69 lines
1.6 KiB
Scilab
69 lines
1.6 KiB
Scilab
function [Gamma_y]=th_autocovariances(dr,ivar)
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Gamma_y=[];
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// Copyright (C) 2001 Michel Juillard
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//
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// computes the theoretical auto-covariances, Gamma_y, for an AR(p) process
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// with coefficients dr.ghx and dr.ghu and shock variances Sigma_e_
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// for a subset of variables ivar (indices in lgy_)
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//
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global('lgy_','endo_nbr','exo_nbr','Sigma_e_','ykmin_');
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nvar = size(ivar,1);
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ghx = dr('ghx');
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ghu = dr('ghu');
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npred = dr('npred');
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nstatic = dr('nstatic');
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kstate = dr('kstate');
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order = dr('order_var');
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iv(1,order) = 1:endo_nbr;
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nx = size(ghx,2);
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ikx = nstatic+1:nstatic+npred;
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A = zeros(nx,nx);
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A(1:npred,:) = ghx(ikx,:);
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offset_r = npred;
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offset_c = 0;
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i0 = find(kstate(:,2)==(ykmin_+1))';
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n0 = size(i0,1);
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for i = ykmin_:-1:2
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i1 = find(kstate(:,2)==i)';
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n1 = size(i1,1);
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j = zeros(n1,1);
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for j1 = 1:n1
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%v2 = find(kstate(i0,1)==kstate(i1(j1),1))'
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j(j1,1) = %v2(:);
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end
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A(offset_r+1:offset_r+n1,offset_c+j) = eye(n1,n1);
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offset_r = offset_r+n1;
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offset_c = offset_c+n0;
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i0 = i1;
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n0 = n1;
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end
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kron_A = A.*.A;
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ghu1 = [ghu(ikx,:);zeros(nx-npred,exo_nbr)];
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kron_ghu = ghu1.*.ghu1;
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// vx such that vec(sigma_y) = vx * vec(Sigma_e_) (predetermined vars)
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vx = (eye(nx*nx,nx*nx)-kron_A)\kron_ghu;
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kron_ghx = [];
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kron_ghu = [];
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// order of variables with preset variances in ghx and ghu
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//! mtlb_e(iv,ivar) may be replaced by iv(ivar)
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//! iv(ivar) if iv is a vector,
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//! iv(ivar).' if iv is a matrix.
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iky = mtlb_e(iv,ivar);
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kron_ghx = ghx(iky,:).*.ghx(iky,:);
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kron_ghu = ghu(iky,:).*.ghu(iky,:);
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Gamma_y = matrix((kron_ghx*vx+kron_ghu)*Sigma_e_(:),nvar,nvar);
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// 10/18/2002 MJ
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