410 lines
17 KiB
Matlab
410 lines
17 KiB
Matlab
function oo_=display_estimation_results_table(xparam1,stdh,M_,options_,estim_params_,bayestopt_,oo_,pnames,table_title,field_name)
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%function oo_=display_results_table(xparam1,stdh,M_,estim_params_,bayestopt_,oo_,pnames,table_title,field_name)
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% Display estimation results on screen and write them to TeX-file
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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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% o pnames [string] Cell of strings storing the names for prior distributions
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% o table_title [string] Title of the Table
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% o field_name [string] String storing the name of the fields for oo_ where the parameters are stored
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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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% Copyright (C) 2014-2018 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 <http://www.gnu.org/licenses/>.
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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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skipline()
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disp(['RESULTS FROM ' upper(table_title) ' ESTIMATION'])
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LaTeXtitle=strrep(table_title,' ','_');
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tstath = abs(xparam1)./stdh;
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header_width = row_header_width(M_, estim_params_, bayestopt_);
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if strcmp(field_name,'posterior')
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tit1 = sprintf('%-*s %7s %8s %7s %4s %6s\n', header_width-2, ' ', 'prior mean', ...
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'mode', 's.d.', 'prior', 'pstdev');
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else
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tit1 = sprintf('%-*s %10s %7s %6s\n', header_width-2, ' ', 'Estimate', 's.d.', 't-stat');
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end
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if np
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ip = nvx+nvn+ncx+ncn+1;
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disp('parameters')
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disp(tit1)
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for i=1:np
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name = bayestopt_.name{ip};
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if strcmp(field_name,'posterior')
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fprintf('%-*s %7.3f %8.4f %7.4f %4s %6.4f \n', ...
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header_width,name, ...
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bayestopt_.p1(ip),xparam1(ip),stdh(ip), ...
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pnames{bayestopt_.pshape(ip)+1}, ...
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bayestopt_.p2(ip));
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else
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fprintf('%-*s %8.4f %7.4f %7.4f \n', ...
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header_width, name, xparam1(ip), stdh(ip), tstath(ip));
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end
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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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skipline()
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end
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if nvx
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ip = 1;
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disp('standard deviation of shocks')
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disp(tit1)
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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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if strcmp(field_name,'posterior')
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fprintf('%-*s %7.3f %8.4f %7.4f %4s %6.4f \n', ...
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header_width, name, bayestopt_.p1(ip), xparam1(ip), ...
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stdh(ip), pnames{bayestopt_.pshape(ip)+1}, ...
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bayestopt_.p2(ip));
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else
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fprintf('%-*s %8.4f %7.4f %7.4f \n', header_width, name, xparam1(ip), stdh(ip), tstath(ip));
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end
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M_.Sigma_e(k,k) = xparam1(ip)*xparam1(ip);
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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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skipline()
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end
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if nvn
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disp('standard deviation of measurement errors')
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disp(tit1)
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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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if strcmp(field_name,'posterior')
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fprintf('%-*s %7.3f %8.4f %7.4f %4s %6.4f \n', ...
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header_width, name, bayestopt_.p1(ip), ...
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xparam1(ip), stdh(ip), ...
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pnames{bayestopt_.pshape(ip)+1}, ...
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bayestopt_.p2(ip));
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else
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fprintf('%-*s %8.4f %7.4f %7.4f \n', header_width, name, xparam1(ip), ...
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stdh(ip), tstath(ip))
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end
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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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skipline()
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end
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if ncx
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disp('correlation of shocks')
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disp(tit1)
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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 = sprintf('%s,%s', M_.exo_names{k1}, M_.exo_names{k2});
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NAME = sprintf('%s_%s', M_.exo_names{k1}, M_.exo_names{k2});
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if strcmp(field_name, 'posterior')
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fprintf('%-*s %7.3f %8.4f %7.4f %4s %6.4f \n', ...
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header_width, name, bayestopt_.p1(ip), xparam1(ip), stdh(ip), ...
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pnames{bayestopt_.pshape(ip)+1}, bayestopt_.p2(ip));
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else
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fprintf('%-*s %8.4f %7.4f %7.4f \n', header_width,name, xparam1(ip), ...
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stdh(ip), tstath(ip));
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end
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M_.Sigma_e(k1,k2) = xparam1(ip)*sqrt(M_.Sigma_e(k1,k1)*M_.Sigma_e(k2,k2));
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M_.Sigma_e(k2,k1) = M_.Sigma_e(k1,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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skipline()
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end
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if ncn
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disp('correlation of measurement errors')
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disp(tit1)
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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 = sprintf('%s,%s', M_.endo_names{k1}, M_.endo_names{k2});
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NAME = sprintf('%s_%s', M_.endo_names{k1}, M_.endo_names{k2});
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if strcmp(field_name,'posterior')
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fprintf('%-*s %7.3f %8.4f %7.4f %4s %6.4f \n', ...
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header_width, name, bayestopt_.p1(ip), xparam1(ip), stdh(ip), ...
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pnames{bayestopt_.pshape(ip)+1}, bayestopt_.p2(ip));
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else
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fprintf('%-*s %8.4f %7.4f %7.4f \n',header_width, name, xparam1(ip), ...
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stdh(ip), tstath(ip));
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end
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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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skipline()
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end
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if any(xparam1(1:nvx+nvn)<0)
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warning('Some estimated standard deviations are negative. Dynare internally works with variances so that the sign does not matter. Nevertheless, it is recommended to impose either prior restrictions (Bayesian Estimation) or a lower bound (ML) to assure positive values.')
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end
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OutputDirectoryName = CheckPath('Output',M_.dname);
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if any(bayestopt_.pshape > 0) && options_.TeX %% Bayesian estimation (posterior mode) Latex output
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if np
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filename = [OutputDirectoryName '/' M_.fname '_Posterior_Mode_1.tex'];
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fidTeX = fopen(filename,'w');
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TeXBegin_Bayesian(fidTeX,1,'parameters')
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ip = nvx+nvn+ncx+ncn+1;
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for i=1:np
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fprintf(fidTeX,'$%s$ & %s & %7.3f & %6.4f & %8.4f & %7.4f \\\\ \n',...
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M_.param_names_tex{estim_params_.param_vals(i,1)}, ...
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pnames{bayestopt_.pshape(ip)+1}, ...
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bayestopt_.p1(ip), ...
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bayestopt_.p2(ip), ...
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xparam1(ip), ...
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stdh(ip));
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ip = ip + 1;
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end
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TeXEnd(fidTeX)
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end
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if nvx
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TeXfile = [OutputDirectoryName '/' M_.fname '_Posterior_Mode_2.tex'];
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fidTeX = fopen(TeXfile,'w');
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TeXBegin_Bayesian(fidTeX,2,'standard deviation of structural shocks')
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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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fprintf(fidTeX,[ '$%s$ & %4s & %7.3f & %6.4f & %8.4f & %7.4f \\\\ \n'],...
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M_.exo_names_tex{k},...
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pnames{bayestopt_.pshape(ip)+1},...
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bayestopt_.p1(ip),...
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bayestopt_.p2(ip),...
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xparam1(ip), ...
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stdh(ip));
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ip = ip+1;
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end
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TeXEnd(fidTeX)
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end
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if nvn
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TeXfile = [OutputDirectoryName '/' M_.fname '_Posterior_Mode_3.tex'];
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fidTeX = fopen(TeXfile,'w');
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TeXBegin_Bayesian(fidTeX,3,'standard deviation of measurement errors')
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ip = nvx+1;
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for i=1:nvn
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idx = strmatch(options_.varobs{estim_params_.nvn_observable_correspondence(i,1)}, M_.endo_names);
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fprintf(fidTeX,'$%s$ & %4s & %7.3f & %6.4f & %8.4f & %7.4f \\\\ \n',...
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M_.endo_names_tex{idx}, ...
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pnames{bayestopt_.pshape(ip)+1}, ...
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bayestopt_.p1(ip), ...
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bayestopt_.p2(ip),...
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xparam1(ip),...
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stdh(ip));
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ip = ip+1;
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end
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TeXEnd(fidTeX)
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end
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if ncx
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TeXfile = [OutputDirectoryName '/' M_.fname '_Posterior_Mode_4.tex'];
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fidTeX = fopen(TeXfile,'w');
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TeXBegin_Bayesian(fidTeX,4,'correlation of structural shocks')
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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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fprintf(fidTeX,[ '$%s$ & %s & %7.3f & %6.4f & %8.4f & %7.4f \\\\ \n'],...
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[M_.exo_names_tex{k1} ',' M_.exo_names_tex{k2}], ...
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pnames{bayestopt_.pshape(ip)+1}, ...
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bayestopt_.p1(ip), ...
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bayestopt_.p2(ip), ...
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xparam1(ip), ...
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stdh(ip));
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ip = ip+1;
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end
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TeXEnd(fidTeX)
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end
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if ncn
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TeXfile = [OutputDirectoryName '/' M_.fname '_Posterior_Mode_5.tex'];
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fidTeX = fopen(TeXfile,'w');
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TeXBegin_Bayesian(fidTeX,5,'correlation of measurement errors')
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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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fprintf(fidTeX,'$%s$ & %s & %7.3f & %6.4f & %8.4f & %7.4f \\\\ \n',...
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[ M_.endo_names_tex{k1} ',' M_.endo_names_tex{k2}], ...
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pnames{bayestopt_.pshape(ip)+1}, ...
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bayestopt_.p1(ip), ...
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bayestopt_.p2(ip), ...
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xparam1(ip), ...
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stdh(ip));
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ip = ip+1;
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end
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TeXEnd(fidTeX)
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end
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elseif all(bayestopt_.pshape == 0) && options_.TeX %% MLE and GMM Latex output
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if np
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filename = [OutputDirectoryName '/' M_.fname '_' LaTeXtitle '_Mode_1.tex'];
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fidTeX = fopen(filename, 'w');
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TeXBegin_ML(fidTeX, 1, 'parameters', table_title, LaTeXtitle)
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ip = nvx+nvn+ncx+ncn+1;
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for i=1:np
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fprintf(fidTeX,'$%s$ & %8.4f & %7.4f & %7.4f\\\\ \n',...
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M_.param_names_tex{estim_params_.param_vals(i,1)}, ...
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xparam1(ip), ...
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stdh(ip), ...
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tstath(ip));
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ip = ip + 1;
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end
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TeXEnd(fidTeX)
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end
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if nvx
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filename = [OutputDirectoryName '/' M_.fname '_' LaTeXtitle '_Mode_2.tex'];
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fidTeX = fopen(filename, 'w');
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TeXBegin_ML(fidTeX, 2, 'standard deviation of structural shocks', table_title, LaTeXtitle)
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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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fprintf(fidTeX,[ '$%s$ & %8.4f & %7.4f & %7.4f\\\\ \n'], ...
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M_.exo_names_tex{k}, ...
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xparam1(ip), ...
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stdh(ip), ...
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tstath(ip));
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ip = ip+1;
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end
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TeXEnd(fidTeX)
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end
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if nvn
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filename = [OutputDirectoryName '/' M_.fname '_' LaTeXtitle '_Mode_3.tex'];
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fidTeX = fopen(filename, 'w');
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TeXBegin_ML(fidTeX, 3, 'standard deviation of measurement errors', table_title, LaTeXtitle)
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ip = nvx+1;
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for i=1:nvn
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idx = strmatch(options_.varobs{estim_params_.nvn_observable_correspondence(i,1)}, M_.endo_names);
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fprintf(fidTeX, '$%s$ & %8.4f & %7.4f & %7.4f \\\\ \n', ...
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M_.endo_names_tex{idx}, ...
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xparam1(ip), ...
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stdh(ip), ...
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tstath(ip));
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ip = ip+1;
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end
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TeXEnd(fidTeX)
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end
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if ncx
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filename = [OutputDirectoryName '/' M_.fname '_' LaTeXtitle '_Mode_4.tex'];
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fidTeX = fopen(filename, 'w');
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TeXBegin_ML(fidTeX, 4, 'correlation of structural shocks', table_title,LaTeXtitle)
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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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fprintf(fidTeX,[ '$%s$ & %8.4f & %7.4f & %7.4f \\\\ \n'], ...
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[M_.exo_names_tex{k1} ',' M_.exo_names_tex{k2}], ...
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xparam1(ip), ...
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stdh(ip), ...
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tstath(ip));
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ip = ip+1;
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end
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TeXEnd(fidTeX)
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end
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if ncn
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filename = [OutputDirectoryName '/' M_.fname '_' LaTeXtitle '_Mode_5.tex'];
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fidTeX = fopen(filename, 'w');
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TeXBegin_ML(fidTeX, 5, 'correlation of measurement errors', table_title, LaTeXtitle)
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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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fprintf(fidTeX, '$%s$ & %8.4f & %7.4f & %7.4f \\\\ \n', ...
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[ M_.endo_names_tex{k1} ',' M_.endo_names_tex{k2}], ...
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xparam1(ip), ...
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stdh(ip), ...
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tstath(ip));
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ip = ip+1;
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end
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TeXEnd(fidTeX)
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end
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end
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%% subfunctions:
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%
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function TeXBegin_Bayesian(fid, fnum, title)
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fprintf(fid,'%% TeX-table generated by dynare_estimation (Dynare).\n');
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fprintf(fid,['%% RESULTS FROM POSTERIOR MAXIMIZATION (' title ')\n']);
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fprintf(fid,['%% ' datestr(now,0)]);
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fprintf(fid,' \n');
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fprintf(fid,' \n');
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fprintf(fid,'\\begin{center}\n');
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fprintf(fid,'\\begin{longtable}{llcccc} \n');
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fprintf(fid,['\\caption{Results from posterior maximization (' title ')}\\\\\n ']);
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fprintf(fid,['\\label{Table:Posterior:' int2str(fnum) '}\\\\\n']);
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fprintf(fid,'\\toprule \n');
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fprintf(fid,' & \\multicolumn{3}{c}{Prior} & \\multicolumn{2}{c}{Posterior} \\\\\n');
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fprintf(fid,' \\cmidrule(r{.75em}){2-4} \\cmidrule(r{.75em}){5-6}\n');
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fprintf(fid,' & Dist. & Mean & Stdev & Mode & Stdev \\\\ \n');
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fprintf(fid,'\\midrule \\endfirsthead \n');
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fprintf(fid,'\\caption{(continued)}\\\\\n ');
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fprintf(fid,'\\bottomrule \n');
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fprintf(fid,' & \\multicolumn{3}{c}{Prior} & \\multicolumn{2}{c}{Posterior} \\\\\n');
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fprintf(fid,' \\cmidrule(r{.75em}){2-4} \\cmidrule(r{.75em}){5-6}\n');
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fprintf(fid,' & Dist. & Mean & Stdev & Mode & Stdev \\\\ \n');
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fprintf(fid,'\\midrule \\endhead \n');
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fprintf(fid,'\\bottomrule \\multicolumn{6}{r}{(Continued on next page)}\\endfoot \n');
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fprintf(fid,'\\bottomrule\\endlastfoot \n');
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function TeXBegin_ML(fid, fnum, title, table_title, LaTeXtitle)
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fprintf(fid,'%% TeX-table generated by dynare_estimation (Dynare).\n');
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fprintf(fid,['%% RESULTS FROM ' table_title ' MAXIMIZATION (' title ')\n']);
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fprintf(fid,['%% ' datestr(now,0)]);
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fprintf(fid,' \n');
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fprintf(fid,' \n');
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fprintf(fid,'\\begin{center}\n');
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fprintf(fid,'\\begin{longtable}{llcc} \n');
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fprintf(fid,['\\caption{Results from ' table_title ' maximization (' title ')}\\\\\n ']);
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fprintf(fid,['\\label{Table:' LaTeXtitle ':' int2str(fnum) '}\\\\\n']);
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fprintf(fid,'\\toprule \n');
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fprintf(fid,' & Mode & s.d. & t-stat\\\\ \n');
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fprintf(fid,'\\midrule \\endfirsthead \n');
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fprintf(fid,'\\caption{(continued)}\\\\\n ');
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fprintf(fid,'\\toprule \n');
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fprintf(fid,' & Mode & s.d. & t-stat\\\\ \n');
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fprintf(fid,'\\midrule \\endhead \n');
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fprintf(fid,'\\bottomrule \\multicolumn{4}{r}{(Continued on next page)} \\endfoot \n');
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fprintf(fid,'\\bottomrule \\endlastfoot \n');
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|
|
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function TeXEnd(fid)
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fprintf(fid,'\\end{longtable}\n ');
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fprintf(fid,'\\end{center}\n');
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fprintf(fid,'%% End of TeX file.\n');
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fclose(fid); |