82 lines
3.1 KiB
Matlab
82 lines
3.1 KiB
Matlab
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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% 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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% 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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% 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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%
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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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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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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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disp(' ')
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disp('APPROXIMATED CONDITIONAL VARIANCE DECOMPOSITION (in percent)')
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else
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disp(' ')
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disp('CONDITIONAL VARIANCE DECOMPOSITION (in percent)')
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end
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end
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vardec_i = zeros(length(SubsetOfVariables),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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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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