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function [nvar,vartan,NumberOfConditionalDecompFiles] = ...
dsge_simulated_theoretical_conditional_variance_decomposition(SampleSize,Steps,M_,options_,oo_,type)
% This function computes the posterior or prior distribution of the conditional variance
% decomposition of the endogenous variables (or a subset of the endogenous variables).
%
% INPUTS
% SampleSize [integer] scalar, number of simulations.
% M_ [structure] Dynare structure describing the model.
% options_ [structure] Dynare structure defining global options.
% oo_ [structure] Dynare structure where the results are saved.
% type [string] 'prior' or 'posterior'
%
%
% OUTPUTS
% nvar [integer] nvar is the number of stationary variables.
% vartan [char] array of characters (with nvar rows).
% NumberOfConditionalDecompFiles [integer] scalar, number of prior or posterior data files (for covariance).
% Copyright (C) 2009-2012 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
% Get informations about the _posterior_draws files.
if strcmpi(type,'posterior')
DrawsFiles = dir([M_.dname '/metropolis/' M_.fname '_' type '_draws*' ]);
posterior = 1;
elseif strcmpi(type,'prior')
DrawsFiles = dir([M_.dname '/prior/draws/' type '_draws*' ]);
CheckPath('prior/moments',M_.dname);
posterior = 0;
else
disp('dsge_simulated_theoretical_conditional_variance_decomposition:: Unknown type!')
error()
end
% Set varlist (vartan)
if ~posterior
if isfield(options_,'varlist')
temp = options_.varlist;
end
options_.varlist = options_.prior_analysis_endo_var_list;
end
[ivar,vartan ] = get_variables_list(options_, M_);
if ~posterior
if exist('temp','var')
options_.varlist = temp;
end
end
nvar = length(ivar);
% Set the size of the auto-correlation function to zero.
nar = options_.ar;
options_.ar = 0;
NumberOfDrawsFiles = rows(DrawsFiles);
NumberOfSavedElementsPerSimulation = nvar*M_.exo_nbr*length(Steps);
MaXNumberOfConditionalDecompLines = ceil(options_.MaxNumberOfBytes/NumberOfSavedElementsPerSimulation/8);
if SampleSize<=MaXNumberOfConditionalDecompLines
Conditional_decomposition_array = zeros(nvar,length(Steps),M_.exo_nbr,SampleSize);
NumberOfConditionalDecompFiles = 1;
else
Conditional_decomposition_array = zeros(nvar,length(Steps),M_.exo_nbr,MaXNumberOfConditionalDecompLines);
NumberOfLinesInTheLastConditionalDecompFile = mod(SampleSize,MaXNumberOfConditionalDecompLines);
NumberOfConditionalDecompFiles = ceil(SampleSize/MaXNumberOfConditionalDecompLines);
end
NumberOfConditionalDecompLines = size(Conditional_decomposition_array,4);
ConditionalDecompFileNumber = 0;
StateSpaceModel.number_of_state_equations = M_.endo_nbr;
StateSpaceModel.number_of_state_innovations = M_.exo_nbr;
first_call = 1;
linea = 0;
for file = 1:NumberOfDrawsFiles
if posterior
load([M_.dname '/metropolis/' DrawsFiles(file).name ]);
else
load([M_.dname '/prior/draws/' DrawsFiles(file).name ]);
end
isdrsaved = columns(pdraws)-1;
NumberOfDraws = rows(pdraws);
for linee = 1:NumberOfDraws
linea = linea+1;
if isdrsaved
set_parameters(pdraws{linee,1});% Needed to update the covariance matrix of the state innovations.
dr = pdraws{linee,2};
else
set_parameters(pdraws{linee,1});
[dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
end
if first_call
endo_nbr = M_.endo_nbr;
nstatic = dr.nstatic;
npred = dr.npred;
iv = (1:endo_nbr)';
ic = [ nstatic+(1:npred) endo_nbr+(1:size(dr.ghx,2)-npred) ]';
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.order_var = dr.order_var;
first_call = 0;
clear('endo_nbr','nstatic','npred','k');
end
[StateSpaceModel.transition_matrix,StateSpaceModel.impulse_matrix] = kalman_transition_matrix(dr,iv,ic,M_.exo_nbr);
StateSpaceModel.state_innovations_covariance_matrix = M_.Sigma_e;
clear('dr');
Conditional_decomposition_array(:,:,:,linea) = conditional_variance_decomposition(StateSpaceModel, Steps, ivar);
if linea == NumberOfConditionalDecompLines
ConditionalDecompFileNumber = ConditionalDecompFileNumber + 1;
linea = 0;
if posterior
save([M_.dname '/metropolis/' M_.fname '_PosteriorConditionalVarianceDecomposition' int2str(ConditionalDecompFileNumber) '.mat' ], ...
'Conditional_decomposition_array');
else
save([M_.dname '/prior/moments/' M_.fname '_PriorConditionalVarianceDecomposition' int2str(ConditionalDecompFileNumber) '.mat' ], ...
'Conditional_decomposition_array');
end
if (ConditionalDecompFileNumber==NumberOfConditionalDecompFiles-1)% Prepare last round.
Conditional_decomposition_array = zeros(nvar, length(Steps),M_.exo_nbr,NumberOfLinesInTheLastConditionalDecompFile) ;
NumberOfConditionalDecompLines = NumberOfLinesInTheLastConditionalDecompFile;
elseif ConditionalDecompFileNumber<NumberOfConditionalDecompFiles-1
Conditional_decomposition_array = zeros(nvar,length(Steps),M_.exo_nbr,MaXNumberOfConditionalDecompLines);
else
clear('Conditional_decomposition_array');
end
end
end
end
options_.ar = nar;
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