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function xparam = get_posterior_parameters(type,M_,estim_params_,oo_,options_,field1)
% function xparam = get_posterior_parameters(type,M_,estim_params_,oo_,options_,field1)
% Selects (estimated) parameters (posterior mode or posterior mean).
%
% INPUTS
% o type [char] = 'mode' or 'mean'.
% o M_: [structure] Dynare structure describing the model.
% o estim_params_: [structure] Dynare structure describing the estimated parameters.
% o field_1 [char] optional field like 'mle_'.
%
% OUTPUTS
% o xparam vector of estimated parameters
%
% SPECIAL REQUIREMENTS
% None.
% Copyright (C) 2006-2018 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 nargin<6
field1='posterior_';
end
nvx = estim_params_.nvx;
nvn = estim_params_.nvn;
ncx = estim_params_.ncx;
ncn = estim_params_.ncn;
np = estim_params_.np;
xparam = zeros(nvx+nvn+ncx+ncn+np,1);
m = 1;
for i=1:nvx
k1 = estim_params_.var_exo(i,1);
name1 = M_.exo_names{k1};
xparam(m) = oo_.([field1 type]).shocks_std.(name1);
m = m+1;
end
for i=1:nvn
k1 = estim_params_.nvn_observable_correspondence(i,1);
name1 = options_.varobs{k1};
xparam(m) = oo_.([field1 type]).measurement_errors_std.(name1);
m = m+1;
end
for i=1:ncx
k1 = estim_params_.corrx(i,1);
k2 = estim_params_.corrx(i,2);
name1 = M_.exo_names{k1};
name2 = M_.exo_names{k2};
xparam(m) = oo_.([field1 type]).shocks_corr.([name1 '_' name2]);
m = m+1;
end
for i=1:ncn
k1 = estim_params_.corrn_observable_correspondence(i,1);
k2 = estim_params_.corrn_observable_correspondence(i,2);
name1 = options_.varobs{k1};
name2 = options_.varobs{k2};
xparam(m) = oo_.([field1 type]).measurement_errors_corr.([name1 '_' name2]);
m = m+1;
end
FirstDeep = m;
for i=1:np
name1 = M_.param_names{estim_params_.param_vals(i,1)};
xparam(m) = oo_.([field1 type]).parameters.(name1);
m = m+1;
end
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