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changed handling of nonstationary variables: 

- oo_.mean, oo_.var, oo_.gamma_y contains all selected variables
- moments of non-stationary variables are set to NaN
options_.Schur_vec_tolerance lowered to 10^-11


git-svn-id: https://www.dynare.org/svn/dynare/trunk@2810 ac1d8469-bf42-47a9-8791-bf33cf982152
time-shift
michel 2009-07-04 14:11:51 +00:00
parent 80921e4042
commit 05189497a5
3 changed files with 43 additions and 28 deletions
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24
matlab/disp_th_moments.m
View File

@ -35,8 +35,10 @@ function disp_th_moments(dr,var_list)
end end
end end
[oo_.gamma_y,ivar] = th_autocovariances(dr,ivar,M_,options_); [oo_.gamma_y,stationary_vars] = th_autocovariances(dr,ivar,M_,options_);
m = dr.ys(ivar); m = dr.ys(ivar);
non_stationary_vars = setdiff(1:length(ivar),stationary_vars);
m(ivar(non_stationary_vars)) = NaN;
i1 = find(abs(diag(oo_.gamma_y{1})) > 1e-12); i1 = find(abs(diag(oo_.gamma_y{1})) > 1e-12);
@ -50,14 +52,15 @@ function disp_th_moments(dr,var_list)
oo_.mean = m; oo_.mean = m;
oo_.var = oo_.gamma_y{1}; oo_.var = oo_.gamma_y{1};
lh = size(deblank(M_.endo_names(ivar,:)),2)+2;
if options_.nomoments == 0 if options_.nomoments == 0
title='THEORETICAL MOMENTS'; title='THEORETICAL MOMENTS';
if options_.hp_filter if options_.hp_filter
title = [title ' (HP filter, lambda = ' int2str(options_.hp_filter) ')']; title = [title ' (HP filter, lambda = ' int2str(options_.hp_filter) ')'];
end end
headers=strvcat('VARIABLE','MEAN','STD. DEV.','VARIANCE'); headers=strvcat('VARIABLE','MEAN','STD. DEV.','VARIANCE');
dyntable(title,headers,deblank(M_.endo_names(ivar,:)),z,lh,11,4); labels = deblank(M_.endo_names(ivar,:));
lh = size(labels,2)+2;
dyntable(title,headers,labels,z,lh,11,4);
if M_.exo_nbr > 1 if M_.exo_nbr > 1
disp(' ') disp(' ')
title='VARIANCE DECOMPOSITION (in percent)'; title='VARIANCE DECOMPOSITION (in percent)';
@ -68,8 +71,9 @@ function disp_th_moments(dr,var_list)
headers = M_.exo_names; headers = M_.exo_names;
headers(M_.exo_names_orig_ord,:) = headers; headers(M_.exo_names_orig_ord,:) = headers;
headers = strvcat(' ',headers); headers = strvcat(' ',headers);
dyntable(title,headers,deblank(M_.endo_names(ivar(i1),:)),100*oo_.gamma_y{options_.ar+2}(i1,:), ... lh = size(deblank(M_.endo_names(ivar(stationary_vars),:)),2)+2;
lh,8,2); dyntable(title,headers,deblank(M_.endo_names(ivar(stationary_vars), ...
:)),100*oo_.gamma_y{options_.ar+2}(stationary_vars,:),lh,8,2);
end end
end end
@ -79,10 +83,11 @@ function disp_th_moments(dr,var_list)
if options_.hp_filter if options_.hp_filter
title = [title ' (HP filter, lambda = ' int2str(options_.hp_filter) ')']; title = [title ' (HP filter, lambda = ' int2str(options_.hp_filter) ')'];
end end
labels = deblank(M_.endo_names(ivar,:)); labels = deblank(M_.endo_names(ivar(i1),:));
headers = strvcat('Variables',labels(i1,:)); headers = strvcat('Variables',labels);
corr = oo_.gamma_y{1}(i1,i1)./(sd(i1)*sd(i1)'); corr = oo_.gamma_y{1}(i1,i1)./(sd(i1)*sd(i1)');
dyntable(title,headers,labels(i1,:),corr,lh,8,4); lh = size(labels,2)+2;
dyntable(title,headers,labels,corr,lh,8,4);
end end
if options_.ar > 0 if options_.ar > 0
@ -98,7 +103,8 @@ function disp_th_moments(dr,var_list)
oo_.autocorr{i} = oo_.gamma_y{i+1}; oo_.autocorr{i} = oo_.gamma_y{i+1};
z(:,i) = diag(oo_.gamma_y{i+1}(i1,i1)); z(:,i) = diag(oo_.gamma_y{i+1}(i1,i1));
end end
dyntable(title,headers,labels,z,0,8,4); lh = size(labels,2)+2;
dyntable(title,headers,labels,z,lh,8,4);
end end
% 10/09/02 MJ % 10/09/02 MJ

6
matlab/global_initialization.m
View File

@ -41,9 +41,9 @@ function global_initialization()
options_.gstep = 1e-2; options_.gstep = 1e-2;
options_.debug = 0; options_.debug = 0;
options_.initval_file = 0; options_.initval_file = 0;
options_.Schur_vec_tol = 1e-8; % used to find nonstationary variables options_.Schur_vec_tol = 1e-11; % used to find nonstationary variables
% in Schur decomposition of the % in Schur decomposition of the
% transition matrix % transition matrix
options_.qz_criterium = 1.000001; options_.qz_criterium = 1.000001;
options_.lyapunov_complex_threshold = 1e-15; options_.lyapunov_complex_threshold = 1e-15;
options_.solve_tolf = eps^(1/3); options_.solve_tolf = eps^(1/3);

41
matlab/th_autocovariances.m
View File

@ -1,4 +1,4 @@
function [Gamma_y,ivar] = th_autocovariances(dr,ivar,M_,options_,nodecomposition) function [Gamma_y,stationary_vars] = th_autocovariances(dr,ivar,M_,options_,nodecomposition)
% Computes the theoretical auto-covariances, Gamma_y, for an AR(p) process % Computes the theoretical auto-covariances, Gamma_y, for an AR(p) process
% with coefficients dr.ghx and dr.ghu and shock variances Sigma_e_ % with coefficients dr.ghx and dr.ghu and shock variances Sigma_e_
% for a subset of variables ivar (indices in lgy_) % for a subset of variables ivar (indices in lgy_)
@ -19,7 +19,8 @@ function [Gamma_y,ivar] = th_autocovariances(dr,ivar,M_,options_,nodecomposition
% Gamma_y{nar+2} [double] Variance decomposition. % Gamma_y{nar+2} [double] Variance decomposition.
% Gamma_y{nar+3} [double] Expectation of the endogenous variables associated with a second % Gamma_y{nar+3} [double] Expectation of the endogenous variables associated with a second
% order approximation. % order approximation.
% ivar [integer] Vector of indices for a subset of variables. % stationary_vars [integer] Vector of indices of stationary
% variables in declaration order
% %
% SPECIAL REQUIREMENTS % SPECIAL REQUIREMENTS
% %
@ -45,6 +46,7 @@ function [Gamma_y,ivar] = th_autocovariances(dr,ivar,M_,options_,nodecomposition
nodecomposition = 0; nodecomposition = 0;
end end
endo_nbr = M_.endo_nbr;
exo_names_orig_ord = M_.exo_names_orig_ord; exo_names_orig_ord = M_.exo_names_orig_ord;
if exist('OCTAVE_VERSION') if exist('OCTAVE_VERSION')
warning('off', 'Octave:divide-by-zero') warning('off', 'Octave:divide-by-zero')
@ -54,7 +56,7 @@ function [Gamma_y,ivar] = th_autocovariances(dr,ivar,M_,options_,nodecomposition
nar = options_.ar; nar = options_.ar;
Gamma_y = cell(nar+1,1); Gamma_y = cell(nar+1,1);
if isempty(ivar) if isempty(ivar)
ivar = [1:M_.endo_nbr]'; ivar = [1:endo_nbr]';
end end
nvar = size(ivar,1); nvar = size(ivar,1);
@ -63,8 +65,8 @@ function [Gamma_y,ivar] = th_autocovariances(dr,ivar,M_,options_,nodecomposition
npred = dr.npred; npred = dr.npred;
nstatic = dr.nstatic; nstatic = dr.nstatic;
kstate = dr.kstate; kstate = dr.kstate;
order = dr.order_var; order_var = dr.order_var;
iv(order) = [1:length(order)]; inv_order_var = dr.inv_order_var;
nx = size(ghx,2); nx = size(ghx,2);
ikx = [nstatic+1:nstatic+npred]; ikx = [nstatic+1:nstatic+npred];
@ -94,10 +96,12 @@ function [Gamma_y,ivar] = th_autocovariances(dr,ivar,M_,options_,nodecomposition
[A,B] = kalman_transition_matrix(dr,ipred,1:nx,dr.transition_auxiliary_variables,M_.exo_nbr); [A,B] = kalman_transition_matrix(dr,ipred,1:nx,dr.transition_auxiliary_variables,M_.exo_nbr);
if options_.order == 2 | options_.hp_filter == 0 if options_.order == 2 | options_.hp_filter == 0
[vx, u] = lyapunov_symm(A,B*M_.Sigma_e*B',options_.qz_criterium,options_.lyapunov_complex_threshold); [vx, u] = lyapunov_symm(A,B*M_.Sigma_e*B',options_.qz_criterium,options_.lyapunov_complex_threshold);
iky = iv(ivar); iky = inv_order_var(ivar);
stationary_vars = (1:length(ivar))';
if ~isempty(u) if ~isempty(u)
iky = iky(find(all(abs(ghx(iky,:)*u) < options_.Schur_vec_tol,2))); x = abs(ghx*u);
ivar = dr.order_var(iky); iky = iky(find(all(x(iky,:) < options_.Schur_vec_tol,2)));
stationary_vars = find(all(x(inv_order_var(ivar(stationary_vars)),:) < options_.Schur_vec_tol,2));
end end
aa = ghx(iky,:); aa = ghx(iky,:);
bb = ghu(iky,:); bb = ghu(iky,:);
@ -109,23 +113,28 @@ function [Gamma_y,ivar] = th_autocovariances(dr,ivar,M_,options_,nodecomposition
end end
end end
if options_.hp_filter == 0 if options_.hp_filter == 0
Gamma_y{1} = aa*vx*aa'+ bb*M_.Sigma_e*bb'; v = NaN*ones(nvar,nvar);
k = find(abs(Gamma_y{1}) < 1e-12); v(stationary_vars,stationary_vars) = aa*vx*aa'+ bb*M_.Sigma_e*bb';
Gamma_y{1}(k) = 0; k = find(abs(v) < 1e-12);
v(k) = 0;
Gamma_y{1} = v;
% autocorrelations % autocorrelations
if nar > 0 if nar > 0
vxy = (A*vx*aa'+ghu1*M_.Sigma_e*bb'); vxy = (A*vx*aa'+ghu1*M_.Sigma_e*bb');
sy = sqrt(diag(Gamma_y{1})); sy = sqrt(diag(Gamma_y{1}));
sy = sy(stationary_vars);
sy = sy *sy'; sy = sy *sy';
Gamma_y{2} = aa*vxy./sy; Gamma_y{2} = NaN*ones(nvar,nvar);
Gamma_y{2}(stationary_vars,stationary_vars) = aa*vxy./sy;
for i=2:nar for i=2:nar
vxy = A*vxy; vxy = A*vxy;
Gamma_y{i+1} = aa*vxy./sy; Gamma_y{i+1} = NaN*ones(nvar,nvar);
Gamma_y{i+1}(stationary_vars,stationary_vars) = aa*vxy./sy;
end end
end end
% variance decomposition % variance decomposition
if ~nodecomposition && M_.exo_nbr > 1 if ~nodecomposition && M_.exo_nbr > 1
Gamma_y{nar+2} = zeros(length(ivar),M_.exo_nbr); 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); SS(exo_names_orig_ord,exo_names_orig_ord)=M_.Sigma_e+1e-14*eye(M_.exo_nbr);
cs = chol(SS)'; cs = chol(SS)';
b1(:,exo_names_orig_ord) = ghu1; b1(:,exo_names_orig_ord) = ghu1;
@ -136,12 +145,12 @@ function [Gamma_y,ivar] = th_autocovariances(dr,ivar,M_,options_,nodecomposition
vv = diag(aa*vx*aa'+b2*b2'); vv = diag(aa*vx*aa'+b2*b2');
for i=1:M_.exo_nbr for i=1:M_.exo_nbr
vx1 = lyapunov_symm(A,b1(:,i)*b1(:,i)',options_.qz_criterium,options_.lyapunov_complex_threshold,2); vx1 = lyapunov_symm(A,b1(:,i)*b1(:,i)',options_.qz_criterium,options_.lyapunov_complex_threshold,2);
Gamma_y{nar+2}(:,i) = abs(diag(aa*vx1*aa'+b2(:,i)*b2(:,i)'))./vv; Gamma_y{nar+2}(stationary_vars,i) = abs(diag(aa*vx1*aa'+b2(:,i)*b2(:,i)'))./vv;
end end
end end
else% ==> Theoretical HP filter. else% ==> Theoretical HP filter.
if options_.order < 2 if options_.order < 2
iky = iv(ivar); iky = inv_order_var(ivar);
aa = ghx(iky,:); aa = ghx(iky,:);
bb = ghu(iky,:); bb = ghu(iky,:);
end end