210 lines
8.1 KiB
Matlab
210 lines
8.1 KiB
Matlab
function [marginal,oo_] = marginal_density(M_, options_, estim_params_, oo_, bayestopt_)
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% function marginal = marginal_density()
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% Computes the marginal density
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%
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% INPUTS
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% options_ [structure]
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% estim_params_ [structure]
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% M_ [structure]
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% oo_ [structure]
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%
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% OUTPUTS
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% marginal: [double] marginal density (modified harmonic mean)
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% oo_ [structure]
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%
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% SPECIAL REQUIREMENTS
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% none
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% Copyright (C) 2005-2018 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 <https://www.gnu.org/licenses/>.
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npar = estim_params_.np+estim_params_.nvn+estim_params_.ncx+estim_params_.ncn+estim_params_.nvx;
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nblck = options_.mh_nblck;
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MetropolisFolder = CheckPath('metropolis',M_.dname);
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ModelName = M_.fname;
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BaseName = [MetropolisFolder filesep ModelName];
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load_last_mh_history_file(MetropolisFolder, ModelName);
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FirstMhFile = record.KeepedDraws.FirstMhFile;
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FirstLine = record.KeepedDraws.FirstLine; ifil = FirstLine;
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TotalNumberOfMhFiles = sum(record.MhDraws(:,2));
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TotalNumberOfMhDraws = sum(record.MhDraws(:,1));
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MAX_nruns = ceil(options_.MaxNumberOfBytes/(npar+2)/8);
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TODROP = floor(options_.mh_drop*TotalNumberOfMhDraws);
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fprintf('Estimation::marginal density: I''m computing the posterior mean and covariance... ');
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[posterior_mean,posterior_covariance,posterior_mode,posterior_kernel_at_the_mode] = compute_mh_covariance_matrix();
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MU = transpose(posterior_mean);
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SIGMA = posterior_covariance;
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lpost_mode = posterior_kernel_at_the_mode;
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xparam1 = posterior_mean;
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hh = inv(SIGMA);
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fprintf(' Done!\n');
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if ~isfield(oo_,'posterior_mode') || (options_.mh_replic && isequal(options_.posterior_sampler_options.posterior_sampling_method,'slice'))
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oo_=fill_mh_mode(posterior_mode',NaN(npar,1),M_,options_,estim_params_,bayestopt_,oo_,'posterior');
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end
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% save the posterior mean and the inverse of the covariance matrix
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% (usefull if the user wants to perform some computations using
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% the posterior mean instead of the posterior mode ==> ).
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parameter_names = bayestopt_.name;
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save([M_.dname filesep 'Output' filesep M_.fname '_mean.mat'],'xparam1','hh','parameter_names','SIGMA');
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fprintf('Estimation::marginal density: I''m computing the posterior log marginal density (modified harmonic mean)... ');
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logdetSIGMA = log(det(SIGMA));
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invSIGMA = hh;
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marginal = zeros(9,2);
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linee = 0;
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check_coverage = 1;
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increase = 1;
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while check_coverage
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for p = 0.1:0.1:0.9
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critval = chi2inv(p,npar);
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ifil = FirstLine;
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tmp = 0;
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for n = FirstMhFile:TotalNumberOfMhFiles
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for b=1:nblck
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load([ BaseName '_mh' int2str(n) '_blck' int2str(b) '.mat'],'x2','logpo2');
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EndOfFile = size(x2,1);
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for i = ifil:EndOfFile
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deviation = ((x2(i,:)-MU)*invSIGMA*(x2(i,:)-MU)')/increase;
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if deviation <= critval
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lftheta = -log(p)-(npar*log(2*pi)+(npar*log(increase)+logdetSIGMA)+deviation)/2;
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tmp = tmp + exp(lftheta - logpo2(i) + lpost_mode);
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end
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end
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end
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ifil = 1;
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end
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linee = linee + 1;
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warning_old_state = warning;
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warning off;
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marginal(linee,:) = [p, lpost_mode-log(tmp/((TotalNumberOfMhDraws-TODROP)*nblck))];
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warning(warning_old_state);
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end
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if abs((marginal(9,2)-marginal(1,2))/marginal(9,2)) > options_.marginal_data_density.harmonic_mean.tolerance || isinf(marginal(1,2))
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fprintf('\n')
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if increase == 1
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disp('Estimation::marginal density: The support of the weighting density function is not large enough...')
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disp('Estimation::marginal density: I increase the variance of this distribution.')
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increase = 1.2*increase;
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linee = 0;
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else
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disp('Estimation::marginal density: Let me try again.')
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increase = 1.2*increase;
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linee = 0;
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if increase > 20
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check_coverage = 0;
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clear invSIGMA detSIGMA increase;
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disp('Estimation::marginal density: There''s probably a problem with the modified harmonic mean estimator.')
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end
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end
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else
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check_coverage = 0;
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clear invSIGMA detSIGMA increase;
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fprintf('Done!\n')
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end
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end
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oo_.MarginalDensity.ModifiedHarmonicMean = mean(marginal(:,2));
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return
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function oo_=fill_mh_mode(xparam1,stdh,M_,options_,estim_params_,bayestopt_,oo_, field_name)
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%function oo_=fill_mh_mode(xparam1,stdh,M_,options_,estim_params_,bayestopt_,oo_, field_name)
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%
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% INPUTS
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% o xparam1 [double] (p*1) vector of estimate parameters.
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% o stdh [double] (p*1) vector of estimate parameters.
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% o M_ Matlab's structure describing the Model (initialized by dynare, see @ref{M_}).
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% o estim_params_ Matlab's structure describing the estimated_parameters (initialized by dynare, see @ref{estim_params_}).
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% o options_ Matlab's structure describing the options (initialized by dynare, see @ref{options_}).
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% o bayestopt_ Matlab's structure describing the priors (initialized by dynare, see @ref{bayesopt_}).
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% o oo_ Matlab's structure gathering the results (initialized by dynare, see @ref{oo_}).
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%
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% OUTPUTS
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% o oo_ Matlab's structure gathering the results
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%
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% SPECIAL REQUIREMENTS
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% None.
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nvx = estim_params_.nvx; % Variance of the structural innovations (number of parameters).
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nvn = estim_params_.nvn; % Variance of the measurement innovations (number of parameters).
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ncx = estim_params_.ncx; % Covariance of the structural innovations (number of parameters).
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ncn = estim_params_.ncn; % Covariance of the measurement innovations (number of parameters).
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np = estim_params_.np ; % Number of deep parameters.
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nx = nvx+nvn+ncx+ncn+np; % Total number of parameters to be estimated.
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if np
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ip = nvx+nvn+ncx+ncn+1;
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for i=1:np
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name = bayestopt_.name{ip};
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eval(['oo_.' field_name '_mode.parameters.' name ' = xparam1(ip);']);
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eval(['oo_.' field_name '_std_at_mode.parameters.' name ' = stdh(ip);']);
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ip = ip+1;
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end
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end
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if nvx
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ip = 1;
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for i=1:nvx
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k = estim_params_.var_exo(i,1);
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name = M_.exo_names{k};
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eval(['oo_.' field_name '_mode.shocks_std.' name ' = xparam1(ip);']);
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eval(['oo_.' field_name '_std_at_mode.shocks_std.' name ' = stdh(ip);']);
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ip = ip+1;
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end
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end
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if nvn
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ip = nvx+1;
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for i=1:nvn
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name = options_.varobs{estim_params_.nvn_observable_correspondence(i,1)};
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eval(['oo_.' field_name '_mode.measurement_errors_std.' name ' = xparam1(ip);']);
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eval(['oo_.' field_name '_std_at_mode.measurement_errors_std.' name ' = stdh(ip);']);
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ip = ip+1;
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end
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end
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if ncx
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ip = nvx+nvn+1;
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for i=1:ncx
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k1 = estim_params_.corrx(i,1);
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k2 = estim_params_.corrx(i,2);
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NAME = [M_.exo_names{k1} '_' M_.exo_names{k2}];
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eval(['oo_.' field_name '_mode.shocks_corr.' NAME ' = xparam1(ip);']);
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eval(['oo_.' field_name '_std_at_mode.shocks_corr.' NAME ' = stdh(ip);']);
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ip = ip+1;
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end
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end
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if ncn
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ip = nvx+nvn+ncx+1;
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for i=1:ncn
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k1 = estim_params_.corrn(i,1);
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k2 = estim_params_.corrn(i,2);
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NAME = [M_.endo_names{k1} '_' M_.endo_names{k2}];
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eval(['oo_.' field_name '_mode.measurement_errors_corr.' NAME ' = xparam1(ip);']);
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eval(['oo_.' field_name '_std_at_mode.measurement_errors_corr.' NAME ' = stdh(ip);']);
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ip = ip+1;
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end
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end
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return |