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function [nvar,vartan,CovarFileNumber] = dsge_simulated_theoretical_covariance(SampleSize,M_,options_,oo_,type)
% This function computes the posterior or prior distribution of the endogenous
% variables second order moments.
%
% 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).
% CovarFileNumber [integer] scalar, number of prior or posterior data files (for covariance).
% Copyright (C) 2007-2012 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/>.
nodecomposition = 1;
% 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_covariance:: Unknown type!')
error();
end
NumberOfDrawsFiles = length(DrawsFiles);
% 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;
% Number of lines in posterior data files.
MaXNumberOfCovarLines = ceil(options_.MaxNumberOfBytes/(nvar*(nvar+1)/2)/8);
if SampleSize<=MaXNumberOfCovarLines
Covariance_matrix = zeros(SampleSize,nvar*(nvar+1)/2);
NumberOfCovarFiles = 1;
else
Covariance_matrix = zeros(MaXNumberOfCovarLines,nvar*(nvar+1)/2);
NumberOfLinesInTheLastCovarFile = mod(SampleSize,MaXNumberOfCovarLines);
NumberOfCovarFiles = ceil(SampleSize/MaXNumberOfCovarLines);
end
NumberOfCovarLines = rows(Covariance_matrix);
CovarFileNumber = 1;
% Compute 2nd order moments and save them in *_[Posterior, Prior]2ndOrderMoments* files
linea = 0;
for file = 1:NumberOfDrawsFiles
if posterior
load([M_.dname '/metropolis/' DrawsFiles(file).name ],'pdraws');
else
load([M_.dname '/prior/draws/' DrawsFiles(file).name ],'pdraws');
end
NumberOfDraws = rows(pdraws);
isdrsaved = columns(pdraws)-1;
for linee = 1:NumberOfDraws
linea = linea+1;
if isdrsaved
dr = pdraws{linee,2};
else
set_parameters(pdraws{linee,1});
[dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
end
tmp = th_autocovariances(dr,ivar,M_,options_,nodecomposition);
for i=1:nvar
for j=i:nvar
Covariance_matrix(linea,symmetric_matrix_index(i,j,nvar)) = tmp{1}(i,j);
end
end
if linea == NumberOfCovarLines
if posterior
save([ M_.dname '/metropolis/' M_.fname '_Posterior2ndOrderMoments' int2str(CovarFileNumber) '.mat' ],'Covariance_matrix');
else
save([ M_.dname '/prior/moments/' M_.fname '_Prior2ndOrderMoments' int2str(CovarFileNumber) '.mat' ],'Covariance_matrix');
end
CovarFileNumber = CovarFileNumber + 1;
linea = 0;
test = CovarFileNumber-NumberOfCovarFiles;
if ~test% Prepare the last round...
Covariance_matrix = zeros(NumberOfLinesInTheLastCovarFile,nvar*(nvar+1)/2);
NumberOfCovarLines = NumberOfLinesInTheLastCovarFile;
CovarFileNumber = CovarFileNumber - 1;
elseif test<0
Covariance_matrix = zeros(MaXNumberOfCovarLines,nvar*(nvar+1)/2);
else
clear('Covariance_matrix');
end
end
end
end
options_.ar = nar;
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