4.1: corrected estimation results tables with long parameter names
added function row_header_width.m git-svn-id: https://www.dynare.org/svn/dynare/trunk@2570 ac1d8469-bf42-47a9-8791-bf33cf982152time-shift
michel
parent
02681122f1
commit
aa843c739d
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@ -59,7 +59,7 @@ clear record;
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pnames=[' ';'beta ';'gamm ';'norm ';'invg ';'unif ';'invg2'];
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tit2 = sprintf('%10s %7s %10s %14s %4s %6s\n',' ','prior mean','post. mean','conf. interval','prior','pstdev');
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pformat = '%12s %7.3f %8.4f %7.4f %7.4f %4s %6.4f';
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pformat = '%-*s %7.3f %8.4f %7.4f %7.4f %4s %6.4f';
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disp(' ');disp(' ');disp('ESTIMATION RESULTS');disp(' ');
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try
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@ -68,6 +68,7 @@ catch
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[marginal,oo_] = marginal_density(M_, options_, estim_params_, oo_)
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disp(sprintf('Log data density is %f.',oo_.MarginalDensity.ModifiedHarmonicMean))
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end
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header_width = row_header_width(M_,estim_params,bayestopt_);
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if np
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type = 'parameters';
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if TeX
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@ -97,6 +98,7 @@ if np
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end
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end
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disp(sprintf(pformat,name,bayestopt_.p1(ip),...
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header_width, ...
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post_mean, ...
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hpd_interval, ...
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pnames(bayestopt_.pshape(ip)+1,:), ...
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@ -144,7 +146,7 @@ if nvx
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M_.Sigma_e(k,k) = post_mean*post_mean;
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end
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end
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disp(sprintf(pformat,name,bayestopt_.p1(ip),post_mean,hpd_interval,...
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disp(sprintf(pformat,header_width,name,bayestopt_.p1(ip),post_mean,hpd_interval,...
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pnames(bayestopt_.pshape(ip)+1,:),bayestopt_.p2(ip)));
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if TeX
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TeXCore(fid,name,deblank(pnames(bayestopt_.pshape(ip)+1,:)),bayestopt_.p1(ip),...
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@ -184,7 +186,7 @@ if nvn
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oo_ = Filloo(oo_,name,type,post_mean,hpd_interval,post_median,post_var,post_deciles,density);
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end
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end
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disp(sprintf(pformat,name,bayestopt_.p1(ip),post_mean,hpd_interval, ...
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disp(sprintf(pformat,header_width,name,bayestopt_.p1(ip),post_mean,hpd_interval, ...
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pnames(bayestopt_.pshape(ip)+1,:),bayestopt_.p2(ip)));
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if TeX
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TeXCore(fid,name,deblank(pnames(bayestopt_.pshape(ip)+1,:)),bayestopt_.p1(ip),...
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@ -237,7 +239,7 @@ if ncx
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M_.Sigma_e(k2,k1) = M_.Sigma_e(k1,k2);
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end
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end
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disp(sprintf(pformat, name,bayestopt_.p1(ip),post_mean,hpd_interval, ...
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disp(sprintf(pformat, header_width,name,bayestopt_.p1(ip),post_mean,hpd_interval, ...
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pnames(bayestopt_.pshape(ip)+1,:),bayestopt_.p2(ip)));
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if TeX
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TeXCore(fid,name,deblank(pnames(bayestopt_.pshape(ip)+1,:)),bayestopt_.p1(ip),...
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@ -288,7 +290,7 @@ if ncn
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post_median,post_var,post_deciles,density);
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end
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end
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disp(sprintf(pformat, name,bayestopt_.p1(ip),post_mean,hpd_interval, ...
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disp(sprintf(pformat, header_width,name,bayestopt_.p1(ip),post_mean,hpd_interval, ...
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pnames(bayestopt_.pshape(ip)+1,:),bayestopt_.p2(ip)));
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if TeX
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TeXCore(fid,name,deblank(pnames(bayestopt_.pshape(ip)+1,:)),bayestopt_.p1(ip),...
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@ -624,7 +624,10 @@ if any(bayestopt_.pshape > 0) & options_.posterior_mode_estimation
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for i = 1:nx
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tstath(i) = abs(xparam1(i))/stdh(i);
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end
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tit1 = sprintf('%10s %7s %8s %7s %6s %4s %6s\n',' ','prior mean', ...
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header_width = row_header_width(M_,estim_params_,bayestopt_);
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tit1 = sprintf('%-*s %7s %8s %7s %6s %4s %6s\n',header_width-2,' ','prior mean', ...
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'mode','s.d.','t-stat','prior','pstdev');
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if np
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ip = nvx+nvn+ncx+ncn+1;
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@ -632,8 +635,8 @@ if any(bayestopt_.pshape > 0) & options_.posterior_mode_estimation
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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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disp(sprintf('%12s %7.3f %8.4f %7.4f %7.4f %4s %6.4f', ...
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name, ...
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disp(sprintf('%-*s %7.3f %8.4f %7.4f %7.4f %4s %6.4f', ...
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header_width,name, ...
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bayestopt_.p1(ip),xparam1(ip),stdh(ip),tstath(ip), ...
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pnames(bayestopt_.pshape(ip)+1,:), ...
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bayestopt_.p2(ip)));
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@ -649,8 +652,8 @@ if any(bayestopt_.pshape > 0) & options_.posterior_mode_estimation
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for i=1:nvx
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k = estim_params_.var_exo(i,1);
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name = deblank(M_.exo_names(k,:));
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disp(sprintf('%12s %7.3f %8.4f %7.4f %7.4f %4s %6.4f', ...
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name,bayestopt_.p1(ip),xparam1(ip), ...
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disp(sprintf('%-*s %7.3f %8.4f %7.4f %7.4f %4s %6.4f', ...
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header_width,name,bayestopt_.p1(ip),xparam1(ip), ...
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stdh(ip),tstath(ip),pnames(bayestopt_.pshape(ip)+1,:), ...
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bayestopt_.p2(ip)));
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M_.Sigma_e(k,k) = xparam1(ip)*xparam1(ip);
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@ -665,8 +668,8 @@ if any(bayestopt_.pshape > 0) & options_.posterior_mode_estimation
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ip = nvx+1;
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for i=1:nvn
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name = deblank(options_.varobs(estim_params_.var_endo(i,1),:));
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disp(sprintf('%12s %7.3f %8.4f %7.4f %7.4f %4s %6.4f', ...
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name,bayestopt_.p1(ip), ...
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disp(sprintf('%-*s %7.3f %8.4f %7.4f %7.4f %4s %6.4f', ...
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header_width,name,bayestopt_.p1(ip), ...
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xparam1(ip),stdh(ip),tstath(ip), ...
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pnames(bayestopt_.pshape(ip)+1,:), ...
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bayestopt_.p2(ip)));
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@ -684,8 +687,8 @@ if any(bayestopt_.pshape > 0) & options_.posterior_mode_estimation
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k2 = estim_params_.corrx(i,2);
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name = [deblank(M_.exo_names(k1,:)) ',' deblank(M_.exo_names(k2,:))];
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NAME = [deblank(M_.exo_names(k1,:)) '_' deblank(M_.exo_names(k2,:))];
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disp(sprintf('%12s %7.3f %8.4f %7.4f %7.4f %4s %6.4f', name, ...
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bayestopt_.p1(ip),xparam1(ip),stdh(ip),tstath(ip), ...
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disp(sprintf('%-*s %7.3f %8.4f %7.4f %7.4f %4s %6.4f', name, ...
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header_width,bayestopt_.p1(ip),xparam1(ip),stdh(ip),tstath(ip), ...
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pnames(bayestopt_.pshape(ip)+1,:), bayestopt_.p2(ip)));
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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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@ -703,8 +706,8 @@ if any(bayestopt_.pshape > 0) & options_.posterior_mode_estimation
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k2 = estim_params_.corrn(i,2);
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name = [deblank(M_.endo_names(k1,:)) ',' deblank(M_.endo_names(k2,:))];
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NAME = [deblank(M_.endo_names(k1,:)) '_' deblank(M_.endo_names(k2,:))];
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disp(sprintf('%12s %7.3f %8.4f %7.4f %7.4f %4s %6.4f', name, ...
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bayestopt_.p1(ip),xparam1(ip),stdh(ip),tstath(ip), ...
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disp(sprintf('%-*s %7.3f %8.4f %7.4f %7.4f %4s %6.4f', name, ...
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header_width,bayestopt_.p1(ip),xparam1(ip),stdh(ip),tstath(ip), ...
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pnames(bayestopt_.pshape(ip)+1,:), bayestopt_.p2(ip)));
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eval(['oo_.posterior_mode.measurement_errors_corr.' NAME ' = xparam1(ip);']);
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eval(['oo_.posterior_std.measurement_errors_corr.' NAME ' = stdh(ip);']);
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@ -733,15 +736,16 @@ elseif ~any(bayestopt_.pshape > 0) & options_.posterior_mode_estimation
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for i = 1:nx
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tstath(i) = abs(xparam1(i))/stdh(i);
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end
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tit1 = sprintf('%10s %10s %7s %6s\n',' ','Estimate','s.d.','t-stat');
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header_width = row_header_width(M_,estim_params_,bayestopt_);
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tit1 = sprintf('%-*s %10s %7s %6s\n',header_width-2,' ','Estimate','s.d.','t-stat');
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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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disp(sprintf('%12s %8.4f %7.4f %7.4f', ...
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name,xparam1(ip),stdh(ip),tstath(ip)));
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disp(sprintf('%-*s %8.4f %7.4f %7.4f', ...
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header_width,name,xparam1(ip),stdh(ip),tstath(ip)));
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eval(['oo_.mle_mode.parameters.' name ' = xparam1(ip);']);
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eval(['oo_.mle_std.parameters.' name ' = stdh(ip);']);
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ip = ip+1;
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@ -754,7 +758,7 @@ elseif ~any(bayestopt_.pshape > 0) & options_.posterior_mode_estimation
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for i=1:nvx
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k = estim_params_.var_exo(i,1);
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name = deblank(M_.exo_names(k,:));
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disp(sprintf('%12s %8.4f %7.4f %7.4f',name,xparam1(ip),stdh(ip),tstath(ip)));
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disp(sprintf('%-*s %8.4f %7.4f %7.4f',header_width,name,xparam1(ip),stdh(ip),tstath(ip)));
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M_.Sigma_e(k,k) = xparam1(ip)*xparam1(ip);
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eval(['oo_.mle_mode.shocks_std.' name ' = xparam1(ip);']);
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eval(['oo_.mle_std.shocks_std.' name ' = stdh(ip);']);
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@ -767,7 +771,7 @@ elseif ~any(bayestopt_.pshape > 0) & options_.posterior_mode_estimation
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ip = nvx+1;
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for i=1:nvn
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name = deblank(options_.varobs(estim_params_.var_endo(i,1),:));
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disp(sprintf('%12s %8.4f %7.4f %7.4f',name,xparam1(ip),stdh(ip),tstath(ip)))
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disp(sprintf('%-*s %8.4f %7.4f %7.4f',header_width,name,xparam1(ip),stdh(ip),tstath(ip)))
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eval(['oo_.mle_mode.measurement_errors_std.' name ' = xparam1(ip);']);
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eval(['oo_.mle_std.measurement_errors_std.' name ' = stdh(ip);']);
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ip = ip+1;
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@ -782,7 +786,7 @@ elseif ~any(bayestopt_.pshape > 0) & options_.posterior_mode_estimation
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k2 = estim_params_.corrx(i,2);
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name = [deblank(M_.exo_names(k1,:)) ',' deblank(M_.exo_names(k2,:))];
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NAME = [deblank(M_.exo_names(k1,:)) '_' deblank(M_.exo_names(k2,:))];
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disp(sprintf('%12s %8.4f %7.4f %7.4f', name,xparam1(ip),stdh(ip),tstath(ip)));
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disp(sprintf('%-*s %8.4f %7.4f %7.4f', header_width,name,xparam1(ip),stdh(ip),tstath(ip)));
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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_.mle_mode.shocks_corr.' NAME ' = xparam1(ip);']);
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@ -799,7 +803,7 @@ elseif ~any(bayestopt_.pshape > 0) & options_.posterior_mode_estimation
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k2 = estim_params_.corrn(i,2);
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name = [deblank(M_.endo_names(k1,:)) ',' deblank(M_.endo_names(k2,:))];
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NAME = [deblank(M_.endo_names(k1,:)) '_' deblank(M_.endo_names(k2,:))];
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disp(sprintf('%12s %8.4f %7.4f %7.4f',name,xparam1(ip),stdh(ip),tstath(ip)));
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disp(sprintf('%-*s %8.4f %7.4f %7.4f',header_width,name,xparam1(ip),stdh(ip),tstath(ip)));
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eval(['oo_.mle_mode.measurement_error_corr.' NAME ' = xparam1(ip);']);
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eval(['oo_.mle_std.measurement_error_corr.' NAME ' = stdh(ip);']);
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ip = ip+1;
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@ -1549,3 +1553,4 @@ if np > 0
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pindx = estim_params_.param_vals(:,1);
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save([M_.fname '_pindx.mat'] ,'pindx');
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end
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@ -0,0 +1,75 @@
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function w=row_header_width(M_,estim_params_,bayestopt_)
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% This function computes the width of the row headers for
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% the estimation results
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%
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% INPUTS
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% estim_params_ [structure]
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% M_ [structure]
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% bayestopt_ [structure]
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%
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% OUTPUTS
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% w integer
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%
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% SPECIAL REQUIREMENTS
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% None.
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% Copyright (C) 2006-2009 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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np = estim_params_.np;
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nvx = estim_params_.nvx;
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nvn = estim_params_.nvn;
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ncx = estim_params_.ncx;
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ncn = estim_params_.ncn;
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w = 0;
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if np
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for i=1:np
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w = max(w,length(bayestopt_.name{i}));
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end
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end
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if nvx
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for i=1:nvx
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k = estim_params_.var_exo(i,1)
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w = max(w,length(deblank(M_.exo_names(k,:))));
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end
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end
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if nvn
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for i=1:nvn
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k = estim_params_.var_endo(i,1)
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w = max(w,length(deblank(M_.endo_names(k,:))));
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end
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end
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if ncx
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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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w = max(w,length(deblank(M_.exo_names(k1,:)))...
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+length(deblank(M_.exo_names(k2,:))))
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end
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end
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if ncn
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for i=1:nvn
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k1 = estim_params_.corrn(i,1);
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k2 = estim_params_.corrn(i,2);
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w = max(w,length(deblank(M_.endo_names(k1,:)))...
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+length(deblank(M_.endo_names(k2,:))))
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end
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end
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