121 lines
7.9 KiB
HTML
121 lines
7.9 KiB
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<title>Description of varprior</title>
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<div><a href="../index.html">Home</a> > <a href="index.html">.</a> > varprior.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>varprior
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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>function [ydum,xdum,breaks]=varprior(nv,nx,lags,mnprior,vprior)</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 [ydum,xdum,breaks]=varprior(nv,nx,lags,mnprior,vprior) </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">function [ydum,xdum,breaks]=varprior(nv,nx,lags,mnprior,vprior)
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ydum, xdum: dummy observation data that implement the prior
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breaks: vector of points in the dummy data after which new dummy obs's start
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Set breaks=T+[0;breaks], ydata=[ydata;ydum], xdum=[xdata;xdum], where
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actual data matrix has T rows, in preparing input for rfvar3
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nv,nx,lags: VAR dimensions
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mnprior.tight:Overall tightness of Minnesota prior
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mnprior.decay:Standard deviations of lags shrink as lag^(-decay)
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vprior.sig: Vector of prior modes for diagonal elements of r.f. covariance matrix
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vprior.w: Weight on prior on vcv. 1 corresponds to "one dummy observation" weight
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Should be an integer, and will be rounded if not. vprior.sig is needed
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to scale the Minnesota prior, even if the prior on sigma is not used itself.
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Set vprior.w=0 to achieve this.
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Note: The original Minnesota prior treats own lags asymmetrically, and therefore
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cannot be implemented entirely with dummy observations. It is also usually
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taken to include the sum-of-coefficients and co-persistence components
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that are implemented directly in rfvar3.m. The diagonal prior on v, combined
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with sum-of-coefficients and co-persistence components and with the unit own-first-lag
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prior mean generates larger prior variances for own than for cross-effects even in
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this formulation, but here there is no way to shrink toward a set of unconstrained
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univariate AR's.
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-----------------------</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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<li><a href="mgnldnsty.html" class="code" title="function w=mgnldnsty(ydata,lags,xdata,breaks,lambda,mu,mnprior,vprior,train,flat)">mgnldnsty</a> function w=mgnldnsty(ydata,lags,xdata,breaks,lambda,mu,mnprior,vprior,train,flat)</li></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 [ydum,xdum,breaks]=varprior(nv,nx,lags,mnprior,vprior)</a>
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0002 <span class="comment">%function [ydum,xdum,breaks]=varprior(nv,nx,lags,mnprior,vprior)</span>
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0003 <span class="comment">% ydum, xdum: dummy observation data that implement the prior</span>
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0004 <span class="comment">% breaks: vector of points in the dummy data after which new dummy obs's start</span>
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0005 <span class="comment">% Set breaks=T+[0;breaks], ydata=[ydata;ydum], xdum=[xdata;xdum], where</span>
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0006 <span class="comment">% actual data matrix has T rows, in preparing input for rfvar3</span>
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0007 <span class="comment">% nv,nx,lags: VAR dimensions</span>
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0008 <span class="comment">% mnprior.tight:Overall tightness of Minnesota prior</span>
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0009 <span class="comment">% mnprior.decay:Standard deviations of lags shrink as lag^(-decay)</span>
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0010 <span class="comment">% vprior.sig: Vector of prior modes for diagonal elements of r.f. covariance matrix</span>
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0011 <span class="comment">% vprior.w: Weight on prior on vcv. 1 corresponds to "one dummy observation" weight</span>
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0012 <span class="comment">% Should be an integer, and will be rounded if not. vprior.sig is needed</span>
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0013 <span class="comment">% to scale the Minnesota prior, even if the prior on sigma is not used itself.</span>
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0014 <span class="comment">% Set vprior.w=0 to achieve this.</span>
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0015 <span class="comment">% Note: The original Minnesota prior treats own lags asymmetrically, and therefore</span>
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0016 <span class="comment">% cannot be implemented entirely with dummy observations. It is also usually</span>
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0017 <span class="comment">% taken to include the sum-of-coefficients and co-persistence components</span>
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0018 <span class="comment">% that are implemented directly in rfvar3.m. The diagonal prior on v, combined</span>
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0019 <span class="comment">% with sum-of-coefficients and co-persistence components and with the unit own-first-lag</span>
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0020 <span class="comment">% prior mean generates larger prior variances for own than for cross-effects even in</span>
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0021 <span class="comment">% this formulation, but here there is no way to shrink toward a set of unconstrained</span>
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0022 <span class="comment">% univariate AR's.</span>
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0023 <span class="comment">%-----------------------</span>
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0024 <span class="comment">%</span>
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0025 <span class="keyword">if</span> ~isempty(mnprior)
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0026 xdum=zeros(lags+1,nx,lags,nv);
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0027 ydum=zeros(lags+1,nv,lags,nv);
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0028 <span class="keyword">for</span> il=1:lags
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0029 ydum(il+1,:,il,:)=il^mnprior.decay*diag(vprior.sig);
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0030 <span class="keyword">end</span>
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0031 ydum(1,:,1,:)=diag(vprior.sig);
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0032 ydum=mnprior.tight*reshape(ydum,[lags+1,nv,lags*nv]);
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0033 ydum=flipdim(ydum,1);
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0034 xdum=mnprior.tight*reshape(xdum,[lags+1,nx,lags*nv]);
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0035 xdum=flipdim(xdum,1);
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0036 breaks=(lags+1)*[1:(nv*lags)]';
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0037 lbreak=breaks(end);
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0038 <span class="keyword">else</span>
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0039 ydum=[];
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0040 xdum=[];
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0041 breaks=[];
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0042 lbreak=0;
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0043 <span class="keyword">end</span>
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0044 <span class="keyword">if</span> ~isempty(vprior) & vprior.w>0
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0045 ydum2=zeros(lags+1,nv,nv);
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0046 xdum2=zeros(lags+1,nx,nv);
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0047 ydum2(<span class="keyword">end</span>,:,:)=diag(vprior.sig);
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0048 ydum=cat(3,ydum,ydum2);
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0049 xdum=cat(3,xdum,xdum2);
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0050 dimy=size(ydum);
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0051 ydum=reshape(permute(ydum,[1 3 2]),dimy(1)*dimy(3),nv);
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0052 xdum=reshape(permute(xdum,[1 3 2]),dimy(1)*dimy(3),nx);
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0053 breaks=[breaks;(lags+1)*[1:nv-1]'+lbreak];
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0054 <span class="keyword">end</span></pre></div>
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