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function oo_=display_estimation_results_table(xparam1,stdh,M_,options_,estim_params_,bayestopt_,oo_,pnames,table_title,field_name)
%function oo_=display_results_table(xparam1,stdh,M_,estim_params_,bayestopt_,oo_,pnames,table_title,field_name)
% Display estimation results on screen and write them to TeX-file
%
% INPUTS
% o xparam1 [double] (p*1) vector of estimate parameters.
% o stdh [double] (p*1) vector of estimate parameters.
% o M_ Matlab's structure describing the Model (initialized by dynare, see @ref{M_}).
% o estim_params_ Matlab's structure describing the estimated_parameters (initialized by dynare, see @ref{estim_params_}).
% o options_ Matlab's structure describing the options (initialized by dynare, see @ref{options_}).
% o bayestopt_ Matlab's structure describing the priors (initialized by dynare, see @ref{bayesopt_}).
% o oo_ Matlab's structure gathering the results (initialized by dynare, see @ref{oo_}).
% o pnames [string] Cell of strings storing the names for prior distributions
% o table_title [string] Title of the Table
% o field_name [string] String storing the name of the fields for oo_ where the parameters are stored
%
% OUTPUTS
% o oo_ Matlab's structure gathering the results
%
% SPECIAL REQUIREMENTS
% None.
% Copyright (C) 2014-2018 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/>.
nvx = estim_params_.nvx; % Variance of the structural innovations (number of parameters).
nvn = estim_params_.nvn; % Variance of the measurement innovations (number of parameters).
ncx = estim_params_.ncx; % Covariance of the structural innovations (number of parameters).
ncn = estim_params_.ncn; % Covariance of the measurement innovations (number of parameters).
np = estim_params_.np ; % Number of deep parameters.
nx = nvx+nvn+ncx+ncn+np; % Total number of parameters to be estimated.
skipline()
disp(['RESULTS FROM ' upper(table_title) ' ESTIMATION'])
LaTeXtitle=strrep(table_title,' ','_');
tstath = abs(xparam1)./stdh;
header_width = row_header_width(M_, estim_params_, bayestopt_);
if strcmp(field_name,'posterior')
tit1 = sprintf('%-*s %7s %8s %7s %4s %6s\n', header_width-2, ' ', 'prior mean', ...
'mode', 's.d.', 'prior', 'pstdev');
else
tit1 = sprintf('%-*s %10s %7s %6s\n', header_width-2, ' ', 'Estimate', 's.d.', 't-stat');
end
if np
ip = nvx+nvn+ncx+ncn+1;
disp('parameters')
disp(tit1)
for i=1:np
name = bayestopt_.name{ip};
if strcmp(field_name,'posterior')
fprintf('%-*s %7.3f %8.4f %7.4f %4s %6.4f \n', ...
header_width,name, ...
bayestopt_.p1(ip),xparam1(ip),stdh(ip), ...
pnames{bayestopt_.pshape(ip)+1}, ...
bayestopt_.p2(ip));
else
fprintf('%-*s %8.4f %7.4f %7.4f \n', ...
header_width, name, xparam1(ip), stdh(ip), tstath(ip));
end
eval(['oo_.' field_name '_mode.parameters.' name ' = xparam1(ip);']);
eval(['oo_.' field_name '_std_at_mode.parameters.' name ' = stdh(ip);']);
ip = ip+1;
end
skipline()
end
if nvx
ip = 1;
disp('standard deviation of shocks')
disp(tit1)
for i=1:nvx
k = estim_params_.var_exo(i,1);
name = M_.exo_names{k};
if strcmp(field_name,'posterior')
fprintf('%-*s %7.3f %8.4f %7.4f %4s %6.4f \n', ...
header_width, name, bayestopt_.p1(ip), xparam1(ip), ...
stdh(ip), pnames{bayestopt_.pshape(ip)+1}, ...
bayestopt_.p2(ip));
else
fprintf('%-*s %8.4f %7.4f %7.4f \n', header_width, name, xparam1(ip), stdh(ip), tstath(ip));
end
M_.Sigma_e(k,k) = xparam1(ip)*xparam1(ip);
eval(['oo_.' field_name '_mode.shocks_std.' name ' = xparam1(ip);']);
eval(['oo_.' field_name '_std_at_mode.shocks_std.' name ' = stdh(ip);']);
ip = ip+1;
end
skipline()
end
if nvn
disp('standard deviation of measurement errors')
disp(tit1)
ip = nvx+1;
for i=1:nvn
name = options_.varobs{estim_params_.nvn_observable_correspondence(i,1)};
if strcmp(field_name,'posterior')
fprintf('%-*s %7.3f %8.4f %7.4f %4s %6.4f \n', ...
header_width, name, bayestopt_.p1(ip), ...
xparam1(ip), stdh(ip), ...
pnames{bayestopt_.pshape(ip)+1}, ...
bayestopt_.p2(ip));
else
fprintf('%-*s %8.4f %7.4f %7.4f \n', header_width, name, xparam1(ip), ...
stdh(ip), tstath(ip))
end
eval(['oo_.' field_name '_mode.measurement_errors_std.' name ' = xparam1(ip);']);
eval(['oo_.' field_name '_std_at_mode.measurement_errors_std.' name ' = stdh(ip);']);
ip = ip+1;
end
skipline()
end
if ncx
disp('correlation of shocks')
disp(tit1)
ip = nvx+nvn+1;
for i=1:ncx
k1 = estim_params_.corrx(i,1);
k2 = estim_params_.corrx(i,2);
name = sprintf('%s,%s', M_.exo_names{k1}, M_.exo_names{k2});
NAME = sprintf('%s_%s', M_.exo_names{k1}, M_.exo_names{k2});
if strcmp(field_name, 'posterior')
fprintf('%-*s %7.3f %8.4f %7.4f %4s %6.4f \n', ...
header_width, name, bayestopt_.p1(ip), xparam1(ip), stdh(ip), ...
pnames{bayestopt_.pshape(ip)+1}, bayestopt_.p2(ip));
else
fprintf('%-*s %8.4f %7.4f %7.4f \n', header_width,name, xparam1(ip), ...
stdh(ip), tstath(ip));
end
M_.Sigma_e(k1,k2) = xparam1(ip)*sqrt(M_.Sigma_e(k1,k1)*M_.Sigma_e(k2,k2));
M_.Sigma_e(k2,k1) = M_.Sigma_e(k1,k2);
eval(['oo_.' field_name '_mode.shocks_corr.' NAME ' = xparam1(ip);']);
eval(['oo_.' field_name '_std_at_mode.shocks_corr.' NAME ' = stdh(ip);']);
ip = ip+1;
end
skipline()
end
if ncn
disp('correlation of measurement errors')
disp(tit1)
ip = nvx+nvn+ncx+1;
for i=1:ncn
k1 = estim_params_.corrn(i,1);
k2 = estim_params_.corrn(i,2);
name = sprintf('%s,%s', M_.endo_names{k1}, M_.endo_names{k2});
NAME = sprintf('%s_%s', M_.endo_names{k1}, M_.endo_names{k2});
if strcmp(field_name,'posterior')
fprintf('%-*s %7.3f %8.4f %7.4f %4s %6.4f \n', ...
header_width, name, bayestopt_.p1(ip), xparam1(ip), stdh(ip), ...
pnames{bayestopt_.pshape(ip)+1}, bayestopt_.p2(ip));
else
fprintf('%-*s %8.4f %7.4f %7.4f \n',header_width, name, xparam1(ip), ...
stdh(ip), tstath(ip));
end
eval(['oo_.' field_name '_mode.measurement_errors_corr.' NAME ' = xparam1(ip);']);
eval(['oo_.' field_name '_std_at_mode.measurement_errors_corr.' NAME ' = stdh(ip);']);
ip = ip+1;
end
skipline()
end
if any(xparam1(1:nvx+nvn)<0)
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.')
end
OutputDirectoryName = CheckPath('Output',M_.dname);
if any(bayestopt_.pshape > 0) && options_.TeX %% Bayesian estimation (posterior mode) Latex output
if np
filename = [OutputDirectoryName '/' M_.fname '_Posterior_Mode_1.tex'];
fidTeX = fopen(filename,'w');
TeXBegin_Bayesian(fidTeX,1,'parameters')
ip = nvx+nvn+ncx+ncn+1;
for i=1:np
fprintf(fidTeX,'$%s$ & %s & %7.3f & %6.4f & %8.4f & %7.4f \\\\ \n',...
M_.param_names_tex{estim_params_.param_vals(i,1)}, ...
pnames{bayestopt_.pshape(ip)+1}, ...
bayestopt_.p1(ip), ...
bayestopt_.p2(ip), ...
xparam1(ip), ...
stdh(ip));
ip = ip + 1;
end
TeXEnd(fidTeX)
end
if nvx
TeXfile = [OutputDirectoryName '/' M_.fname '_Posterior_Mode_2.tex'];
fidTeX = fopen(TeXfile,'w');
TeXBegin_Bayesian(fidTeX,2,'standard deviation of structural shocks')
ip = 1;
for i=1:nvx
k = estim_params_.var_exo(i,1);
fprintf(fidTeX,[ '$%s$ & %4s & %7.3f & %6.4f & %8.4f & %7.4f \\\\ \n'],...
M_.exo_names_tex{k},...
pnames{bayestopt_.pshape(ip)+1},...
bayestopt_.p1(ip),...
bayestopt_.p2(ip),...
xparam1(ip), ...
stdh(ip));
ip = ip+1;
end
TeXEnd(fidTeX)
end
if nvn
TeXfile = [OutputDirectoryName '/' M_.fname '_Posterior_Mode_3.tex'];
fidTeX = fopen(TeXfile,'w');
TeXBegin_Bayesian(fidTeX,3,'standard deviation of measurement errors')
ip = nvx+1;
for i=1:nvn
idx = strmatch(options_.varobs{estim_params_.nvn_observable_correspondence(i,1)}, M_.endo_names);
fprintf(fidTeX,'$%s$ & %4s & %7.3f & %6.4f & %8.4f & %7.4f \\\\ \n',...
M_.endo_names_tex{idx}, ...
pnames{bayestopt_.pshape(ip)+1}, ...
bayestopt_.p1(ip), ...
bayestopt_.p2(ip),...
xparam1(ip),...
stdh(ip));
ip = ip+1;
end
TeXEnd(fidTeX)
end
if ncx
TeXfile = [OutputDirectoryName '/' M_.fname '_Posterior_Mode_4.tex'];
fidTeX = fopen(TeXfile,'w');
TeXBegin_Bayesian(fidTeX,4,'correlation of structural shocks')
ip = nvx+nvn+1;
for i=1:ncx
k1 = estim_params_.corrx(i,1);
k2 = estim_params_.corrx(i,2);
fprintf(fidTeX,[ '$%s$ & %s & %7.3f & %6.4f & %8.4f & %7.4f \\\\ \n'],...
[M_.exo_names_tex{k1} ',' M_.exo_names_tex{k2}], ...
pnames{bayestopt_.pshape(ip)+1}, ...
bayestopt_.p1(ip), ...
bayestopt_.p2(ip), ...
xparam1(ip), ...
stdh(ip));
ip = ip+1;
end
TeXEnd(fidTeX)
end
if ncn
TeXfile = [OutputDirectoryName '/' M_.fname '_Posterior_Mode_5.tex'];
fidTeX = fopen(TeXfile,'w');
TeXBegin_Bayesian(fidTeX,5,'correlation of measurement errors')
ip = nvx+nvn+ncx+1;
for i=1:ncn
k1 = estim_params_.corrn(i,1);
k2 = estim_params_.corrn(i,2);
fprintf(fidTeX,'$%s$ & %s & %7.3f & %6.4f & %8.4f & %7.4f \\\\ \n',...
[ M_.endo_names_tex{k1} ',' M_.endo_names_tex{k2}], ...
pnames{bayestopt_.pshape(ip)+1}, ...
bayestopt_.p1(ip), ...
bayestopt_.p2(ip), ...
xparam1(ip), ...
stdh(ip));
ip = ip+1;
end
TeXEnd(fidTeX)
end
elseif all(bayestopt_.pshape == 0) && options_.TeX %% MLE and GMM Latex output
if np
filename = [OutputDirectoryName '/' M_.fname '_' LaTeXtitle '_Mode_1.tex'];
fidTeX = fopen(filename, 'w');
TeXBegin_ML(fidTeX, 1, 'parameters', table_title, LaTeXtitle)
ip = nvx+nvn+ncx+ncn+1;
for i=1:np
fprintf(fidTeX,'$%s$ & %8.4f & %7.4f & %7.4f\\\\ \n',...
M_.param_names_tex{estim_params_.param_vals(i,1)}, ...
xparam1(ip), ...
stdh(ip), ...
tstath(ip));
ip = ip + 1;
end
TeXEnd(fidTeX)
end
if nvx
filename = [OutputDirectoryName '/' M_.fname '_' LaTeXtitle '_Mode_2.tex'];
fidTeX = fopen(filename, 'w');
TeXBegin_ML(fidTeX, 2, 'standard deviation of structural shocks', table_title, LaTeXtitle)
ip = 1;
for i=1:nvx
k = estim_params_.var_exo(i,1);
fprintf(fidTeX,[ '$%s$ & %8.4f & %7.4f & %7.4f\\\\ \n'], ...
M_.exo_names_tex{k}, ...
xparam1(ip), ...
stdh(ip), ...
tstath(ip));
ip = ip+1;
end
TeXEnd(fidTeX)
end
if nvn
filename = [OutputDirectoryName '/' M_.fname '_' LaTeXtitle '_Mode_3.tex'];
fidTeX = fopen(filename, 'w');
TeXBegin_ML(fidTeX, 3, 'standard deviation of measurement errors', table_title, LaTeXtitle)
ip = nvx+1;
for i=1:nvn
idx = strmatch(options_.varobs{estim_params_.nvn_observable_correspondence(i,1)}, M_.endo_names);
fprintf(fidTeX, '$%s$ & %8.4f & %7.4f & %7.4f \\\\ \n', ...
M_.endo_names_tex{idx}, ...
xparam1(ip), ...
stdh(ip), ...
tstath(ip));
ip = ip+1;
end
TeXEnd(fidTeX)
end
if ncx
filename = [OutputDirectoryName '/' M_.fname '_' LaTeXtitle '_Mode_4.tex'];
fidTeX = fopen(filename, 'w');
TeXBegin_ML(fidTeX, 4, 'correlation of structural shocks', table_title,LaTeXtitle)
ip = nvx+nvn+1;
for i=1:ncx
k1 = estim_params_.corrx(i,1);
k2 = estim_params_.corrx(i,2);
fprintf(fidTeX,[ '$%s$ & %8.4f & %7.4f & %7.4f \\\\ \n'], ...
[M_.exo_names_tex{k1} ',' M_.exo_names_tex{k2}], ...
xparam1(ip), ...
stdh(ip), ...
tstath(ip));
ip = ip+1;
end
TeXEnd(fidTeX)
end
if ncn
filename = [OutputDirectoryName '/' M_.fname '_' LaTeXtitle '_Mode_5.tex'];
fidTeX = fopen(filename, 'w');
TeXBegin_ML(fidTeX, 5, 'correlation of measurement errors', table_title, LaTeXtitle)
ip = nvx+nvn+ncx+1;
for i=1:ncn
k1 = estim_params_.corrn(i,1);
k2 = estim_params_.corrn(i,2);
fprintf(fidTeX, '$%s$ & %8.4f & %7.4f & %7.4f \\\\ \n', ...
[ M_.endo_names_tex{k1} ',' M_.endo_names_tex{k2}], ...
xparam1(ip), ...
stdh(ip), ...
tstath(ip));
ip = ip+1;
end
TeXEnd(fidTeX)
end
end
%% subfunctions:
%
function TeXBegin_Bayesian(fid, fnum, title)
fprintf(fid,'%% TeX-table generated by dynare_estimation (Dynare).\n');
fprintf(fid,['%% RESULTS FROM POSTERIOR MAXIMIZATION (' title ')\n']);
fprintf(fid,['%% ' datestr(now,0)]);
fprintf(fid,' \n');
fprintf(fid,' \n');
fprintf(fid,'\\begin{center}\n');
fprintf(fid,'\\begin{longtable}{llcccc} \n');
fprintf(fid,['\\caption{Results from posterior maximization (' title ')}\\\\\n ']);
fprintf(fid,['\\label{Table:Posterior:' int2str(fnum) '}\\\\\n']);
fprintf(fid,'\\toprule \n');
fprintf(fid,' & \\multicolumn{3}{c}{Prior} & \\multicolumn{2}{c}{Posterior} \\\\\n');
fprintf(fid,' \\cmidrule(r{.75em}){2-4} \\cmidrule(r{.75em}){5-6}\n');
fprintf(fid,' & Dist. & Mean & Stdev & Mode & Stdev \\\\ \n');
fprintf(fid,'\\midrule \\endfirsthead \n');
fprintf(fid,'\\caption{(continued)}\\\\\n ');
fprintf(fid,'\\bottomrule \n');
fprintf(fid,' & \\multicolumn{3}{c}{Prior} & \\multicolumn{2}{c}{Posterior} \\\\\n');
fprintf(fid,' \\cmidrule(r{.75em}){2-4} \\cmidrule(r{.75em}){5-6}\n');
fprintf(fid,' & Dist. & Mean & Stdev & Mode & Stdev \\\\ \n');
fprintf(fid,'\\midrule \\endhead \n');
fprintf(fid,'\\bottomrule \\multicolumn{6}{r}{(Continued on next page)}\\endfoot \n');
fprintf(fid,'\\bottomrule\\endlastfoot \n');
function TeXBegin_ML(fid, fnum, title, table_title, LaTeXtitle)
fprintf(fid,'%% TeX-table generated by dynare_estimation (Dynare).\n');
fprintf(fid,['%% RESULTS FROM ' table_title ' MAXIMIZATION (' title ')\n']);
fprintf(fid,['%% ' datestr(now,0)]);
fprintf(fid,' \n');
fprintf(fid,' \n');
fprintf(fid,'\\begin{center}\n');
fprintf(fid,'\\begin{longtable}{llcc} \n');
fprintf(fid,['\\caption{Results from ' table_title ' maximization (' title ')}\\\\\n ']);
fprintf(fid,['\\label{Table:' LaTeXtitle ':' int2str(fnum) '}\\\\\n']);
fprintf(fid,'\\toprule \n');
fprintf(fid,' & Mode & s.d. & t-stat\\\\ \n');
fprintf(fid,'\\midrule \\endfirsthead \n');
fprintf(fid,'\\caption{(continued)}\\\\\n ');
fprintf(fid,'\\toprule \n');
fprintf(fid,' & Mode & s.d. & t-stat\\\\ \n');
fprintf(fid,'\\midrule \\endhead \n');
fprintf(fid,'\\bottomrule \\multicolumn{4}{r}{(Continued on next page)} \\endfoot \n');
fprintf(fid,'\\bottomrule \\endlastfoot \n');
function TeXEnd(fid)
fprintf(fid,'\\end{longtable}\n ');
fprintf(fid,'\\end{center}\n');
fprintf(fid,'%% End of TeX file.\n');
fclose(fid);
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