42 lines
1.9 KiB
Matlab
42 lines
1.9 KiB
Matlab
function [Fmat,gvec] = fn_gfmean(b,P,Vi,nvar,ncoef,n0,np)
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% [Fmat,gvec] = fn_gfmean(b,P,Vi,nvar,ncoef,n0,np)
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%
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% Mean of free lagged parameters g and original lagged parameters F, conditional on comtemporaneous b's
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% See Waggoner and Zha's Gibbs sampling
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%
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% b: sum(n0)-element vector of mean estimate of A0 free parameters
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% P: cell(nvar,1). In each cell, the transformation matrix that affects the posterior mean of A+ conditional on A0.
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% Vi: nvar-by-1 cell. In each cell, k-by-ri orthonormal basis for the null of the ith
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% equation lagged restriction matrix where k is a total of exogenous variables and
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% ri is the number of free parameters. With this transformation, we have fi = Vi*gi
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% or Vi'*fi = gi where fi is a vector of total original parameters and gi is a
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% vector of free parameters. There must be at least one free parameter left for
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% the ith equation.
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% nvar: number of endogeous variables
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% ncoef: number of original lagged variables per equation
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% n0: nvar-element vector, ith element represents the number of free A0 parameters in ith equation
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% np: nvar-element vector, ith element represents the number of free A+ parameters in ith equation
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%---------------
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% Fmat: ncoef-by-nvar matrix of original lagged parameters A+. Column corresponding to equation.
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% gvec: sum(np)-by-1 stacked vector of all free lagged parameters A+.
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%
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% Tao Zha, February 2000. Revised, August 2000.
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b=b(:); n0=n0(:); np=np(:);
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n0cum = [0;cumsum(n0)];
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npcum = [0;cumsum(np)];
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gvec = zeros(npcum(end),1);
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Fmat = zeros(ncoef,nvar); % ncoef: maximum original lagged parameters per equation
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if ~(length(b)==n0cum(end))
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error('Make inputs n0 and length(b) match exactly')
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end
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for kj=1:nvar
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bj = b(n0cum(kj)+1:n0cum(kj+1));
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gj = P{kj}*bj;
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gvec(npcum(kj)+1:npcum(kj+1)) = gj;
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Fmat(:,kj) = Vi{kj}*gj;
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end
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