140 lines
7.0 KiB
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140 lines
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<title>Description of DiffuseLikelihood1</title>
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<div><a href="../index.html">Home</a> > <a href="index.html">.</a> > DiffuseLikelihood1.m</div>
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<!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png"> Master index</a></td>
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<td align="right"><a href="index.html">Index for . <img alt=">" border="0" src="../right.png"></a></td></tr></table>-->
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<h1>DiffuseLikelihood1
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</h1>
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<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
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<div class="box"><strong>M. Ratto added lik in output</strong></div>
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<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
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<div class="box"><strong>function [LIK, lik] = DiffuseLikelihood1(T,R,Q,Pinf,Pstar,Y,trend,start) </strong></div>
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<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
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<div class="fragment"><pre class="comment"> M. Ratto added lik in output
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stephane.adjemian@cepremap.cnrs.fr [07-19-2004]
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Same as DiffuseLikelihoodH1 without measurement error.</pre></div>
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<!-- crossreference -->
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<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
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This function calls:
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<ul style="list-style-image:url(../matlabicon.gif)">
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</ul>
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This function is called by:
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<ul style="list-style-image:url(../matlabicon.gif)">
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</ul>
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<!-- crossreference -->
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<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
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<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>
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0002 <span class="comment">% M. Ratto added lik in output</span>
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0003 <span class="comment">% stephane.adjemian@cepremap.cnrs.fr [07-19-2004]</span>
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0004 <span class="comment">%</span>
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0005 <span class="comment">% Same as DiffuseLikelihoodH1 without measurement error.</span>
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0006 <span class="keyword">global</span> bayestopt_ options_
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0007
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0008 mf = bayestopt_.mf;
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0009 smpl = size(Y,2);
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0010 mm = size(T,2);
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0011 pp = size(Y,1);
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0012 a = zeros(mm,1);
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0013 dF = 1;
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0014 QQ = R*Q*transpose(R);
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0015 t = 0;
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0016 lik = zeros(smpl+1,1);
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0017 LIK = Inf;
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0018 lik(smpl+1) = smpl*pp*log(2*pi);
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0019 notsteady = 1;
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0020 crit = options_.kalman_tol;
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0021 reste = 0;
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0022 <span class="keyword">while</span> rank(Pinf,crit) & t < smpl
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0023 t = t+1;
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0024 v = Y(:,t)-a(mf)-trend(:,t);
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0025 Finf = Pinf(mf,mf);
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0026 <span class="keyword">if</span> rcond(Finf) < crit
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0027 <span class="keyword">if</span> ~all(abs(Finf(:)) < crit)
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0028 <span class="keyword">return</span>
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0029 <span class="keyword">else</span>
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0030 iFstar = inv(Pstar(mf,mf));
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0031 dFstar = det(Pstar(mf,mf));
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0032 Kstar = Pstar(:,mf)*iFstar;
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0033 lik(t) = log(dFstar) + transpose(v)*iFstar*v;
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0034 Pinf = T*Pinf*transpose(T);
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0035 Pstar = T*(Pstar-Pstar(:,mf)*transpose(Kstar))*transpose(T)+QQ;
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0036 a = T*(a+Kstar*v);
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0037 <span class="keyword">end</span>
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0038 <span class="keyword">else</span>
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0039 lik(t) = log(det(Finf));
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0040 iFinf = inv(Finf);
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0041 Kinf = Pinf(:,mf)*iFinf; <span class="comment">%% premultiplication by the transition matrix T is removed (stephane)</span>
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0042 Fstar = Pstar(mf,mf);
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0043 Kstar = (Pstar(:,mf)-Kinf*Fstar)*iFinf; <span class="comment">%% premultiplication by the transition matrix T is removed (stephane)</span>
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0044 Pstar = T*(Pstar-Pstar(:,mf)*transpose(Kinf)-Pinf(:,mf)*transpose(Kstar))*transpose(T)+QQ;
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0045 Pinf = T*(Pinf-Pinf(:,mf)*transpose(Kinf))*transpose(T);
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0046 a = T*(a+Kinf*v);
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0047 <span class="keyword">end</span>
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0048 <span class="keyword">end</span>
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0049 <span class="keyword">if</span> t == smpl
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0050 error([<span class="string">'There isn''t enough information to estimate the initial'</span> <span class="keyword">...</span><span class="comment"> </span>
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0051 <span class="string">' conditions of the nonstationary variables'</span>]);
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0052 <span class="keyword">end</span>
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0053 F_singular = 1;
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0054 <span class="keyword">while</span> notsteady & t < smpl
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0055 t = t+1;
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0056 v = Y(:,t)-a(mf)-trend(:,t);
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0057 F = Pstar(mf,mf);
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0058 oldPstar = Pstar;
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0059 dF = det(F);
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0060 <span class="keyword">if</span> rcond(F) < crit
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0061 <span class="keyword">if</span> ~all(abs(F(:))<crit)
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0062 <span class="keyword">return</span>
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0063 <span class="keyword">else</span>
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0064 a = T*a;
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0065 Pstar = T*Pstar*transpose(T)+QQ;
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0066 <span class="keyword">end</span>
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0067 <span class="keyword">else</span>
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0068 F_singular = 0;
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0069 iF = inv(F);
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0070 lik(t) = log(dF)+transpose(v)*iF*v;
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0071 K = Pstar(:,mf)*iF; <span class="comment">%% premultiplication by the transition matrix T is removed (stephane)</span>
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0072 a = T*(a+K*v); <span class="comment">%% --> factorization of the transition matrix...</span>
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0073 Pstar = T*(Pstar-K*Pstar(mf,:))*transpose(T)+QQ; <span class="comment">%% ... idem (stephane)</span>
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0074 <span class="keyword">end</span>
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0075 notsteady = ~(max(max(abs(Pstar-oldPstar)))<crit);
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0076 <span class="keyword">end</span>
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0077 <span class="keyword">if</span> F_singular == 1
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0078 error([<span class="string">'The variance of the forecast error remains singular until the'</span> <span class="keyword">...</span>
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0079 <span class="string">'end of the sample'</span>])
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0080 <span class="keyword">end</span>
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0081 reste = smpl-t;
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0082 <span class="keyword">while</span> t < smpl
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0083 t = t+1;
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0084 v = Y(:,t)-a(mf)-trend(:,t);
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0085 a = T*(a+K*v);
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0086 lik(t) = transpose(v)*iF*v;
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0087 <span class="keyword">end</span>
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0088 lik(t) = lik(t) + reste*log(dF);
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0089
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0090
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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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<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> © 2003</address>
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