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