([pnam{nsnam(j)}],<span class="string">'interpreter'</span>,<span class="string">'none'</span>)
0482 <span class="keyword">if</span> options_.opt_gsa.ppost
0483 saveas(gcf,[OutDir,<span class="string">'\'</span>,fname_,<span class="string">'_SA_fit_post_dens_'</span>,num2str(ix)])
0484 eval([<span class="string">'print -depsc2 '</span> OutDir <span class="string">'\'</span> fname_ <span class="string">'_SA_fit_post_dens_'</span> int2str(ix)]);
0485 eval([<span class="string">'print -dpdf '</span> OutDir <span class="string">'\'</span> fname_ <span class="string">'_SA_fit_post_dens_'</span> int2str(ix)]);
0486 <span class="keyword">else</span>
0487 <span class="keyword">if</span> options_.opt_gsa.pprior
0488 saveas(gcf,[OutDir,<span class="string">'\'</span>,fname_,<span class="string">'_SA_fit_prior_dens_'</span>,num2str(ix)])
0489 eval([<span class="string">'print -depsc2 '</span> OutDir <span class="string">'\'</span> fname_ <span class="string">'_SA_fit_prior_dens_'</span> int2str(ix)]);
0490 eval([<span class="string">'print -dpdf '</span> OutDir <span class="string">'\'</span> fname_ <span class="string">'_SA_fit_prior_dens_'</span> int2str(ix)]);
0491 <span class="keyword">else</span>
0492 saveas(gcf,[OutDir,<span class="string">'\'</span>,fname_,<span class="string">'_SA_fit_mc_dens_'</span>,num2str(ix)])
0493 eval([<span class="string">'print -depsc2 '</span> OutDir <span class="string">'\'</span> fname_ <span class="string">'_SA_fit_mc_dens_'</span> int2str(ix)]);
0494 eval([<span class="string">'print -dpdf '</span> OutDir <span class="string">'\'</span> fname_ <span class="string">'_SA_fit_mc_dens_'</span> int2str(ix)]);
0495 <span class="keyword">end</span>
0496 <span class="keyword">end</span>
0497 <span class="keyword">end</span>
0498 <span class="keyword">end</span>
0499 close all
0500
0501 <span class="comment">% for j=1:size(SP,2),</span>
0502 <span class="comment">% nfig=0;</span>
0503 <span class="comment">% np=find(SP(:,j));</span>
0504 <span class="comment">% for i=1:nsx(j), %size(vvarvecm,1),</span>
0505 <span class="comment">% if mod(i,12)==1,</span>
0506 <span class="comment">% nfig=nfig+1;</span>
0507 <span class="comment">% %figure('name',['Sensitivity of fit of ',vnam{j}]),</span>
0508 <span class="comment">% figure('name',['Sensitivity of fit of ',deblank(vvarvecm(j,:)),' ',num2str(nfig)]),</span>
0509 <span class="comment">% end</span>
0510 <span class="comment">%</span>
0511 <span class="comment">% subplot(3,4,i-12*(nfig-1))</span>
0512 <span class="comment">% optimal_bandwidth = mh_optimal_bandwidth(x(ixx(1:nfilt,j),np(i)),nfilt,bandwidth,kernel_function);</span>
0513 <span class="comment">% [x1,f1] = kernel_density_estimate(x(ixx(1:nfilt,j),np(i)),number_of_grid_points,...</span>
0514 <span class="comment">% optimal_bandwidth,kernel_function);</span>
0515 <span class="comment">% plot(x1, f1,':k','linewidth',2)</span>
0516 <span class="comment">% optimal_bandwidth = mh_optimal_bandwidth(x(ixx(nfilt+1:end,j),np(i)),nruns-nfilt,bandwidth,kernel_function);</span>
0517 <span class="comment">% [x1,f1] = kernel_density_estimate(x(ixx(nfilt+1:end,j),np(i)),number_of_grid_points,...</span>
0518 <span class="comment">% optimal_bandwidth,kernel_function);</span>
0519 <span class="comment">% hold on, plot(x1, f1,'k','linewidth',2)</span>
0520 <span class="comment">% ydum=get(gca,'ylim');</span>
0521 <span class="comment">% %xdum=xparam1(nshock+np(i));</span>
0522 <span class="comment">% xdum=xparam1(np(i));</span>
0523 <span class="comment">% h1=plot([xdum xdum],ydum);</span>
0524 <span class="comment">% set(h1,'color',[0.85 0.85 0.85],'linewidth',2)</span>
0525 <span class="comment">% %xdum1=mean(x(ixx(1:nfilt,j),np(i)+nshock));</span>
0526 <span class="comment">% xdum1=mean(x(ixx(1:nfilt,j),np(i)));</span>
0527 <span class="comment">% h2=plot([xdum1 xdum1],ydum);</span>
0528 <span class="comment">% set(h2,'color',[0 1 0],'linewidth',2)</span>
0529 <span class="comment">% % h0=cumplot(x(nfilt+1:end,np(i)+nshock));</span>
0530 <span class="comment">% % set(h0,'color',[1 1 1])</span>
0531 <span class="comment">% % hold on,</span>
0532 <span class="comment">% % h0=cumplot(x(ixx(1:nfilt,j),np(i)+nshock));</span>
0533 <span class="comment">% % set(h0,'linestyle',':','color',[1 1 1])</span>
0534 <span class="comment">% %title([pnam{np(i)}])</span>
0535 <span class="comment">% title([pnam{np(i)},'. K-S prob ', num2str(PP(np(i),j))],'interpreter','none')</span>
0536 <span class="comment">% xlabel('')</span>
0537 <span class="comment">% if mod(i,12)==0 | i==nsx(j),</span>
0538 <span class="comment">% saveas(gcf,[fname_,'_SA_fit_',deblank(vvarvecm(j,:)),'_',int2str(nfig)])</span>
0539 <span class="comment">% close(gcf)</span>
0540 <span class="comment">% end</span>
0541 <span class="comment">% end</span>
0542 <span class="comment">% end</span>
0543
0544
0545 disp(<span class="string">' '</span>)
0546 disp(<span class="string">' '</span>)
0547 disp(<span class="string">'Sensitivity table (significance and direction):'</span>)
0548 vav=char(zeros(1, size(param_names,2)+3 ));
0549 ibl = 12-size(vvarvecm,2);
0550 <span class="keyword">for</span> j=1:size(vvarvecm,1),
0551 vav = [vav, char(zeros(1,ibl)),vvarvecm(j,:)];
0552 <span class="keyword">end</span>
0553 disp(vav)
0554 <span class="keyword">for</span> j=1:npar+nshock, <span class="comment">%estim_params_.np,</span>
0555 <span class="comment">%disp([param_names(j,:), sprintf('%8.5g',SP(j,:))])</span>
0556 disp([param_names(j,:),<span class="string">' '</span>, sprintf(<span class="string">'%12.3g'</span>,PP(j,:))])
0557 disp([char(zeros(1, size(param_names,2)+3 )),sprintf(<span class="string">' (%6g)'</span>,SS(j,:))])
0558 <span class="keyword">end</span>
0559
0560
0561 disp(<span class="string">' '</span>)
0562 disp(<span class="string">' '</span>)
0563 disp(<span class="string">'Starting bivariate analysis:'</span>)
0564
0565 <span class="keyword">for</span> i=1:size(vvarvecm,1)
0566 <span class="keyword">if</span> options_.opt_gsa.ppost
0567 fnam = [<span class="string">'SA_fit_post_'</span>,deblank(vvarvecm(i,:))];
0568 <span class="keyword">else</span>
0569 <span class="keyword">if</span> options_.opt_gsa.pprior
0570 fnam = [<span class="string">'SA_fit_prior_'</span>,deblank(vvarvecm(i,:))];
0571 <span class="keyword">else</span>
0572 fnam = [<span class="string">'SA_fit_mc_'</span>,deblank(vvarvecm(i,:))];
0573 <span class="keyword">end</span>
0574 <span class="keyword">end</span>
0575 <a href="stab_map_2.html" class="code" title="function stab_map_2(x,alpha2,fnam, dirname)">stab_map_2</a>(x(ixx(1:nfilt0(i),i),:),alpha2,fnam, OutDir);
0576
0577 <span class="comment">% [pc,latent,explained] = pcacov(c0);</span>
0578 <span class="comment">% %figure, bar([explained cumsum(explained)])</span>
0579 <span class="comment">% ifig=0;</span>
0580 <span class="comment">% j2=0;</span>
0581 <span class="comment">% for j=1:npar+nshock,</span>
0582 <span class="comment">% i2=find(abs(pc(:,j))&gt;alphaPC);</span>
0583 <span class="comment">% if ~isempty(i2),</span>
0584 <span class="comment">% j2=j2+1;</span>
0585 <span class="comment">% if mod(j2,12)==1,</span>
0586 <span class="comment">% ifig=ifig+1;</span>
0587 <span class="comment">% figure('name',['PCA of the filtered sample ',deblank(vvarvecm(i,:)),' ',num2str(ifig)]),</span>
0588 <span class="comment">% end</span>
0589 <span class="comment">% subplot(3,4,j2-(ifig-1)*12)</span>
0590 <span class="comment">% bar(pc(i2,j)),</span>
0591 <span class="comment">% set(gca,'xticklabel',bayestopt_.name(i2)),</span>
0592 <span class="comment">% set(gca,'xtick',[1:length(i2)])</span>
0593 <span class="comment">% title(['PC ',num2str(j),'. Explained ',num2str(explained(j)),'%'])</span>
0594 <span class="comment">% end</span>
0595 <span class="comment">% if (mod(j2,12)==0 | j==(npar+nshock)) &amp; j2,</span>
0596 <span class="comment">% saveas(gcf,[fname_,'_SA_PCA_',deblank(vvarvecm(i,:)),'_',int2str(ifig)])</span>
0597 <span class="comment">% end</span>
0598 <span class="comment">% end</span>
0599 <span class="comment">% close all</span>
0600 <span class="keyword">end</span>
0601</pre></div>
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<html>
<head>
<title>Description of fnorm</title>
<meta name="keywords" content="fnorm">
<meta name="description" content="">
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
<meta name="generator" content="m2html &copy; 2003 Guillaume Flandin">
<meta name="robots" content="index, follow">
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</head>
<body>
<a name="_top"></a>
<div><a href="../index.html">Home</a> &gt; <a href="index.html">.</a> &gt; fnorm.m</div>
<!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png">&nbsp;Master index</a></td>
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<h1>fnorm
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong></strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function e = fnorm(p2,p,p1,perc) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"></pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="pnorm.html" class="code" title="function p = pnorm(x,m,s)">pnorm</a> PNORM The normal distribution function</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function e = fnorm(p2,p,p1,perc)</a>
0002 e = p - <a href="pnorm.html" class="code" title="function p = pnorm(x,m,s)">pnorm</a>(perc,p1,p2);</pre></div>
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<html>
<head>
<title>Description of forcst</title>
<meta name="keywords" content="forcst">
<meta name="description" content="">
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
<meta name="generator" content="m2html &copy; 2003 Guillaume Flandin">
<meta name="robots" content="index, follow">
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</head>
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<h1>forcst
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong></strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function [yf,var_yf]=forcst(dr,y0,k,m) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"></pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="make_ex_.html" class="code" title="function make_ex_">make_ex_</a> Copyright (C) 2001 Michel Juillard</li><li><a href="simult_.html" class="code" title="function y_=simult_(y0,dr,ex_,iorder)">simult_</a> Copyright (C) 2001 Michel Juillard</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function [yf,var_yf]=forcst(dr,y0,k,m)</a>
0002 <span class="keyword">global</span> M_ oo_ options_
0003
0004 options_.periods = k;
0005 <a href="make_ex_.html" class="code" title="function make_ex_">make_ex_</a>;
0006 yf = <a href="simult_.html" class="code" title="function y_=simult_(y0,dr,ex_,iorder)">simult_</a>(y0,dr,oo_.exo_simul(1:k,:),1);
0007
0008 nstatic = dr.nstatic;
0009 npred = dr.npred;
0010 j = find(kstate(dr.kae,2) &lt;= ykmin_+1);
0011 kae = dr.kae(j);
0012 nh = size(dr.ghx,2);
0013 hx = dr.ghx(nstatic+1:nstatic+npred,:);
0014 hu = dr.ghu(nstatic+1:nstatic+npred,:);
0015 <span class="keyword">if</span> ~isempty(kae)
0016 n = length(kae);
0017 tmp = sparse([1:n]',kae-sum(dr.kstate(:,2)&gt;M_.maximum_lag+1),ones(n,1),n,nh);
0018 hx = [hx; tmp];
0019 hu = [hu; zeros(n,M_.exo_nbr)];
0020 <span class="keyword">end</span>
0021 gx = [];
0022 k2 = [];
0023 <span class="keyword">if</span> nstatic &gt; 0
0024 gx = dr.ghx(1:nstatic,:);
0025 k2 = [1:nstatic]';
0026 <span class="keyword">end</span>
0027 <span class="keyword">if</span> size(dr.ghx,1) &gt; nstatic+npred
0028 gx = [gx; dr.ghx(nstatic+npred+1:<span class="keyword">end</span>,:)];
0029 k2 = [k2; [nstatic+npred+1:size(dr.ghx,1)]'];
0030 <span class="keyword">end</span>
0031
0032 k1 = dr.order_var([nstatic+1:nstatic+npred]);
0033 k2 = dr.order_var(k2);
0034
0035 sigma_u = hu*M_.Sigma_e*hu';
0036 sigma_y1 = 0;
0037 var_yf = zeros(k,M_.endo_nbr);
0038
0039
0040 <span class="keyword">if</span> isempty(k2)
0041 <span class="keyword">for</span> i=1:k
0042 sigma_y1 = sigma_y1+sigma_u;
0043 var_yf(i,k1) = diag(sigma_y1(1:npred,1:npred))';
0044 <span class="keyword">if</span> i == k
0045 <span class="keyword">break</span>
0046 <span class="keyword">end</span>
0047 sigma_u = hx*sigma_u*hx';
0048 <span class="keyword">end</span>
0049 <span class="keyword">else</span>
0050 <span class="keyword">for</span> i=1:k
0051 sigma_y1 = sigma_y1+sigma_u;
0052 var_yf(i,k1) = diag(sigma_y1(1:npred,1:npred))';
0053 sigma_y2 = gx*sigma_y1*gx';
0054 var_yf(i,k2) = diag(sigma_y2)';
0055 <span class="keyword">if</span> i == k
0056 <span class="keyword">break</span>
0057 <span class="keyword">end</span>
0058 sigma_u = hx*sigma_u*hx';
0059 <span class="keyword">end</span>
0060 <span class="keyword">end</span>
0061
0062
0063
0064
0065
0066
0067
0068
0069
0070
0071
0072
0073</pre></div>
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<title>Description of ftest</title>
<meta name="keywords" content="ftest">
<meta name="description" content="Copyright (C) 2001 Michel Juillard">
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
<meta name="generator" content="m2html &copy; 2003 Guillaume Flandin">
<meta name="robots" content="index, follow">
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<h1>ftest
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>Copyright (C) 2001 Michel Juillard</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function ftest (s1,s2) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> Copyright (C) 2001 Michel Juillard</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="dcompare.html" class="code" title="function dcompare(s1)">dcompare</a> Copyright (C) 2001 Michel Juillard</li><li><a href="gcompare.html" class="code" title="function gcompare(s1,s2)">gcompare</a> Copyright (C) 2001 Michel Juillard</li><li><a href="mcompare.html" class="code" title="function mcompare(s1,s2)">mcompare</a> Copyright (C) 2001 Michel Juillard</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <span class="comment">% Copyright (C) 2001 Michel Juillard</span>
0002 <span class="comment">%</span>
0003 <a name="_sub0" href="#_subfunctions" class="code">function ftest (s1,s2)</a>
0004
0005 <span class="keyword">global</span> nvx nvy x y lag1
0006
0007 <span class="keyword">if</span> size(s1,1) ~= 2
0008 error (<span class="string">'Spcifiez deux fichiers pour la comparaison.'</span>) ;
0009 <span class="keyword">end</span>
0010
0011 <span class="keyword">for</span> i = 1:2
0012 <span class="keyword">if</span> ~ isempty(find(abs(s1(i,:)) == 46))
0013 error (<span class="string">'Entrez les noms de fichiers sans extensions.'</span>) ;
0014 <span class="keyword">end</span>
0015 <span class="keyword">end</span>
0016
0017 s1 = [s1 [<span class="string">' '</span>;<span class="string">' '</span>]] ;
0018 file1 = [s1(1,1:min(find(abs(s1(1,:)) == 32))-1) <span class="string">'.BIN'</span>] ;
0019 file2 = [s1(2,1:min(find(abs(s1(2,:)) == 32))-1) <span class="string">'.BIN'</span>] ;
0020
0021 fid=fopen(file1,<span class="string">'r'</span>) ;
0022 n1 = fread(fid,1,<span class="string">'int'</span>) ;
0023 n2 = fread(fid,1,<span class="string">'int'</span>) ;
0024 n3 = fread(fid,1,<span class="string">'int'</span>) ;
0025 lag1 = fread(fid,4,<span class="string">'int'</span>) ;
0026 nvx = fread(fid,[n1,n3],<span class="string">'int'</span>) ;
0027 x = fread(fid,[n1,n2],<span class="string">'float64'</span>) ;
0028 fclose(fid) ;
0029 nvx = setstr(nvx) ;
0030
0031 fid=fopen(file2,<span class="string">'r'</span>) ;
0032 n1 = fread(fid,1,<span class="string">'int'</span>) ;
0033 n2 = fread(fid,1,<span class="string">'int'</span>) ;
0034 n3 = fread(fid,1,<span class="string">'int'</span>) ;
0035 lag2 = fread(fid,4,<span class="string">'int'</span>) ;
0036 nvy = fread(fid,[n1,n3],<span class="string">'int'</span>) ;
0037 y = fread(fid,[n1,n2],<span class="string">'float64'</span>) ;
0038 fclose(fid) ;
0039 nvy = setstr(nvy) ;
0040
0041 <span class="keyword">if</span> size(x,1) ~= size(y,1)
0042 error (<span class="string">'FTEST: The two files don''t have the same number of variables.'</span>);
0043 <span class="keyword">end</span>
0044
0045 <span class="keyword">for</span> i = 1:size(x,1)
0046 <span class="keyword">if</span> ~ strcmp(nvx(i,:),nvy(i,:))
0047 error (<span class="string">'FTEST: The two files don''t have the same variables.'</span>) ;
0048 <span class="keyword">end</span>
0049 <span class="keyword">end</span>
0050
0051 <span class="keyword">if</span> nnz(lag1 - lag2) &gt; 0
0052 error (<span class="string">'FTEST: Leads and lags aren''t the same in both files.'</span>) ;
0053 <span class="keyword">end</span>
0054
0055 j = zeros(size(s2,1),1);
0056 <span class="keyword">for</span> i=1:size(s2,1)
0057 k = strmatch(s2(i,:),nvx,<span class="string">'exact'</span>) ;
0058 <span class="keyword">if</span> isempty(k)
0059 t = [<span class="string">'FTEST: Variable '</span> s2(i) <span class="string">'doesn''t exist'</span>] ;
0060 error (t) ;
0061 <span class="keyword">else</span>
0062 j(i) =k;
0063 <span class="keyword">end</span>
0064 <span class="keyword">end</span>
0065
0066 y = y(j,:) ;
0067 x = x(j,:) ;
0068
0069 <span class="comment">%06/18/01 MJ replaced beastr by strmatch</span></pre></div>
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<title>Description of gamm_rnd</title>
<meta name="keywords" content="gamm_rnd">
<meta name="description" content="PURPOSE: a matrix of random draws from the gamma distribution">
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<h1>gamm_rnd
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>PURPOSE: a matrix of random draws from the gamma distribution</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function gb = gamm_rnd(nrow,m,k) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> PURPOSE: a matrix of random draws from the gamma distribution
---------------------------------------------------
USAGE: r = gamm_rnd(n,m,k)
where: n = the size of the vector drawn
m = a parameter such that the mean of the gamma = m/k
k = a parameter such that the variance of the gamma = m/(k^2)
note: m=r/2, k=2 equals chisq r random deviate
---------------------------------------------------
RETURNS:
r = an n x 1 vector of random numbers from the gamma distribution
--------------------------------------------------
SEE ALSO: gamm_inv, gamm_pdf, gamm_cdf
---------------------------------------------------
NOTE: written by: Michael Gordy, 15 Sept 1993
mbgordy@athena.mit.edu
---------------------------------------------------
REFERENCES: Luc Devroye, Non-Uniform Random Variate Generation,
New York: Springer Verlag, 1986, ch 9.3-6.</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="beta_rnd.html" class="code" title="function rnd = beta_rnd (n, a, b)">beta_rnd</a> PURPOSE: random draws from the beta(a,b) distribution</li><li><a href="rndprior.html" class="code" title="function y = rndprior(bayestopt_)">rndprior</a> </li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function gb = gamm_rnd(nrow,m,k)</a>
0002 <span class="comment">% PURPOSE: a matrix of random draws from the gamma distribution</span>
0003 <span class="comment">%---------------------------------------------------</span>
0004 <span class="comment">% USAGE: r = gamm_rnd(n,m,k)</span>
0005 <span class="comment">% where: n = the size of the vector drawn</span>
0006 <span class="comment">% m = a parameter such that the mean of the gamma = m/k</span>
0007 <span class="comment">% k = a parameter such that the variance of the gamma = m/(k^2)</span>
0008 <span class="comment">% note: m=r/2, k=2 equals chisq r random deviate</span>
0009 <span class="comment">%---------------------------------------------------</span>
0010 <span class="comment">% RETURNS:</span>
0011 <span class="comment">% r = an n x 1 vector of random numbers from the gamma distribution</span>
0012 <span class="comment">% --------------------------------------------------</span>
0013 <span class="comment">% SEE ALSO: gamm_inv, gamm_pdf, gamm_cdf</span>
0014 <span class="comment">%---------------------------------------------------</span>
0015 <span class="comment">% NOTE: written by: Michael Gordy, 15 Sept 1993</span>
0016 <span class="comment">% mbgordy@athena.mit.edu</span>
0017 <span class="comment">%---------------------------------------------------</span>
0018 <span class="comment">% REFERENCES: Luc Devroye, Non-Uniform Random Variate Generation,</span>
0019 <span class="comment">% New York: Springer Verlag, 1986, ch 9.3-6.</span>
0020
0021 <span class="keyword">if</span> nargin ~= 3
0022 error(<span class="string">'Wrong # of arguments to gamm_rnd'</span>);
0023 <span class="keyword">end</span>;
0024
0025 ncol = 1;
0026 gb=zeros(nrow,ncol);
0027 <span class="keyword">if</span> m&lt;=1
0028 <span class="comment">% Use RGS algorithm by Best, p. 426</span>
0029 c=1/m;
0030 t=0.07+0.75*sqrt(1-m);
0031 b=1+exp(-t)*m/t;
0032 <span class="keyword">for</span> i1=1:nrow
0033 <span class="keyword">for</span> i2=1:ncol
0034 accept=0;
0035 <span class="keyword">while</span> accept==0
0036 u=rand; w=rand; v=b*u;
0037 <span class="keyword">if</span> v&lt;=1
0038 x=t*(v^c);
0039 accept=((w&lt;=((2-x)/(2+x))) | (w&lt;=exp(-x)));
0040 <span class="keyword">else</span>
0041 x=-log(c*t*(b-v));
0042 y=x/t;
0043 accept=(((w*(m+y-m*y))&lt;=1) | (w&lt;=(y^(m-1))));
0044 <span class="keyword">end</span>
0045 <span class="keyword">end</span>
0046 gb(i1,i2)=x;
0047 <span class="keyword">end</span>
0048 <span class="keyword">end</span>
0049 <span class="keyword">else</span>
0050 <span class="comment">% Use Best's rejection algorithm XG, p. 410</span>
0051 b=m-1;
0052 c=3*m-0.75;
0053 <span class="keyword">for</span> i1=1:nrow
0054 <span class="keyword">for</span> i2=1:ncol
0055 accept=0;
0056 <span class="keyword">while</span> accept==0
0057 u=rand; v=rand;
0058 w=u*(1-u); y=sqrt(c/w)*(u-0.5);
0059 x=b+y;
0060 <span class="keyword">if</span> x &gt;= 0
0061 z=64*(w^3)*v*v;
0062 accept=(z&lt;=(1-2*y*y/x)) <span class="keyword">...</span>
0063 | (log(z)&lt;=(2*(b*log(x/b)-y)));
0064 <span class="keyword">end</span>
0065 <span class="keyword">end</span>
0066 gb(i1,i2)=x;
0067 <span class="keyword">end</span>
0068 <span class="keyword">end</span>
0069 <span class="keyword">end</span>
0070 gb=gb/k;
0071
0072</pre></div>
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<title>Description of gcompare</title>
<meta name="keywords" content="gcompare">
<meta name="description" content="Copyright (C) 2001 Michel Juillard">
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<h1>gcompare
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>Copyright (C) 2001 Michel Juillard</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function gcompare(s1,s2) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> Copyright (C) 2001 Michel Juillard
GCOMPARE : GCOMPARE ( [ 'file1' ; 'file2' ] , [ 'var1' ; 'var2' ...] )
This optional command plots the trajectories of a list of
variables in two different simulations. One plot is drawn
for each variable. The trajectories must have been previously
saved by the instruction DYNASAVE. The simulation in file1
is refered to as the base simulation.</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="ftest.html" class="code" title="function ftest (s1,s2)">ftest</a> Copyright (C) 2001 Michel Juillard</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <span class="comment">% Copyright (C) 2001 Michel Juillard</span>
0002 <span class="comment">%</span>
0003 <a name="_sub0" href="#_subfunctions" class="code">function gcompare(s1,s2)</a>
0004 <span class="comment">% GCOMPARE : GCOMPARE ( [ 'file1' ; 'file2' ] , [ 'var1' ; 'var2' ...] )</span>
0005 <span class="comment">% This optional command plots the trajectories of a list of</span>
0006 <span class="comment">% variables in two different simulations. One plot is drawn</span>
0007 <span class="comment">% for each variable. The trajectories must have been previously</span>
0008 <span class="comment">% saved by the instruction DYNASAVE. The simulation in file1</span>
0009 <span class="comment">% is refered to as the base simulation.</span>
0010
0011
0012 <span class="keyword">global</span> options_ M_
0013 <span class="keyword">global</span> nvx nvy x y lag1
0014
0015 <a href="ftest.html" class="code" title="function ftest (s1,s2)">ftest</a>(s1,s2) ;
0016
0017 ix = [1-lag1(1):size(x,2)-lag1(1)]' ;
0018 i = [lag1(1):size(ix,1)-lag1(2)+1]' ;
0019
0020 <span class="keyword">if</span> options_.smpl == 0
0021 i = [M_.maximum_lag:size(y,2)]' ;
0022 <span class="keyword">else</span>
0023 i = [options_.smpl(1):options_.smpl(2)]' ;
0024 <span class="keyword">end</span>
0025
0026 <span class="keyword">for</span> k = 1:size(x,1)
0027 figure ;
0028 plot (ix(i),x(k,i),ix(i),y(k,i)) ;
0029 xlabel ([<span class="string">'Periods'</span>]) ;
0030 title ([<span class="string">'Variable '</span> s2(k,:)]) ;
0031 l = min(i) + 1;
0032 ll = max(i) - 1 ;
0033 text (l,x(k,l),s1(1,:)) ;
0034 text (ll,y(k,ll),s1(2,:)) ;
0035 <span class="keyword">end</span>
0036
0037 <span class="comment">% 06/18/01 MJ corrected treatment of options_.smpl</span>
0038 <span class="comment">% 06/24/01 MJ removed color specification</span>
0039
0040
0041</pre></div>
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<meta name="keywords" content="gendata">
<meta name="description" content="[y,epsilon,alpha,eta] = gendata(T, ssf, a0)">
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<h1>gendata
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>[y,epsilon,alpha,eta] = gendata(T, ssf, a0)</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function [y,epsilon,alpha,eta] = gendata(T, ssf, a0) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> [y,epsilon,alpha,eta] = gendata(T, ssf, a0)
generates random data of the length T for the given state space form
and initial state</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <span class="comment">% [y,epsilon,alpha,eta] = gendata(T, ssf, a0)</span>
0002 <span class="comment">%</span>
0003 <span class="comment">% generates random data of the length T for the given state space form</span>
0004 <span class="comment">% and initial state</span>
0005
0006 <span class="comment">% $Id: gendata.m 532 2005-11-30 13:51:33Z kamenik $</span>
0007 <span class="comment">% Copyright 2005, Ondra Kamenik</span>
0008
0009 <a name="_sub0" href="#_subfunctions" class="code">function [y,epsilon,alpha,eta] = gendata(T, ssf, a0)</a>
0010
0011 m = size(ssf.T, 1);
0012 p = size(ssf.Z, 1);
0013 r = size(ssf.R, 2);
0014
0015 cholH = chol(ssf.H);
0016 cholQ = chol(ssf.Q);
0017
0018 epsilon = cholH*randn(p,T);
0019 eta = cholQ*randn(r, T);
0020
0021 y = zeros(p, T);
0022 alpha = zeros(m,T);
0023 alpha(:,1) = a0;
0024
0025 <span class="keyword">for</span> t = 1:T
0026 y(:,t) = ssf.Z*alpha(:,t) + epsilon(:,t);
0027 <span class="keyword">if</span> t ~= T
0028 alpha(:,t+1) = ssf.T*alpha(:,t) + ssf.R*eta(:,t);
0029 <span class="keyword">end</span>
0030 <span class="keyword">end</span></pre></div>
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<meta name="description" content="proc gmchol(A);">
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<h1>generalized_cholesky
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>proc gmchol(A);</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function AA = generalized_cholesky(A); </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> proc gmchol(A);
/* calculates the Gill-Murray generalized choleski decomposition */
/* input matrix A must be non-singular and symmetric */
/* Author: Jeff Gill. Part of the Hessian Project. */
local i,j,k,n,sum,R,theta_j,norm_A,phi_j,delta,xi_j,gamm,E,beta_j;</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="rows.html" class="code" title="function nr=rows(x)">rows</a> </li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="dynare_estimation.html" class="code" title="function dynare_estimation(var_list_)">dynare_estimation</a> </li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <span class="comment">% proc gmchol(A);</span>
0002 <span class="comment">% /* calculates the Gill-Murray generalized choleski decomposition */</span>
0003 <span class="comment">% /* input matrix A must be non-singular and symmetric */</span>
0004 <span class="comment">% /* Author: Jeff Gill. Part of the Hessian Project. */</span>
0005 <span class="comment">% local i,j,k,n,sum,R,theta_j,norm_A,phi_j,delta,xi_j,gamm,E,beta_j;</span>
0006
0007 <a name="_sub0" href="#_subfunctions" class="code">function AA = generalized_cholesky(A);</a>
0008
0009 n = <a href="rows.html" class="code" title="function nr=rows(x)">rows</a>(A);
0010 R = eye(n);
0011 E = zeros(n,n);
0012 norm_A = max(transpose(sum(abs(A))));
0013 gamm = max(abs(diag(A)));
0014 delta = max([eps*norm_A;eps]);
0015
0016 <span class="keyword">for</span> j = 1:n;
0017 theta_j = 0;
0018 <span class="keyword">for</span> i=1:n;
0019 somme = 0;
0020 <span class="keyword">for</span> k=1:i-1;
0021 somme = somme + R(k,i)*R(k,j);
0022 <span class="keyword">end</span>;
0023 R(i,j) = (A(i,j) - somme)/R(i,i);
0024 <span class="keyword">if</span> (A(i,j) -somme) &gt; theta_j;
0025 theta_j = A(i,j) - somme;
0026 <span class="keyword">end</span>;
0027 <span class="keyword">if</span> i &gt; j;
0028 R(i,j) = 0;
0029 <span class="keyword">end</span>;
0030 <span class="keyword">end</span>;
0031 somme = 0;
0032 <span class="keyword">for</span> k=1:j-1;
0033 somme = somme + R(k,j)^2;
0034 <span class="keyword">end</span>;
0035 phi_j = A(j,j) - somme;
0036 <span class="keyword">if</span> j+1 &lt;= n;
0037 xi_j = max(abs(A((j+1):n,j)));
0038 <span class="keyword">else</span>;
0039 xi_j = abs(A(n,j));
0040 <span class="keyword">end</span>;
0041 beta_j = sqrt(max([gamm ; (xi_j/n) ; eps]));
0042 <span class="keyword">if</span> all(delta &gt;= [abs(phi_j);((theta_j^2)/(beta_j^2))]);
0043 E(j,j) = delta - phi_j;
0044 <span class="keyword">elseif</span> all(abs(phi_j) &gt;= [((delta^2)/(beta_j^2));delta]);
0045 E(j,j) = abs(phi_j) - phi_j;
0046 <span class="keyword">elseif</span> all(((theta_j^2)/(beta_j^2)) &gt;= [delta;abs(phi_j)]);
0047 E(j,j) = ((theta_j^2)/(beta_j^2)) - phi_j;
0048 <span class="keyword">end</span>;
0049 R(j,j) = sqrt(A(j,j) - somme + E(j,j));
0050 <span class="keyword">end</span>;
0051 AA = transpose(R)*R;</pre></div>
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<title>Description of generalized_cholesky2</title>
<meta name="keywords" content="generalized_cholesky2">
<meta name="description" content="/*">
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<!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png">&nbsp;Master index</a></td>
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<h1>generalized_cholesky2
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>/*</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function AA = generalized_cholesky2(A) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> /*
** By Jeff Gill, April 2002.
**
** This procedure produces:
**
** y = chol(A+E), where E is a diagonal matrix with each element as small
** as possible, and A and E are the same size. E diagonal values are
** constrained by iteravely updated Gerschgorin bounds.
**
** REFERENCES:
**
** Jeff Gill and Gary King. 1998. &quot;`Hessian not Invertable.' Help!&quot;
** manuscript in progress, Harvard University.
**
** Robert B. Schnabel and Elizabeth Eskow. 1990. &quot;A New Modified Cholesky
** Factorization,&quot; SIAM Journal of Scientific Statistical Computating,
** 11, 6: 1136-58.
**
**
**
** Stphane Adjemian (2003): translation from Gauss to Matlab.
*/</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <span class="comment">% /*</span>
0002 <span class="comment">% ** By Jeff Gill, April 2002.</span>
0003 <span class="comment">% **</span>
0004 <span class="comment">% ** This procedure produces:</span>
0005 <span class="comment">% **</span>
0006 <span class="comment">% ** y = chol(A+E), where E is a diagonal matrix with each element as small</span>
0007 <span class="comment">% ** as possible, and A and E are the same size. E diagonal values are</span>
0008 <span class="comment">% ** constrained by iteravely updated Gerschgorin bounds.</span>
0009 <span class="comment">% **</span>
0010 <span class="comment">% ** REFERENCES:</span>
0011 <span class="comment">% **</span>
0012 <span class="comment">% ** Jeff Gill and Gary King. 1998. &quot;`Hessian not Invertable.' Help!&quot;</span>
0013 <span class="comment">% ** manuscript in progress, Harvard University.</span>
0014 <span class="comment">% **</span>
0015 <span class="comment">% ** Robert B. Schnabel and Elizabeth Eskow. 1990. &quot;A New Modified Cholesky</span>
0016 <span class="comment">% ** Factorization,&quot; SIAM Journal of Scientific Statistical Computating,</span>
0017 <span class="comment">% ** 11, 6: 1136-58.</span>
0018 <span class="comment">% **</span>
0019 <span class="comment">% **</span>
0020 <span class="comment">% **</span>
0021 <span class="comment">% ** Stphane Adjemian (2003): translation from Gauss to Matlab.</span>
0022 <span class="comment">% */</span>
0023 <a name="_sub0" href="#_subfunctions" class="code">function AA = generalized_cholesky2(A)</a>
0024
0025 n = size(A,1);
0026 L = zeros(n,n);
0027 deltaprev = 0;
0028 gamm = max(abs(diag(A)));
0029 tau = eps^(1/3);
0030
0031 <span class="keyword">if</span> min(eig(A)) &gt; 0;
0032 tau = -1000000;
0033 <span class="keyword">end</span>;
0034
0035 norm_A = max(sum(abs(A))');
0036 gamm = max(abs(diag(A)));
0037 delta = max([eps*norm_A;eps]);
0038 Pprod = eye(n);
0039
0040 <span class="keyword">if</span> n &gt; 2;
0041 <span class="keyword">for</span> k = 1,n-2;
0042 <span class="keyword">if</span> min((diag(A(k+1:n,k+1:n))' - A(k,k+1:n).^2/A(k,k))') &lt; tau*gamm <span class="keyword">...</span>
0043 &amp; min(eig(A((k+1):n,(k+1):n))) &lt; 0;
0044 [tmp,dmax] = max(diag(A(k:n,k:n)));
0045 <span class="keyword">if</span> A(k+dmax-1,k+dmax-1) &gt; A(k,k);
0046 P = eye(n);
0047 Ptemp = P(k,:);
0048 P(k,:) = P(k+dmax-1,:);
0049 P(k+dmax-1,:) = Ptemp;
0050 A = P*A*P;
0051 L = P*L*P;
0052 Pprod = P*Pprod;
0053 <span class="keyword">end</span>;
0054 g = zeros(n-(k-1),1);
0055 <span class="keyword">for</span> i=k:n;
0056 <span class="keyword">if</span> i == 1;
0057 sum1 = 0;
0058 <span class="keyword">else</span>;
0059 sum1 = sum(abs(A(i,k:(i-1)))');
0060 <span class="keyword">end</span>;
0061 <span class="keyword">if</span> i == n;
0062 sum2 = 0;
0063 <span class="keyword">else</span>;
0064 sum2 = sum(abs(A((i+1):n,i)));
0065 <span class="keyword">end</span>;
0066 g(i-k+1) = A(i,i) - sum1 - sum2;
0067 <span class="keyword">end</span>;
0068 [tmp,gmax] = max(g);
0069 <span class="keyword">if</span> gmax ~= k;
0070 P = eye(n);
0071 Ptemp = P(k,:);
0072 P(k,:) = P(k+dmax-1,:);
0073 P(k+dmax-1,:) = Ptemp;
0074 A = P*A*P;
0075 L = P*L*P;
0076 Pprod = P*Pprod;
0077 <span class="keyword">end</span>;
0078 normj = sum(abs(A(k+1:n,k)));
0079 delta = max([0;deltaprev;-A(k,k)+normj;-A(k,k)+tau*gamm]);
0080 <span class="keyword">if</span> delta &gt; 0;
0081 A(k,k) = A(k,k) + delta;
0082 deltaprev = delta;
0083 <span class="keyword">end</span>;
0084 <span class="keyword">end</span>;
0085 A(k,k) = sqrt(A(k,k));
0086 L(k,k) = A(k,k);
0087 <span class="keyword">for</span> i=k+1:n;
0088 <span class="keyword">if</span> L(k,k) &gt; eps;
0089 A(i,k) = A(i,k)/L(k,k);
0090 <span class="keyword">end</span>;
0091 L(i,k) = A(i,k);
0092 A(i,k+1:i) = A(i,k+1:i) - L(i,k)*L(k+1:i,k)';
0093 <span class="keyword">if</span> A(i,i) &lt; 0;
0094 A(i,i) = 0;
0095 <span class="keyword">end</span>;
0096 <span class="keyword">end</span>;
0097 <span class="keyword">end</span>;
0098 <span class="keyword">end</span>;
0099 A(n-1,n) = A(n,n-1);
0100 eigvals = eig(A(n-1:n,n-1:n));
0101 dlist = [ 0 ; deltaprev ;<span class="keyword">...</span>
0102 -min(eigvals)+tau*max((inv(1-tau)*max(eigvals)-min(eigvals))|gamm) ];
0103 <span class="keyword">if</span> dlist(1) &gt; dlist(2);
0104 delta = dlist(1);
0105 <span class="keyword">else</span>;
0106 delta = dlist(2);
0107 <span class="keyword">end</span>;
0108 <span class="keyword">if</span> delta &lt; dlist(3);
0109 delta = dlist(3);
0110 <span class="keyword">end</span>;
0111 <span class="keyword">if</span> delta &gt; 0;
0112 A(n-1,n-1) = A(n-1,n-1) + delta;
0113 A(n,n) = A(n,n) + delta;
0114 deltaprev = delta;
0115 <span class="keyword">end</span>;
0116 A(n-1,n-1) = sqrt(A(n-1,n-1));
0117 L(n-1,n-1) = A(n-1,n-1);
0118 A(n,n-1) = A(n,n-1)/L(n-1,n-1);
0119 L(n,n-1) = A(n,n-1);
0120 A(n,n) = sqrt(A(n,n) - L(n,(n-1))^2);
0121 L(n,n) = A(n,n);
0122 AA = Pprod'*L'*Pprod';</pre></div>
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<title>Description of get_the_name</title>
<meta name="keywords" content="get_the_name">
<meta name="description" content="stephane.adjemian@cepremap.cnrs.fr [07-13-2004]">
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<!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png">&nbsp;Master index</a></td>
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<h1>get_the_name
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>stephane.adjemian@cepremap.cnrs.fr [07-13-2004]</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function [nam,texnam] = get_the_name(k,TeX) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> stephane.adjemian@cepremap.cnrs.fr [07-13-2004]</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="McMCDiagnostics.html" class="code" title="function McmcDiagnostic">McMCDiagnostics</a> stephane.adjemian@ens.fr [06-04-2005]</li><li><a href="PlotPosteriorDistributions.html" class="code" title="function PlotPosteriorDistributions()">PlotPosteriorDistributions</a> stephane.adjemian@ens.fr [09-09-2005]</li><li><a href="mode_check.html" class="code" title="function mode_check(x,fval,hessian,gend,data,lb,ub)">mode_check</a> </li><li><a href="plot_priors.html" class="code" title="function plot_priors">plot_priors</a> stephane.adjemian@cepremap.cnrs.fr [07-31-2004]</li><li><a href="posterior_distribution.html" class="code" title="function [borneinf,bornesup,x1,x2,f1,f2,top,nam,texnam] =posterior_distribution(indx,number_of_mh_files,first_mh_file,first_line,number_of_blocks,number_of_simulations,number_of_simulations_per_file,TeX);">posterior_distribution</a> stephane.adjemian@cepremap.cnrs.fr [07-15-2004]</li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function [nam,texnam] = get_the_name(k,TeX)</a>
0002 <span class="comment">% stephane.adjemian@cepremap.cnrs.fr [07-13-2004]</span>
0003 <span class="keyword">global</span> M_ estim_params_ options_
0004
0005 nam = [];
0006 texnam = [];
0007
0008 nvx = estim_params_.nvx;
0009 nvn = estim_params_.nvn;
0010 ncx = estim_params_.ncx;
0011 ncn = estim_params_.ncn;
0012
0013 <span class="keyword">if</span> k &lt;= nvx
0014 vname = deblank(M_.exo_names(estim_params_.var_exo(k,1),:));
0015 nam = [<span class="string">'SE_'</span>,vname];
0016 <span class="keyword">if</span> TeX
0017 tname = deblank(M_.exo_names_tex(estim_params_.var_exo(k,1),:));
0018 texnam = [<span class="string">'$ SE_{'</span> tname <span class="string">'} $'</span>];
0019 <span class="keyword">end</span>
0020 <span class="keyword">elseif</span> k &lt;= (nvx+nvn)
0021 vname = deblank(options_.varobs(estim_params_.var_endo(k-estim_params_.nvx,1),:));
0022 nam=[<span class="string">'SE_EOBS_'</span>,vname];
0023 <span class="keyword">if</span> TeX
0024 tname = deblank(options_.TeX_varobs(estim_params_.var_endo(k-estim_params_.nvx,1),:));
0025 texnam = [<span class="string">'$ SE_{'</span> tname <span class="string">'} $'</span>];
0026 <span class="keyword">end</span>
0027 <span class="keyword">elseif</span> k &lt;= (nvx+nvn+ncx)
0028 jj = k - (nvx+nvn);
0029 k1 = estim_params_.corrx(jj,1);
0030 k2 = estim_params_.corrx(jj,2);
0031 vname = [deblank(M_.exo_names(k1,:)) <span class="string">'_'</span> deblank(M_.exo_names(k2,:))];
0032 nam=[<span class="string">'CC_'</span>,vname];
0033 <span class="keyword">if</span> TeX
0034 tname = [deblank(M_.exo_names_tex(k1,:)) <span class="string">','</span> deblank(M_.exo_names_tex(k2,:))];
0035 texnam = [<span class="string">'$ CC_{'</span> tname <span class="string">'} $'</span>];
0036 <span class="keyword">end</span>
0037 <span class="keyword">elseif</span> k &lt;= (nvx+nvn+ncx+ncn)
0038 jj = k - (nvx+nvn+ncx);
0039 k1 = estim_params_.corrn(jj,1);
0040 k2 = estim_params_.corrn(jj,2);
0041 vname = [deblank(M_.endo_names(k1,:)) <span class="string">'_'</span> deblank(M_.endo_names(k2,:))];
0042 nam=[<span class="string">'CC_EOBS_'</span> vname];
0043 <span class="keyword">if</span> TeX
0044 tname = [deblank(M_.endo_names_tex(k1,:)) <span class="string">','</span> deblank(M_.endo_names_tex(k2,:))];
0045 texnam =[<span class="string">'$ CC_{'</span> tname <span class="string">'} $'</span>];
0046 <span class="keyword">end</span>
0047 <span class="keyword">else</span>
0048 jj = k - (nvx+nvn+ncx+ncn);
0049 jj1 = estim_params_.param_vals(jj,1);
0050 nam = deblank(M_.param_names(jj1,:));
0051 <span class="keyword">if</span> TeX
0052 texnam = [<span class="string">'$ '</span> deblank(M_.param_names_tex(jj1,:)) <span class="string">' $'</span>];
0053 <span class="keyword">end</span>
0054 <span class="keyword">end</span>
0055
0056
0057 <span class="comment">% SA 07-15-2004 Added TeX names.</span>
0058 <span class="comment">% SA 12-02-2004 Changed non-TeX names format.</span>
0059 <span class="comment">% SA 01-11-2005 v3TOv4</span></pre></div>
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<h1>global_initialization
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong></strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function global_initialization() </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"></pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function global_initialization()</a>
0002 <span class="keyword">global</span> oo_ M_ options_ ct_ endval_ rplottype_
0003
0004 ct_=0;
0005 endval_=0;
0006
0007 options_.rplottype = 0;
0008 options_.smpl = 0;
0009 options_.dynatol = 0.00001;
0010 options_.maxit_ = 10;
0011 options_.slowc = 1;
0012 options_.timing = 0;
0013 options_.gstep = 1e-2;
0014 options_.debug = 0
0015 options_.initval_file = 0;
0016
0017 oo_.exo_simul = [];
0018 oo_.y_simul = [];
0019 oo_.dr = struct([]);
0020 oo_.exo_det_steady_state = [];
0021 oo_.exo_det_simul = [];</pre></div>
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/* Created by mdot for Matlab */
digraph m2html {
CutSample -> CheckPath;
DiffuseKalmanSmoother1 -> steady;
DiffuseKalmanSmoother3 -> steady;
DiffuseKalmanSmootherH1 -> steady;
DiffuseKalmanSmootherH3 -> steady;
DiffuseKalmanSmootherH3corr -> steady;
DsgeLikelihood -> DiffuseLikelihood3;
DsgeLikelihood -> DiffuseLikelihoodH1;
DsgeLikelihood -> DiffuseLikelihoodH3;
DsgeLikelihood -> DiffuseLikelihoodH3corr;
DsgeLikelihood -> dynare_resolve;
DsgeLikelihood -> kalman_filter;
DsgeLikelihood -> lyapunov_symm;
DsgeLikelihood -> priordens;
DsgeSmoother -> DiffuseKalmanSmoother1;
DsgeSmoother -> DiffuseKalmanSmoother3;
DsgeSmoother -> DiffuseKalmanSmootherH1;
DsgeSmoother -> DiffuseKalmanSmootherH3;
DsgeSmoother -> dynare_resolve;
DsgeSmoother -> lyapunov_symm;
DsgeSmoother -> set_all_parameters;
GetAllPosteriorDraws -> CheckPath;
GetOneDraw -> metropolis_draw;
GetPosteriorParametersStatistics -> CheckPath;
GetPosteriorParametersStatistics -> GetAllPosteriorDraws;
GetPosteriorParametersStatistics -> posterior_moments;
McMCDiagnostics -> CheckPath;
McMCDiagnostics -> get_the_name;
PlotPosteriorDistributions -> CheckPath;
PlotPosteriorDistributions -> draw_prior_density;
PlotPosteriorDistributions -> get_the_name;
PosteriorIRF -> CheckPath;
PosteriorIRF -> GetOneDraw;
PosteriorIRF -> ReshapeMatFiles;
PosteriorIRF -> irf;
PosteriorIRF -> posterior_moments;
PosteriorIRF -> resol;
PosteriorIRF -> set_parameters;
PosteriorSmoother -> CheckPath;
PosteriorSmoother -> DsgeSmoother;
PosteriorSmoother -> GetOneDraw;
PosteriorSmoother -> resol;
PosteriorSmoother -> set_all_parameters;
ReshapeMatFiles -> CheckPath;
UnivariateSpectralDensity -> kalman_transition_matrix;
UnivariateSpectralDensity -> lyapunov_symm;
beta_rnd -> gamm_rnd;
bksup -> irf;
bksup -> selif;
bksup1 -> irf;
bksupk -> irf;
bksupk -> selif;
calib -> calib_obj;
calib -> calib_obj2;
calib -> indnv;
calib -> resol;
calib -> union;
calib_obj -> bicgstab;
calib_obj -> f_var;
calib_obj -> lyapunov_symm;
calib_obj2 -> lyapunov_symm;
check -> print_info;
check -> resol;
check -> set_default_option;
check_mh -> my_subplot;
compDist -> lpdfgam;
compDist -> mj_qgamma;
compDist -> posterior_density_estimate;
compDist -> qbeta;
compDist -> qgamma;
compDist -> qnorm;
csminwel -> bfgsi;
csminwel -> csminit;
csminwel -> numgrad;
dcompare -> bseastr;
dcompare -> ftest;
disp_moments -> set_default_option;
disp_moments -> table;
disp_th_moments -> table;
disp_th_moments -> th_autocovariances;
dlognorm -> dnorm;
dr1 -> hessian;
dr1 -> qzdiv;
dr1 -> resid;
dr1 -> set_default_option;
dr1 -> set_state_space;
dr1 -> sylvester3;
dr1 -> sylvester3a;
dr1 -> transition_matrix;
dr11 -> qzdiv;
dr11 -> set_default_option;
dr11 -> set_state_space;
dr2 -> dr1;
draw_prior_density -> lpdfgam;
draw_prior_density -> mj_qgamma;
draw_prior_density -> qbeta;
draw_prior_density -> qgamma;
draw_prior_density -> qnorm;
dynare_MC -> DsgeLikelihood;
dynare_MC -> DsgeSmoother;
dynare_MC -> prior_bounds;
dynare_MC -> read_variables;
dynare_MC -> set_default_option;
dynare_MC -> set_prior;
dynare_MC -> set_state_space;
dynare_estimation -> CheckPath;
dynare_estimation -> CutSample;
dynare_estimation -> DsgeLikelihood;
dynare_estimation -> DsgeSmoother;
dynare_estimation -> GetPosteriorParametersStatistics;
dynare_estimation -> McMCDiagnostics;
dynare_estimation -> PlotPosteriorDistributions;
dynare_estimation -> PosteriorIRF;
dynare_estimation -> asamin;
dynare_estimation -> csminwel;
dynare_estimation -> generalized_cholesky;
dynare_estimation -> hessian;
dynare_estimation -> initial_estimation_checks;
dynare_estimation -> marginal_density;
dynare_estimation -> metropolis;
dynare_estimation -> metropolis_draw;
dynare_estimation -> mode_check;
dynare_estimation -> newrat;
dynare_estimation -> plot_priors;
dynare_estimation -> pltorg;
dynare_estimation -> prior_bounds;
dynare_estimation -> read_variables;
dynare_estimation -> set_default_option;
dynare_estimation -> set_prior;
dynare_estimation -> set_state_space;
dynare_estimation -> union;
dynare_resolve -> kalman_transition_matrix;
dynare_resolve -> resol;
dynare_sensitivity -> CheckPath;
dynare_sensitivity -> dynare_MC;
dynare_sensitivity -> filt_mc_;
dynare_sensitivity -> read_variables;
dynare_sensitivity -> set_default_option;
dynare_sensitivity -> stab_map_;
dynare_solve -> set_default_option;
dynare_solve -> solve1;
fgamma -> pgamma;
figamm -> pgamma;
filt_mc_ -> CheckPath;
filt_mc_ -> DsgeSmoother;
filt_mc_ -> cumplot;
filt_mc_ -> kernel_density_estimate;
filt_mc_ -> mh_optimal_bandwidth;
filt_mc_ -> priordens;
filt_mc_ -> set_all_parameters;
filt_mc_ -> smirnov;
filt_mc_ -> stab_map_2;
filt_mc_ -> steady_;
fnorm -> pnorm;
forcst -> make_ex_;
forcst -> simult_;
gcompare -> ftest;
generalized_cholesky -> rows;
initial_estimation_checks -> DsgeLikelihood;
initvalf_ -> dy_date;
irf -> simult_;
lnsrch -> check;
lnsrch1 -> check;
lptauSEQ -> LPTAU;
lyapunov_symm -> my_ordeig;
make_ex_ -> set_default_option;
make_y_ -> set_default_option;
marginal_density -> CheckPath;
marginal_density -> qchisq;
mcompare -> ftest;
metropolis -> CheckPath;
metropolis -> DsgeLikelihood;
metropolis_draw -> CheckPath;
mgnldnsty -> matrictint;
mgnldnsty -> rfvar3;
mgnldnsty -> varprior;
mj_qgamma -> lpdfgam;
mj_qgamma -> pgamma;
mode_check -> DsgeLikelihood;
mode_check -> get_the_name;
mode_check -> hessian;
mode_check -> pltorg;
mr_gstep -> csminit;
newrat -> check;
newrat -> csminit;
newrat -> mr_gstep;
newrat -> mr_hessian;
olr -> disp_dr;
olr -> disp_moments;
olr -> disp_th_moments;
olr -> dyn2vec;
olr -> irf;
olr -> make_ex_;
olr -> olr1;
olr -> set_default_option;
olr -> simult;
olr -> table;
olr1 -> check;
olr1 -> dynare_solve;
olr1 -> olr2;
olr2 -> union;
osr -> disp_dr;
osr -> disp_moments;
osr -> disp_th_moments;
osr -> dyn2vec;
osr -> irf;
osr -> make_ex_;
osr -> osr1;
osr -> set_default_option;
osr -> simult;
osr -> table;
osr1 -> check;
osr1 -> dynare_solve;
osr1 -> osr_obj;
osr1 -> resol;
osr_obj -> resol;
p2toperc -> fbeta;
p2toperc -> fgamma;
p2toperc -> figamm;
p2toperc -> qnorm;
plot_priors -> draw_prior_density;
plot_priors -> get_the_name;
plot_priors -> pltorg;
posterior_distribution -> draw_prior_density;
posterior_distribution -> get_the_name;
posterior_distribution -> kernel_density_estimate;
posterior_distribution -> mh_optimal_bandwidth;
posterior_moments -> kernel_density_estimate;
posterior_moments -> mh_optimal_bandwidth;
print_info -> set_default_option;
prior_bounds -> mj_qgamma;
prior_bounds -> qbeta;
prior_bounds -> qnorm;
priordens -> lpdfgam;
priordens -> lpdfgbeta;
priordens -> lpdfig1;
priordens -> lpdfig2;
priordens -> lpdfnorm;
qbeta -> dbeta;
qchisq -> qgamma;
qgamma -> dgamma;
qgamma -> pgamma;
qzdiv -> qzswitch;
resol -> dr1;
resol -> dynare_solve;
resol -> resid;
resol -> set_default_option;
resol1 -> dr11;
resol1 -> dynare_solve;
resol1 -> resid;
rndprior -> beta_rnd;
rndprior -> gamm_rnd;
set_prior -> inverse_gamma_specification;
set_prior -> uniform_specification;
set_state_space -> union;
sim1 -> bksup1;
simk -> bksupk;
simk -> ffill;
simk -> indnv;
simk -> selif;
simk -> union;
simul -> dyn2vec;
simul -> make_ex_;
simul -> make_y_;
simul -> set_default_option;
simul -> sim1;
simul -> simk;
simul -> simult;
simult -> simult_;
simult_ -> check;
simult_ -> dynare_solve;
simult_ -> set_default_option;
solve1 -> check;
solve1 -> lnsrch1;
stab_map_ -> lptauSEQ;
stab_map_ -> stab_map_1;
stab_map_ -> stab_map_2;
stab_map_ -> stoch_simul;
stab_map_1 -> cumplot;
stab_map_1 -> smirnov;
stab_map_marginal -> kernel_density_estimate;
stab_map_marginal -> mh_optimal_bandwidth;
steady -> set_default_option;
steady -> steady_;
steady_ -> check;
steady_ -> dynare_solve;
stoch_simul -> UnivariateSpectralDensity;
stoch_simul -> check_model;
stoch_simul -> disp_dr;
stoch_simul -> disp_moments;
stoch_simul -> disp_th_moments;
stoch_simul -> dyn2vec;
stoch_simul -> irf;
stoch_simul -> pltorg;
stoch_simul -> print_info;
stoch_simul -> resol;
stoch_simul -> set_default_option;
stoch_simul -> simult;
stoch_simul -> table;
th_autocovariances -> kalman_transition_matrix;
th_autocovariances -> lyapunov_symm;
CheckPath [URL="CheckPath.html"];
CreateBenchmark [URL="CreateBenchmark.html"];
CutSample [URL="CutSample.html"];
DiffuseKalmanSmoother1 [URL="DiffuseKalmanSmoother1.html"];
DiffuseKalmanSmoother3 [URL="DiffuseKalmanSmoother3.html"];
DiffuseKalmanSmootherH1 [URL="DiffuseKalmanSmootherH1.html"];
DiffuseKalmanSmootherH3 [URL="DiffuseKalmanSmootherH3.html"];
DiffuseKalmanSmootherH3corr [URL="DiffuseKalmanSmootherH3corr.html"];
DiffuseLikelihood1 [URL="DiffuseLikelihood1.html"];
DiffuseLikelihood3 [URL="DiffuseLikelihood3.html"];
DiffuseLikelihoodH1 [URL="DiffuseLikelihoodH1.html"];
DiffuseLikelihoodH3 [URL="DiffuseLikelihoodH3.html"];
DiffuseLikelihoodH3corr [URL="DiffuseLikelihoodH3corr.html"];
DsgeLikelihood [URL="DsgeLikelihood.html"];
DsgeSmoother [URL="DsgeSmoother.html"];
GetAllPosteriorDraws [URL="GetAllPosteriorDraws.html"];
GetOneDraw [URL="GetOneDraw.html"];
GetPosteriorParametersStatistics [URL="GetPosteriorParametersStatistics.html"];
InitSeed [URL="InitSeed.html"];
LPTAU [URL="LPTAU.html"];
MakeAllFigures [URL="MakeAllFigures.html"];
McMCDiagnostics [URL="McMCDiagnostics.html"];
PlotPosteriorDistributions [URL="PlotPosteriorDistributions.html"];
PosteriorIRF [URL="PosteriorIRF.html"];
PosteriorSmoother [URL="PosteriorSmoother.html"];
ReshapeMatFiles [URL="ReshapeMatFiles.html"];
UnivariateSpectralDensity [URL="UnivariateSpectralDensity.html"];
asamin [URL="asamin.html"];
beta_rnd [URL="beta_rnd.html"];
bfgsi [URL="bfgsi.html"];
bicgstab [URL="bicgstab.html"];
bksup [URL="bksup.html"];
bksup1 [URL="bksup1.html"];
bksupk [URL="bksupk.html"];
bseastr [URL="bseastr.html"];
calib [URL="calib.html"];
calib_obj [URL="calib_obj.html"];
calib_obj2 [URL="calib_obj2.html"];
check [URL="check.html"];
check_mh [URL="check_mh.html"];
check_model [URL="check_model.html"];
compDist [URL="compDist.html"];
csminit [URL="csminit.html"];
csminwel [URL="csminwel.html"];
cumplot [URL="cumplot.html"];
datatomfile [URL="datatomfile.html"];
dbeta [URL="dbeta.html"];
dcompare [URL="dcompare.html"];
dgamma [URL="dgamma.html"];
disp_dr [URL="disp_dr.html"];
disp_moments [URL="disp_moments.html"];
disp_th_moments [URL="disp_th_moments.html"];
dlognorm [URL="dlognorm.html"];
dnorm [URL="dnorm.html"];
dr1 [URL="dr1.html"];
dr11 [URL="dr11.html"];
dr2 [URL="dr2.html"];
draw_prior_density [URL="draw_prior_density.html"];
dsample [URL="dsample.html"];
dy_date [URL="dy_date.html"];
dyn2vec [URL="dyn2vec.html"];
dynare [URL="dynare.html"];
dynare_MC [URL="dynare_MC.html"];
dynare_estimation [URL="dynare_estimation.html"];
dynare_resolve [URL="dynare_resolve.html"];
dynare_sensitivity [URL="dynare_sensitivity.html"];
dynare_solve [URL="dynare_solve.html"];
dynasave [URL="dynasave.html"];
dynatype [URL="dynatype.html"];
f_var [URL="f_var.html"];
fbeta [URL="fbeta.html"];
ff1_ [URL="ff1_.html"];
ffill [URL="ffill.html"];
fgamma [URL="fgamma.html"];
figamm [URL="figamm.html"];
filt_mc_ [URL="filt_mc_.html"];
fnorm [URL="fnorm.html"];
forcst [URL="forcst.html"];
ftest [URL="ftest.html"];
gamm_rnd [URL="gamm_rnd.html"];
gcompare [URL="gcompare.html"];
gendata [URL="gendata.html"];
generalized_cholesky [URL="generalized_cholesky.html"];
generalized_cholesky2 [URL="generalized_cholesky2.html"];
get_the_name [URL="get_the_name.html"];
global_initialization [URL="global_initialization.html"];
hessian [URL="hessian.html"];
hessian_sparse [URL="hessian_sparse.html"];
indnv [URL="indnv.html"];
initial_estimation_checks [URL="initial_estimation_checks.html"];
initvalf_ [URL="initvalf_.html"];
inverse_gamma_specification [URL="inverse_gamma_specification.html"];
irf [URL="irf.html"];
kalman_filter [URL="kalman_filter.html"];
kalman_smoother [URL="kalman_smoother.html"];
kalman_transition_matrix [URL="kalman_transition_matrix.html"];
kernel_density_estimate [URL="kernel_density_estimate.html"];
lnsrch [URL="lnsrch.html"];
lnsrch1 [URL="lnsrch1.html"];
lpdfbeta [URL="lpdfbeta.html"];
lpdfgam [URL="lpdfgam.html"];
lpdfgbeta [URL="lpdfgbeta.html"];
lpdfig1 [URL="lpdfig1.html"];
lpdfig2 [URL="lpdfig2.html"];
lpdfnorm [URL="lpdfnorm.html"];
lptauSEQ [URL="lptauSEQ.html"];
lyapunov_symm [URL="lyapunov_symm.html"];
make_ex_ [URL="make_ex_.html"];
make_y_ [URL="make_y_.html"];
marginal_density [URL="marginal_density.html"];
matrictint [URL="matrictint.html"];
mcompare [URL="mcompare.html"];
metropolis [URL="metropolis.html"];
metropolis_draw [URL="metropolis_draw.html"];
mgnldnsty [URL="mgnldnsty.html"];
mh_optimal_bandwidth [URL="mh_optimal_bandwidth.html"];
mj_qgamma [URL="mj_qgamma.html"];
mode_check [URL="mode_check.html"];
mr_gstep [URL="mr_gstep.html"];
mr_hessian [URL="mr_hessian.html"];
my_ordeig [URL="my_ordeig.html"];
my_subplot [URL="my_subplot.html"];
newrat [URL="newrat.html"];
numgrad [URL="numgrad.html"];
olr [URL="olr.html"];
olr1 [URL="olr1.html"];
olr2 [URL="olr2.html"];
osr [URL="osr.html"];
osr1 [URL="osr1.html"];
osr_obj [URL="osr_obj.html"];
p2toperc [URL="p2toperc.html"];
pgamma [URL="pgamma.html"];
plot_priors [URL="plot_priors.html"];
pltorg [URL="pltorg.html"];
pnorm [URL="pnorm.html"];
posterior_density_estimate [URL="posterior_density_estimate.html"];
posterior_distribution [URL="posterior_distribution.html"];
posterior_moments [URL="posterior_moments.html"];
print_info [URL="print_info.html"];
prior_bounds [URL="prior_bounds.html"];
priordens [URL="priordens.html"];
qbeta [URL="qbeta.html"];
qchisq [URL="qchisq.html"];
qgamma [URL="qgamma.html"];
qnorm [URL="qnorm.html"];
qzdiv [URL="qzdiv.html"];
qzswitch [URL="qzswitch.html"];
read_variables [URL="read_variables.html"];
recursive_moments [URL="recursive_moments.html"];
resid [URL="resid.html"];
resol [URL="resol.html"];
resol1 [URL="resol1.html"];
rfvar3 [URL="rfvar3.html"];
rndprior [URL="rndprior.html"];
rows [URL="rows.html"];
rplot [URL="rplot.html"];
sec2hms [URL="sec2hms.html"];
selif [URL="selif.html"];
set_all_parameters [URL="set_all_parameters.html"];
set_default_option [URL="set_default_option.html"];
set_parameters [URL="set_parameters.html"];
set_prior [URL="set_prior.html"];
set_shocks [URL="set_shocks.html"];
set_state_space [URL="set_state_space.html"];
sim1 [URL="sim1.html"];
simk [URL="simk.html"];
simul [URL="simul.html"];
simult [URL="simult.html"];
simult_ [URL="simult_.html"];
smirnov [URL="smirnov.html"];
solve1 [URL="solve1.html"];
stab_map_ [URL="stab_map_.html"];
stab_map_1 [URL="stab_map_1.html"];
stab_map_2 [URL="stab_map_2.html"];
stab_map_marginal [URL="stab_map_marginal.html"];
steady [URL="steady.html"];
steady_ [URL="steady_.html"];
stoch_simul [URL="stoch_simul.html"];
sylvester3 [URL="sylvester3.html"];
sylvester3a [URL="sylvester3a.html"];
table [URL="table.html"];
th_autocovariances [URL="th_autocovariances.html"];
transition_matrix [URL="transition_matrix.html"];
uniform_specification [URL="uniform_specification.html"];
union [URL="union.html"];
varprior [URL="varprior.html"];
}

213
matlab/doc/graph.html
View File

@ -1,213 +0,0 @@
<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"
"http://www.w3.org/TR/REC-html40/loose.dtd">
<html>
<head>
<title>Dependency Graph for .</title>
<meta name="keywords" content="dependency, graph, dependence, .">
<meta name="description" content="Dependency graph for .">
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<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png">&nbsp;Master index</a></td>
<td align="right"><a href="index.html">Index for .&nbsp;<img alt=">" border="0" src="../right.png"></a></td></tr></table>
<h1>Dependency Graph for .</h1>
<center>
<img src="graph.png" usemap="#mainmap" alt="Dependency Graph for ." border="2" style="color:#000;">
<map name="mainmap">
<area shape="rect" href="CutSample.html" title="CutSample" alt="" coords="2892,198,3009,246">
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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"
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<title>Description of hessian</title>
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<meta name="description" content="Copyright (C) 2001 Michel Juillard">
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<h1>hessian
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>Copyright (C) 2001 Michel Juillard</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function hessian_mat = hessian(func,x,varargin) </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> Copyright (C) 2001 Michel Juillard
% computes second order partial derivatives
uses Abramowitz and Stegun (1965) formulas 25.3.24 and 25.3.27 p. 884</pre></div>
<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="dr1.html" class="code" title="function [dr,info]=dr1(dr,task)">dr1</a> Copyright (C) 2001 Michel Juillard</li><li><a href="dynare_estimation.html" class="code" title="function dynare_estimation(var_list_)">dynare_estimation</a> </li><li><a href="mode_check.html" class="code" title="function mode_check(x,fval,hessian,gend,data,lb,ub)">mode_check</a> </li></ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <span class="comment">% Copyright (C) 2001 Michel Juillard</span>
0002 <span class="comment">%% computes second order partial derivatives</span>
0003 <span class="comment">% uses Abramowitz and Stegun (1965) formulas 25.3.24 and 25.3.27 p. 884</span>
0004
0005 <a name="_sub0" href="#_subfunctions" class="code">function hessian_mat = hessian(func,x,varargin)</a>
0006 <span class="keyword">global</span> options_
0007 func = str2func(func);
0008 n=size(x,1);
0009 <span class="comment">%h1=max(abs(x),options_.gstep*ones(n,1))*eps^(1/3);</span>
0010 h1=max(abs(x),sqrt(options_.gstep)*ones(n,1))*eps^(1/6);
0011 h_1=h1;
0012 xh1=x+h1;
0013 h1=xh1-x;
0014 xh1=x-h_1;
0015 h_1=x-xh1;
0016 xh1=x;
0017 f0=feval(func,x,varargin{:});
0018 f1=zeros(size(f0,1),n);
0019 f_1=f1;
0020 <span class="keyword">for</span> i=1:n
0021 xh1(i)=x(i)+h1(i);
0022 f1(:,i)=feval(func,xh1,varargin{:});
0023 xh1(i)=x(i)-h_1(i);
0024 f_1(:,i)=feval(func,xh1,varargin{:});
0025 xh1(i)=x(i);
0026 i=i+1;
0027 <span class="keyword">end</span>
0028 xh_1=xh1;
0029 hessian_mat = zeros(size(f0,1),n*n);
0030 <span class="keyword">for</span> i=1:n
0031 <span class="keyword">if</span> i &gt; 1
0032 k=[i:n:n*(i-1)];
0033 hessian_mat(:,(i-1)*n+1:(i-1)*n+i-1)=hessian_mat(:,k);
0034 <span class="keyword">end</span>
0035 hessian_mat(:,(i-1)*n+i)=(f1(:,i)+f_1(:,i)-2*f0)./(h1(i)*h_1(i));
0036 temp=f1+f_1-f0*ones(1,n);
0037 <span class="keyword">for</span> j=i+1:n
0038 xh1(i)=x(i)+h1(i);
0039 xh1(j)=x(j)+h_1(j);
0040 xh_1(i)=x(i)-h1(i);
0041 xh_1(j)=x(j)-h_1(j);
0042 hessian_mat(:,(i-1)*n+j)=-(-feval(func,xh1,varargin{:})-feval(func,xh_1,varargin{:})+temp(:,i)+temp(:,j))./(2*h1(i)*h_1(j));
0043 xh1(i)=x(i);
0044 xh1(j)=x(j);
0045 xh_1(i)=x(i);
0046 xh_1(j)=x(j);
0047 j=j+1;
0048 <span class="keyword">end</span>
0049 i=i+1;
0050 <span class="keyword">end</span>
0051 <span class="comment">% 11/25/03 SA Created from Hessian_sparse (removed sparse)</span></pre></div>
<hr><address>Generated on Fri 16-Jun-2006 09:09:06 by <strong><a href="http://www.artefact.tk/software/matlab/m2html/">m2html</a></strong> &copy; 2003</address>
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<meta name="keywords" content="hessian_sparse">
<meta name="description" content="Copyright (C) 2001 Michel Juillard%% computes second order partial derivatives% uses Abramowitz and Stegun (1965) formulas 25.3.24 and 25.3.27 p. 884function hessian_mat = hessian_sparse(func,x,varargin)global options_func = str2func(func);n=size(x,1);%h1=max(abs(x),options_.gstep*ones(n,1))*eps^(1/3);h1=max(abs(x),sqrt(options_.gstep)*ones(n,1))*eps^(1/6);h_1=h1;xh1=x+h1;h1=xh1-x;xh1=x-h_1;h_1=x-xh1;xh1=x;f0=feval(func,x,varargin{:});nf = size(f0,1);f1=zeros(nf,n);f_1=f1;for i=1:n xh1(i)=x(i)+h1(i); f1(:,i)=feval(func,xh1,varargin{:}); xh1(i)=x(i)-h_1(i); f_1(:,i)=feval(func,xh1,varargin{:}); xh1(i)=x(i); i=i+1;endxh_1=xh1;hessian_mat = spalloc(nf,n*n,3*nf*n);for i=1:n if i > 1 k=[i:n:n*(i-1)]; hessian_mat(:,(i-1)*n+1:(i-1)*n+i-1)=hessian_mat(:,k); end hessian_mat(:,(i-1)*n+i)=sparse((f1(:,i)+f_1(:,i)-2*f0)./(h1(i)*h_1(i))); temp=f1+f_1-f0*ones(1,n); for j=i+1:n xh1(i)=x(i)+h1(i); xh1(j)=x(j)+h_1(j); xh_1(i)=x(i)-h1(i); xh_1(j)=x(j)-h_1(j); hessian_mat(:,(i-1)*n+j)=sparse(-(-feval(func,xh1,varargin{:})-feval(func,xh_1,varargin{:})+temp(:,i)+temp(:,j))./(2*h1(i)*h_1(j))); xh1(i)=x(i); xh1(j)=x(j); xh_1(i)=x(i); xh_1(j)=x(j); j=j+1; end i=i+1;end% 10/03/02 MJ used the 7 points formula">
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<h1>hessian_sparse
</h1>
<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>Copyright (C) 2001 Michel Juillard%% computes second order partial derivatives% uses Abramowitz and Stegun (1965) formulas 25.3.24 and 25.3.27 p. 884function hessian_mat = hessian_sparse(func,x,varargin)global options_func = str2func(func);n=size(x,1);%h1=max(abs(x),options_.gstep*ones(n,1))*eps^(1/3);h1=max(abs(x),sqrt(options_.gstep)*ones(n,1))*eps^(1/6);h_1=h1;xh1=x+h1;h1=xh1-x;xh1=x-h_1;h_1=x-xh1;xh1=x;f0=feval(func,x,varargin{:});nf = size(f0,1);f1=zeros(nf,n);f_1=f1;for i=1:n xh1(i)=x(i)+h1(i); f1(:,i)=feval(func,xh1,varargin{:}); xh1(i)=x(i)-h_1(i); f_1(:,i)=feval(func,xh1,varargin{:}); xh1(i)=x(i); i=i+1;endxh_1=xh1;hessian_mat = spalloc(nf,n*n,3*nf*n);for i=1:n if i > 1 k=[i:n:n*(i-1)]; hessian_mat(:,(i-1)*n+1:(i-1)*n+i-1)=hessian_mat(:,k); end hessian_mat(:,(i-1)*n+i)=sparse((f1(:,i)+f_1(:,i)-2*f0)./(h1(i)*h_1(i))); temp=f1+f_1-f0*ones(1,n); for j=i+1:n xh1(i)=x(i)+h1(i); xh1(j)=x(j)+h_1(j); xh_1(i)=x(i)-h1(i); xh_1(j)=x(j)-h_1(j); hessian_mat(:,(i-1)*n+j)=sparse(-(-feval(func,xh1,varargin{:})-feval(func,xh_1,varargin{:})+temp(:,i)+temp(:,j))./(2*h1(i)*h_1(j))); xh1(i)=x(i); xh1(j)=x(j); xh_1(i)=x(i); xh_1(j)=x(j); j=j+1; end i=i+1;end% 10/03/02 MJ used the 7 points formula</strong></div>
<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>This is a script file. </strong></div>
<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> Copyright (C) 2001 Michel Juillard%% computes second order partial derivatives% uses Abramowitz and Stegun (1965) formulas 25.3.24 and 25.3.27 p. 884function hessian_mat = hessian_sparse(func,x,varargin)global options_func = str2func(func);n=size(x,1);%h1=max(abs(x),options_.gstep*ones(n,1))*eps^(1/3);h1=max(abs(x),sqrt(options_.gstep)*ones(n,1))*eps^(1/6);h_1=h1;xh1=x+h1;h1=xh1-x;xh1=x-h_1;h_1=x-xh1;xh1=x;f0=feval(func,x,varargin{:});nf = size(f0,1);f1=zeros(nf,n);f_1=f1;for i=1:n xh1(i)=x(i)+h1(i); f1(:,i)=feval(func,xh1,varargin{:}); xh1(i)=x(i)-h_1(i); f_1(:,i)=feval(func,xh1,varargin{:}); xh1(i)=x(i); i=i+1;endxh_1=xh1;hessian_mat = spalloc(nf,n*n,3*nf*n);for i=1:n if i &gt; 1 k=[i:n:n*(i-1)]; hessian_mat(:,(i-1)*n+1:(i-1)*n+i-1)=hessian_mat(:,k); end hessian_mat(:,(i-1)*n+i)=sparse((f1(:,i)+f_1(:,i)-2*f0)./(h1(i)*h_1(i))); temp=f1+f_1-f0*ones(1,n); for j=i+1:n xh1(i)=x(i)+h1(i); xh1(j)=x(j)+h_1(j); xh_1(i)=x(i)-h1(i); xh_1(j)=x(j)-h_1(j); hessian_mat(:,(i-1)*n+j)=sparse(-(-feval(func,xh1,varargin{:})-feval(func,xh_1,varargin{:})+temp(:,i)+temp(:,j))./(2*h1(i)*h_1(j))); xh1(i)=x(i); xh1(j)=x(j); xh_1(i)=x(i); xh_1(j)=x(j); j=j+1; end i=i+1;end% 10/03/02 MJ used the 7 points formula</pre></div>
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<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
<!-- crossreference -->
<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre>0001 <span class="comment">% Copyright (C) 2001 Michel Juillard%% computes second order partial derivatives% uses Abramowitz and Stegun (1965) formulas 25.3.24 and 25.3.27 p. 884function hessian_mat = hessian_sparse(func,x,varargin)global options_func = str2func(func);n=size(x,1);%h1=max(abs(x),options_.gstep*ones(n,1))*eps^(1/3);h1=max(abs(x),sqrt(options_.gstep)*ones(n,1))*eps^(1/6);h_1=h1;xh1=x+h1;h1=xh1-x;xh1=x-h_1;h_1=x-xh1;xh1=x;f0=feval(func,x,varargin{:});nf = size(f0,1);f1=zeros(nf,n);f_1=f1;for i=1:n xh1(i)=x(i)+h1(i); f1(:,i)=feval(func,xh1,varargin{:}); xh1(i)=x(i)-h_1(i); f_1(:,i)=feval(func,xh1,varargin{:}); xh1(i)=x(i); i=i+1;endxh_1=xh1;hessian_mat = spalloc(nf,n*n,3*nf*n);for i=1:n if i &gt; 1 k=[i:n:n*(i-1)]; hessian_mat(:,(i-1)*n+1:(i-1)*n+i-1)=hessian_mat(:,k); end hessian_mat(:,(i-1)*n+i)=sparse((f1(:,i)+f_1(:,i)-2*f0)./(h1(i)*h_1(i))); temp=f1+f_1-f0*ones(1,n); for j=i+1:n xh1(i)=x(i)+h1(i); xh1(j)=x(j)+h_1(j); xh_1(i)=x(i)-h1(i); xh_1(j)=x(j)-h_1(j); hessian_mat(:,(i-1)*n+j)=sparse(-(-feval(func,xh1,varargin{:})-feval(func,xh_1,varargin{:})+temp(:,i)+temp(:,j))./(2*h1(i)*h_1(j))); xh1(i)=x(i); xh1(j)=x(j); xh_1(i)=x(i); xh_1(j)=x(j); j=j+1; end i=i+1;end% 10/03/02 MJ used the 7 points formula</span></pre></div>
<hr><address>Generated on Fri 16-Jun-2006 09:09:06 by <strong><a href="http://www.artefact.tk/software/matlab/m2html/">m2html</a></strong> &copy; 2003</address>
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