From 491301c67ba3fa782ed88c2db4f30be4920509df Mon Sep 17 00:00:00 2001
From: Michel Juillard <michel.juillard@mjui.fr>
Date: Sun, 2 Nov 2014 11:17:27 +0100
Subject: [PATCH] th_autocovariances.m creates Gamma_y{nar+2} (variance
decomposition) even if there is a single shock. In that case variance
decomposition is one. It is better if Gamma_y has always the expected size.
disp_th_moments.m only displays variance decomposition if there is more than
one shock.
---
matlab/th_autocovariances.m | 124 +++++++++++++++++++-----------------
1 file changed, 66 insertions(+), 58 deletions(-)
diff --git a/matlab/th_autocovariances.m b/matlab/th_autocovariances.m
index 63780e3b54..39967de93d 100644
--- a/matlab/th_autocovariances.m
+++ b/matlab/th_autocovariances.m
@@ -177,30 +177,34 @@ if options_.hp_filter == 0
end
end
% variance decomposition
- if ~nodecomposition && M_.exo_nbr > 1 && size(stationary_vars, 1) > 0
- Gamma_y{nar+2} = zeros(nvar,M_.exo_nbr);
- SS(exo_names_orig_ord,exo_names_orig_ord)=M_.Sigma_e+1e-14*eye(M_.exo_nbr);
- cs = chol(SS)';
- b1(:,exo_names_orig_ord) = ghu1;
- b1 = b1*cs;
- b2(:,exo_names_orig_ord) = ghu(iky,:);
- b2 = b2*cs;
- vx = lyapunov_symm(A,b1*b1',options_.qz_criterium,options_.lyapunov_complex_threshold,1,[],options_.debug);
- vv = diag(aa*vx*aa'+b2*b2');
- vv2 = 0;
- for i=1:M_.exo_nbr
- vx1 = lyapunov_symm(A,b1(:,i)*b1(:,i)',options_.qz_criterium,options_.lyapunov_complex_threshold,2,[],options_.debug);
- vx2 = abs(diag(aa*vx1*aa'+b2(:,i)*b2(:,i)'));
- Gamma_y{nar+2}(stationary_vars,i) = vx2;
- vv2 = vv2 +vx2;
- end
- if max(abs(vv2-vv)./vv) > 1e-4
- warning(['Aggregate variance and sum of variances by shocks ' ...
- 'differ by more than 0.01 %'])
- end
- for i=1:M_.exo_nbr
- Gamma_y{nar+2}(stationary_vars,i) = Gamma_y{nar+ ...
- 2}(stationary_vars,i)./vv2;
+ if ~nodecomposition && M_.exo_nbr > 0 && size(stationary_vars, 1) > 0
+ if M_.exo_nbr == 1
+ Gamma_y{nar+2} = ones(nvar,1);
+ else
+ Gamma_y{nar+2} = zeros(nvar,M_.exo_nbr);
+ SS(exo_names_orig_ord,exo_names_orig_ord)=M_.Sigma_e+1e-14*eye(M_.exo_nbr);
+ cs = chol(SS)';
+ b1(:,exo_names_orig_ord) = ghu1;
+ b1 = b1*cs;
+ b2(:,exo_names_orig_ord) = ghu(iky,:);
+ b2 = b2*cs;
+ vx = lyapunov_symm(A,b1*b1',options_.qz_criterium,options_.lyapunov_complex_threshold,1,[],options_.debug);
+ vv = diag(aa*vx*aa'+b2*b2');
+ vv2 = 0;
+ for i=1:M_.exo_nbr
+ vx1 = lyapunov_symm(A,b1(:,i)*b1(:,i)',options_.qz_criterium,options_.lyapunov_complex_threshold,2,[],options_.debug);
+ vx2 = abs(diag(aa*vx1*aa'+b2(:,i)*b2(:,i)'));
+ Gamma_y{nar+2}(stationary_vars,i) = vx2;
+ vv2 = vv2 +vx2;
+ end
+ if max(abs(vv2-vv)./vv) > 1e-4
+ warning(['Aggregate variance and sum of variances by shocks ' ...
+ 'differ by more than 0.01 %'])
+ end
+ for i=1:M_.exo_nbr
+ Gamma_y{nar+2}(stationary_vars,i) = Gamma_y{nar+ ...
+ 2}(stationary_vars,i)./vv2;
+ end
end
end
else% ==> Theoretical HP filter.
@@ -224,8 +228,8 @@ else% ==> Theoretical HP filter.
f_hp = zeros(length(ivar),length(ivar));
else
f_omega =(1/(2*pi))*( [inv(IA-A*tneg(ig))*ghu1;IE]...
- *M_.Sigma_e*[ghu1'*inv(IA-A'*tpos(ig)) ...
- IE]); % state variables
+ *M_.Sigma_e*[ghu1'*inv(IA-A'*tpos(ig)) ...
+ IE]); % state variables
g_omega = [aa*tneg(ig) bb]*f_omega*[aa'*tpos(ig); bb']; % selected variables
f_hp = hp1(ig)^2*g_omega; % spectral density of selected filtered series
end
@@ -244,49 +248,53 @@ else% ==> Theoretical HP filter.
end
end
% Variance decomposition
- if ~nodecomposition && M_.exo_nbr > 1
- Gamma_y{nar+2} = zeros(nvar,M_.exo_nbr);
- SS(exo_names_orig_ord,exo_names_orig_ord) = M_.Sigma_e+1e-14*eye(M_.exo_nbr);
- cs = chol(SS)';
- SS = cs*cs';
- b1(:,exo_names_orig_ord) = ghu1;
- b2(:,exo_names_orig_ord) = ghu(iky,:);
- mathp_col = [];
- IA = eye(size(A,1));
- IE = eye(M_.exo_nbr);
- for ig = 1:ngrid
- if hp1(ig)==0,
- f_hp = zeros(length(ivar),length(ivar));
- else
- f_omega =(1/(2*pi))*( [inv(IA-A*tneg(ig))*b1;IE]...
- *SS*[b1'*inv(IA-A'*tpos(ig)) ...
- IE]); % state variables
- g_omega = [aa*tneg(ig) b2]*f_omega*[aa'*tpos(ig); b2']; % selected variables
- f_hp = hp1(ig)^2*g_omega; % spectral density of selected filtered series
- end
- mathp_col = [mathp_col ; (f_hp(:))']; % store as matrix row
- % for ifft
- end;
- imathp_col = real(ifft(mathp_col))*(2*pi);
- vv = diag(reshape(imathp_col(1,:),nvar,nvar));
- for i=1:M_.exo_nbr
+ if ~nodecomposition && M_.exo_nbr > 0
+ if M_.exo_nbr == 1
+ Gamma_y{nar+2} = ones(nvar,1);
+ else
+ Gamma_y{nar+2} = zeros(nvar,M_.exo_nbr);
+ SS(exo_names_orig_ord,exo_names_orig_ord) = M_.Sigma_e+1e-14*eye(M_.exo_nbr);
+ cs = chol(SS)';
+ SS = cs*cs';
+ b1(:,exo_names_orig_ord) = ghu1;
+ b2(:,exo_names_orig_ord) = ghu(iky,:);
mathp_col = [];
- SSi = cs(:,i)*cs(:,i)';
+ IA = eye(size(A,1));
+ IE = eye(M_.exo_nbr);
for ig = 1:ngrid
if hp1(ig)==0,
f_hp = zeros(length(ivar),length(ivar));
else
f_omega =(1/(2*pi))*( [inv(IA-A*tneg(ig))*b1;IE]...
- *SSi*[b1'*inv(IA-A'*tpos(ig)) ...
- IE]); % state variables
+ *SS*[b1'*inv(IA-A'*tpos(ig)) ...
+ IE]); % state variables
g_omega = [aa*tneg(ig) b2]*f_omega*[aa'*tpos(ig); b2']; % selected variables
f_hp = hp1(ig)^2*g_omega; % spectral density of selected filtered series
end
mathp_col = [mathp_col ; (f_hp(:))']; % store as matrix row
- % for ifft
- end;
+ % for ifft
+ end;
imathp_col = real(ifft(mathp_col))*(2*pi);
- Gamma_y{nar+2}(:,i) = abs(diag(reshape(imathp_col(1,:),nvar,nvar)))./vv;
+ vv = diag(reshape(imathp_col(1,:),nvar,nvar));
+ for i=1:M_.exo_nbr
+ mathp_col = [];
+ SSi = cs(:,i)*cs(:,i)';
+ for ig = 1:ngrid
+ if hp1(ig)==0,
+ f_hp = zeros(length(ivar),length(ivar));
+ else
+ f_omega =(1/(2*pi))*( [inv(IA-A*tneg(ig))*b1;IE]...
+ *SSi*[b1'*inv(IA-A'*tpos(ig)) ...
+ IE]); % state variables
+ g_omega = [aa*tneg(ig) b2]*f_omega*[aa'*tpos(ig); b2']; % selected variables
+ f_hp = hp1(ig)^2*g_omega; % spectral density of selected filtered series
+ end
+ mathp_col = [mathp_col ; (f_hp(:))']; % store as matrix row
+ % for ifft
+ end;
+ imathp_col = real(ifft(mathp_col))*(2*pi);
+ Gamma_y{nar+2}(:,i) = abs(diag(reshape(imathp_col(1,:),nvar,nvar)))./vv;
+ end
end
end
end
--
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