diff --git a/matlab/disp_th_moments.m b/matlab/disp_th_moments.m
index d1a4876a296cd575dbf51016093277acdcb45767..0c1a245a6db5874bd0d500ef703aa1fa72a2993f 100644
--- a/matlab/disp_th_moments.m
+++ b/matlab/disp_th_moments.m
@@ -50,43 +50,54 @@ z = [ m sd s2 ];
oo_.mean = m;
oo_.var = oo_.gamma_y{1};
-if ~options_.noprint %options_.nomoments == 0
- if options_.order == 2
- title='APROXIMATED THEORETICAL MOMENTS';
- else
- title='THEORETICAL MOMENTS';
- end
- if options_.hp_filter
- title = [title ' (HP filter, lambda = ' num2str(options_.hp_filter) ')'];
- end
- headers=char('VARIABLE','MEAN','STD. DEV.','VARIANCE');
- labels = deblank(M_.endo_names(ivar,:));
- lh = size(labels,2)+2;
- dyntable(title,headers,labels,z,lh,11,4);
- if M_.exo_nbr > 1 && size(stationary_vars, 1) > 0
- skipline()
+if M_.exo_nbr > 1 && size(stationary_vars, 1) > 0
+ oo_.variance_decomposition=100*oo_.gamma_y{options_.ar+2};
+ if ~options_.noprint %options_.nomoments == 0
if options_.order == 2
- title='APPROXIMATED VARIANCE DECOMPOSITION (in percent)';
+ title='APROXIMATED THEORETICAL MOMENTS';
else
- title='VARIANCE DECOMPOSITION (in percent)';
+ title='THEORETICAL MOMENTS';
end
if options_.hp_filter
- title = [title ' (HP filter, lambda = ' ...
- num2str(options_.hp_filter) ')'];
+ title = [title ' (HP filter, lambda = ' num2str(options_.hp_filter) ')'];
end
- headers = M_.exo_names;
- headers(M_.exo_names_orig_ord,:) = headers;
- headers = char(' ',headers);
- lh = size(deblank(M_.endo_names(ivar(stationary_vars),:)),2)+2;
- dyntable(title,headers,deblank(M_.endo_names(ivar(stationary_vars), ...
- :)),100*oo_.gamma_y{options_.ar+2}(stationary_vars,:),lh,8,2);
+ headers=char('VARIABLE','MEAN','STD. DEV.','VARIANCE');
+ labels = deblank(M_.endo_names(ivar,:));
+ lh = size(labels,2)+2;
+ dyntable(title,headers,labels,z,lh,11,4);
+
+ skipline()
+ if options_.order == 2
+ title='APPROXIMATED VARIANCE DECOMPOSITION (in percent)';
+ else
+ title='VARIANCE DECOMPOSITION (in percent)';
+ end
+ if options_.hp_filter
+ title = [title ' (HP filter, lambda = ' ...
+ num2str(options_.hp_filter) ')'];
+ end
+ headers = M_.exo_names;
+ headers(M_.exo_names_orig_ord,:) = headers;
+ headers = char(' ',headers);
+ lh = size(deblank(M_.endo_names(ivar(stationary_vars),:)),2)+2;
+ dyntable(title,headers,deblank(M_.endo_names(ivar(stationary_vars), ...
+ :)),100*oo_.gamma_y{options_.ar+2}(stationary_vars,:),lh,8,2);
end
conditional_variance_steps = options_.conditional_variance_decomposition;
if length(conditional_variance_steps)
- oo_ = display_conditional_variance_decomposition(conditional_variance_steps,...
- ivar,dr,M_, ...
- options_,oo_);
+ StateSpaceModel.number_of_state_equations = M_.endo_nbr;
+ StateSpaceModel.number_of_state_innovations = M_.exo_nbr;
+ StateSpaceModel.sigma_e_is_diagonal = M_.sigma_e_is_diagonal;
+ [StateSpaceModel.transition_matrix,StateSpaceModel.impulse_matrix] = kalman_transition_matrix(dr,(1:M_.endo_nbr)',M_.nstatic+(1:M_.nspred)',M_.exo_nbr);
+ StateSpaceModel.state_innovations_covariance_matrix = M_.Sigma_e;
+ StateSpaceModel.order_var = dr.order_var;
+ oo_.conditional_variance_decomposition = conditional_variance_decomposition(StateSpaceModel,conditional_variance_steps,ivar);
+
+ if options_.noprint == 0
+ display_conditional_variance_decomposition(oo_.conditional_variance_decomposition,conditional_variance_steps,...
+ ivar,M_,options_);
+ end
end
end
diff --git a/matlab/display_conditional_variance_decomposition.m b/matlab/display_conditional_variance_decomposition.m
index 7ce71478c2fceb1a342e3fd41c84c1b860d1fdd6..2e15a1d9e08021fd14759b8586ea5193097c2220 100644
--- a/matlab/display_conditional_variance_decomposition.m
+++ b/matlab/display_conditional_variance_decomposition.m
@@ -1,22 +1,19 @@
-function oo_ = display_conditional_variance_decomposition(Steps, SubsetOfVariables, dr,M_,options_,oo_)
-% This function computes the conditional variance decomposition of a given state space model
+function display_conditional_variance_decomposition(conditional_decomposition_array,Steps,SubsetOfVariables,M_,options_)
+% This function displays the conditional variance decomposition of a given state space model
% for a subset of endogenous variables.
%
% INPUTS
-% StateSpaceModel [structure] Specification of the state space model.
+% conditional_decomposition_array [matrix] Output matrix from compute_conditional_variance_decomposition
% Steps [integer] 1*h vector of dates.
% SubsetOfVariables [integer] 1*q vector of indices.
-%
+% M_ [structure] Dynare structure containing the
+% Model description
+% options_ [structure] Dynare structure containing the
+% options
% OUTPUTS
-% PackedConditionalVarianceDecomposition [double] n(n+1)/2*p matrix, where p is the number of state innovations and
-% n is equal to length(SubsetOfVariables).
+% none
%
-% SPECIAL REQUIREMENTS
-%
-% [1] The covariance matrix of the state innovations needs to be diagonal.
-% [2] In this version, absence of measurement errors is assumed...
-
-% Copyright (C) 2010-2013 Dynare Team
+% Copyright (C) 2010-2014 Dynare Team
%
% This file is part of Dynare.
%
@@ -33,49 +30,28 @@ function oo_ = display_conditional_variance_decomposition(Steps, SubsetOfVariabl
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
-endo_nbr = M_.endo_nbr;
-exo_nbr = M_.exo_nbr;
-StateSpaceModel.number_of_state_equations = M_.endo_nbr;
-StateSpaceModel.number_of_state_innovations = exo_nbr;
-StateSpaceModel.sigma_e_is_diagonal = M_.sigma_e_is_diagonal;
-
-iv = (1:endo_nbr)';
-ic = M_.nstatic+(1:M_.nspred)';
-
-[StateSpaceModel.transition_matrix,StateSpaceModel.impulse_matrix] = kalman_transition_matrix(dr,iv,ic,exo_nbr);
-StateSpaceModel.state_innovations_covariance_matrix = M_.Sigma_e;
-StateSpaceModel.order_var = dr.order_var;
-
-conditional_decomposition_array = conditional_variance_decomposition(StateSpaceModel,Steps,SubsetOfVariables );
-
-if options_.noprint == 0
- if options_.order == 2
- skipline()
- disp('APPROXIMATED CONDITIONAL VARIANCE DECOMPOSITION (in percent)')
- else
- skipline()
- disp('CONDITIONAL VARIANCE DECOMPOSITION (in percent)')
- end
+if options_.order == 2
+skipline()
+disp('APPROXIMATED CONDITIONAL VARIANCE DECOMPOSITION (in percent)')
+else
+skipline()
+disp('CONDITIONAL VARIANCE DECOMPOSITION (in percent)')
end
-vardec_i = zeros(length(SubsetOfVariables),exo_nbr);
+vardec_i = zeros(length(SubsetOfVariables),M_.exo_nbr);
for i=1:length(Steps)
disp(['Period ' int2str(Steps(i)) ':'])
-
- for j=1:exo_nbr
+
+ for j=1:M_.exo_nbr
vardec_i(:,j) = 100*conditional_decomposition_array(:, ...
i,j);
end
- if options_.noprint == 0
- headers = M_.exo_names;
- headers(M_.exo_names_orig_ord,:) = headers;
- headers = char(' ',headers);
- lh = size(deblank(M_.endo_names(SubsetOfVariables,:)),2)+2;
- dyntable('',headers,...
- deblank(M_.endo_names(SubsetOfVariables,:)),...
- vardec_i,lh,8,2);
- end
-end
-
-oo_.conditional_variance_decomposition = conditional_decomposition_array;
+ headers = M_.exo_names;
+ headers(M_.exo_names_orig_ord,:) = headers;
+ headers = char(' ',headers);
+ lh = size(deblank(M_.endo_names(SubsetOfVariables,:)),2)+2;
+ dyntable('',headers,...
+ deblank(M_.endo_names(SubsetOfVariables,:)),...
+ vardec_i,lh,8,2);
+end
\ No newline at end of file