Disentangle computation and display of conditional_variance_decomposition
Previously, noprint suppressed the computation of conditional_variance_decomposition although it should only suppress its display. Also adds a dedicated field for the unconditional variance decomposition.time-shift
Johannes Pfeifer
parent
4a8e737c7a
commit
6eb3a3d3ad
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@ -50,43 +50,54 @@ z = [ m sd s2 ];
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oo_.mean = m;
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oo_.var = oo_.gamma_y{1};
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if ~options_.noprint %options_.nomoments == 0
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if options_.order == 2
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title='APROXIMATED THEORETICAL MOMENTS';
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else
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title='THEORETICAL MOMENTS';
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end
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if options_.hp_filter
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title = [title ' (HP filter, lambda = ' num2str(options_.hp_filter) ')'];
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end
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headers=char('VARIABLE','MEAN','STD. DEV.','VARIANCE');
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labels = deblank(M_.endo_names(ivar,:));
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lh = size(labels,2)+2;
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dyntable(title,headers,labels,z,lh,11,4);
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if M_.exo_nbr > 1 && size(stationary_vars, 1) > 0
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skipline()
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if M_.exo_nbr > 1 && size(stationary_vars, 1) > 0
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oo_.variance_decomposition=100*oo_.gamma_y{options_.ar+2};
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if ~options_.noprint %options_.nomoments == 0
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if options_.order == 2
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title='APPROXIMATED VARIANCE DECOMPOSITION (in percent)';
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title='APROXIMATED THEORETICAL MOMENTS';
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else
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title='VARIANCE DECOMPOSITION (in percent)';
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title='THEORETICAL MOMENTS';
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end
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if options_.hp_filter
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title = [title ' (HP filter, lambda = ' ...
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num2str(options_.hp_filter) ')'];
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title = [title ' (HP filter, lambda = ' num2str(options_.hp_filter) ')'];
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end
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headers = M_.exo_names;
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headers(M_.exo_names_orig_ord,:) = headers;
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headers = char(' ',headers);
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lh = size(deblank(M_.endo_names(ivar(stationary_vars),:)),2)+2;
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dyntable(title,headers,deblank(M_.endo_names(ivar(stationary_vars), ...
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:)),100*oo_.gamma_y{options_.ar+2}(stationary_vars,:),lh,8,2);
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headers=char('VARIABLE','MEAN','STD. DEV.','VARIANCE');
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labels = deblank(M_.endo_names(ivar,:));
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lh = size(labels,2)+2;
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dyntable(title,headers,labels,z,lh,11,4);
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skipline()
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if options_.order == 2
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title='APPROXIMATED VARIANCE DECOMPOSITION (in percent)';
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else
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title='VARIANCE DECOMPOSITION (in percent)';
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end
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if options_.hp_filter
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title = [title ' (HP filter, lambda = ' ...
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num2str(options_.hp_filter) ')'];
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end
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headers = M_.exo_names;
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headers(M_.exo_names_orig_ord,:) = headers;
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headers = char(' ',headers);
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lh = size(deblank(M_.endo_names(ivar(stationary_vars),:)),2)+2;
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dyntable(title,headers,deblank(M_.endo_names(ivar(stationary_vars), ...
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:)),100*oo_.gamma_y{options_.ar+2}(stationary_vars,:),lh,8,2);
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end
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conditional_variance_steps = options_.conditional_variance_decomposition;
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if length(conditional_variance_steps)
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oo_ = display_conditional_variance_decomposition(conditional_variance_steps,...
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ivar,dr,M_, ...
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options_,oo_);
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StateSpaceModel.number_of_state_equations = M_.endo_nbr;
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StateSpaceModel.number_of_state_innovations = M_.exo_nbr;
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StateSpaceModel.sigma_e_is_diagonal = M_.sigma_e_is_diagonal;
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[StateSpaceModel.transition_matrix,StateSpaceModel.impulse_matrix] = kalman_transition_matrix(dr,(1:M_.endo_nbr)',M_.nstatic+(1:M_.nspred)',M_.exo_nbr);
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StateSpaceModel.state_innovations_covariance_matrix = M_.Sigma_e;
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StateSpaceModel.order_var = dr.order_var;
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oo_.conditional_variance_decomposition = conditional_variance_decomposition(StateSpaceModel,conditional_variance_steps,ivar);
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if options_.noprint == 0
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display_conditional_variance_decomposition(oo_.conditional_variance_decomposition,conditional_variance_steps,...
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ivar,M_,options_);
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end
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end
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end
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@ -1,22 +1,19 @@
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function oo_ = display_conditional_variance_decomposition(Steps, SubsetOfVariables, dr,M_,options_,oo_)
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% This function computes the conditional variance decomposition of a given state space model
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function display_conditional_variance_decomposition(conditional_decomposition_array,Steps,SubsetOfVariables,M_,options_)
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% This function displays the conditional variance decomposition of a given state space model
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% for a subset of endogenous variables.
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%
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% INPUTS
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% StateSpaceModel [structure] Specification of the state space model.
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% conditional_decomposition_array [matrix] Output matrix from compute_conditional_variance_decomposition
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% Steps [integer] 1*h vector of dates.
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% SubsetOfVariables [integer] 1*q vector of indices.
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%
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% M_ [structure] Dynare structure containing the
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% Model description
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% options_ [structure] Dynare structure containing the
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% options
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% OUTPUTS
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% PackedConditionalVarianceDecomposition [double] n(n+1)/2*p matrix, where p is the number of state innovations and
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% n is equal to length(SubsetOfVariables).
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% none
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%
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% SPECIAL REQUIREMENTS
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%
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% [1] The covariance matrix of the state innovations needs to be diagonal.
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% [2] In this version, absence of measurement errors is assumed...
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% Copyright (C) 2010-2013 Dynare Team
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% Copyright (C) 2010-2014 Dynare Team
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%
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% This file is part of Dynare.
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%
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@ -33,49 +30,28 @@ function oo_ = display_conditional_variance_decomposition(Steps, SubsetOfVariabl
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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endo_nbr = M_.endo_nbr;
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exo_nbr = M_.exo_nbr;
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StateSpaceModel.number_of_state_equations = M_.endo_nbr;
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StateSpaceModel.number_of_state_innovations = exo_nbr;
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StateSpaceModel.sigma_e_is_diagonal = M_.sigma_e_is_diagonal;
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iv = (1:endo_nbr)';
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ic = M_.nstatic+(1:M_.nspred)';
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[StateSpaceModel.transition_matrix,StateSpaceModel.impulse_matrix] = kalman_transition_matrix(dr,iv,ic,exo_nbr);
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StateSpaceModel.state_innovations_covariance_matrix = M_.Sigma_e;
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StateSpaceModel.order_var = dr.order_var;
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conditional_decomposition_array = conditional_variance_decomposition(StateSpaceModel,Steps,SubsetOfVariables );
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if options_.noprint == 0
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if options_.order == 2
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skipline()
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disp('APPROXIMATED CONDITIONAL VARIANCE DECOMPOSITION (in percent)')
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else
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skipline()
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disp('CONDITIONAL VARIANCE DECOMPOSITION (in percent)')
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end
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if options_.order == 2
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skipline()
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disp('APPROXIMATED CONDITIONAL VARIANCE DECOMPOSITION (in percent)')
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else
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skipline()
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disp('CONDITIONAL VARIANCE DECOMPOSITION (in percent)')
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end
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vardec_i = zeros(length(SubsetOfVariables),exo_nbr);
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vardec_i = zeros(length(SubsetOfVariables),M_.exo_nbr);
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for i=1:length(Steps)
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disp(['Period ' int2str(Steps(i)) ':'])
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for j=1:exo_nbr
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for j=1:M_.exo_nbr
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vardec_i(:,j) = 100*conditional_decomposition_array(:, ...
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i,j);
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end
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if options_.noprint == 0
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headers = M_.exo_names;
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headers(M_.exo_names_orig_ord,:) = headers;
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headers = char(' ',headers);
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lh = size(deblank(M_.endo_names(SubsetOfVariables,:)),2)+2;
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dyntable('',headers,...
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deblank(M_.endo_names(SubsetOfVariables,:)),...
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vardec_i,lh,8,2);
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end
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end
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oo_.conditional_variance_decomposition = conditional_decomposition_array;
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headers = M_.exo_names;
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headers(M_.exo_names_orig_ord,:) = headers;
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headers = char(' ',headers);
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lh = size(deblank(M_.endo_names(SubsetOfVariables,:)),2)+2;
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dyntable('',headers,...
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deblank(M_.endo_names(SubsetOfVariables,:)),...
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vardec_i,lh,8,2);
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end
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