128 lines
4.2 KiB
Matlab
128 lines
4.2 KiB
Matlab
function oo_ = compute_moments_varendo(type,options_,M_,oo_,var_list_)
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% Computes the second order moments (autocorrelation function, covariance
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% matrix and variance decomposition) distributions for all the endogenous variables selected in
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% var_list_. The results are saved in oo_
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%
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% INPUTS:
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% type [string] 'posterior' or 'prior'
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% options_ [structure] Dynare structure.
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% M_ [structure] Dynare structure (related to model definition).
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% oo_ [structure] Dynare structure (results).
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% var_list_ [string] Array of string with endogenous variable names.
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%
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% OUTPUTS
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% oo_ [structure] Dynare structure (results).
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%
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% SPECIAL REQUIREMENTS
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% none
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% Copyright (C) 2008-2010 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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if strcmpi(type,'posterior')
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posterior = 1;
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if nargin==4
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var_list_ = options_.varobs;
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end
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elseif strcmpi(type,'prior')
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posterior = 0;
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if nargin==4
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var_list_ = options_.prior_analysis_endo_var_list;
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if isempty(var_list_)
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options_.prior_analysis_var_list = options_.varobs;
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end
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end
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else
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disp('compute_moments_varendo:: Unknown type!')
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error()
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end
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NumberOfEndogenousVariables = rows(var_list_);
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NumberOfExogenousVariables = M_.exo_nbr;
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list_of_exogenous_variables = M_.exo_names;
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NumberOfLags = options_.ar;
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if isfield(options_,'conditional_variance_decomposition')
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Steps = options_.conditional_variance_decomposition;
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else
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Steps = 0;
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end
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% COVARIANCE MATRIX.
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if posterior
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for i=1:NumberOfEndogenousVariables
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for j=i:NumberOfEndogenousVariables
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oo_ = posterior_analysis('variance',var_list_(i,:),var_list_(j,:),[],options_,M_,oo_);
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end
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end
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else
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for i=1:NumberOfEndogenousVariables
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for j=i:NumberOfEndogenousVariables
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oo_ = prior_analysis('variance',var_list_(i,:),var_list_(j,:),[],options_,M_,oo_);
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end
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end
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end
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% CORRELATION FUNCTION.
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if posterior
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for h=NumberOfLags:-1:1
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for i=1:NumberOfEndogenousVariables
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for j=1:NumberOfEndogenousVariables
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oo_ = posterior_analysis('correlation',var_list_(i,:),var_list_(j,:),h,options_,M_,oo_);
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end
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end
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end
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else
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for h=NumberOfLags:-1:1
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for i=1:NumberOfEndogenousVariables
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for j=1:NumberOfEndogenousVariables
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oo_ = prior_analysis('correlation',var_list_(i,:),var_list_(j,:),h,options_,M_,oo_);
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end
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end
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end
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end
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% VARIANCE DECOMPOSITION.
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if M_.exo_nbr > 1
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if posterior
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for i=1:NumberOfEndogenousVariables
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for j=1:NumberOfExogenousVariables
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oo_ = posterior_analysis('decomposition',var_list_(i,:),M_.exo_names(j,:),[],options_,M_,oo_);
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end
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end
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else
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for i=1:NumberOfEndogenousVariables
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for j=1:NumberOfExogenousVariables
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oo_ = prior_analysis('decomposition',var_list_(i,:),M_.exo_names(j,:),[],options_,M_,oo_);
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end
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end
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end
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% CONDITIONAL VARIANCE DECOMPOSITION.
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if Steps
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if posterior
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for i=1:NumberOfEndogenousVariables
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for j=1:NumberOfExogenousVariables
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oo_ = posterior_analysis('conditional decomposition',i,M_.exo_names(j,:),Steps,options_,M_,oo_);
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end
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end
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else
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for i=1:NumberOfEndogenousVariables
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for j=1:NumberOfExogenousVariables
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oo_ = prior_analysis('conditional decomposition',var_list_(i,:),M_.exo_names(j,:),Steps,options_,M_,oo_);
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end
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end
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end
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end
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end
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