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dynare/matlab/dynare_estimation.m

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function dynare_estimation(var_list_)
global M_ options_ oo_ estim_params_
global bayestopt_ trend_coeff_
options_.varlist = var_list_;
options_.lgyidx2varobs = zeros(size(M_.endo_names,1),1);
for i = 1:size(M_.endo_names,1)
tmp = strmatch(deblank(M_.endo_names(i,:)),options_.varobs,'exact');
if ~isempty(tmp)
options_.lgyidx2varobs(i,1) = tmp;
end
end
options_ = set_default_option(options_,'first_obs',1);
options_ = set_default_option(options_,'prefilter',0);
options_ = set_default_option(options_,'presample',0);
options_ = set_default_option(options_,'lik_algo',1);
options_ = set_default_option(options_,'lik_init',1);
options_ = set_default_option(options_,'nograph',0);
options_ = set_default_option(options_,'mh_conf_sig',0.90);
options_ = set_default_option(options_,'mh_replic',20000);
options_ = set_default_option(options_,'mh_drop',0.5);
options_ = set_default_option(options_,'mh_jscale',0.2);
options_ = set_default_option(options_,'mh_init_scale',2*options_.mh_jscale);
options_ = set_default_option(options_,'mode_file','');
options_ = set_default_option(options_,'mode_compute',4);
options_ = set_default_option(options_,'mode_check',0);
options_ = set_default_option(options_,'prior_trunc',1e-10);
options_ = set_default_option(options_,'mh_mode',1);
options_ = set_default_option(options_,'mh_nblck',2);
options_ = set_default_option(options_,'load_mh_file',0);
options_ = set_default_option(options_,'nodiagnostic',0);
options_ = set_default_option(options_,'loglinear',0);
options_ = set_default_option(options_,'unit_root_vars',[]);
options_ = set_default_option(options_,'XTick',[]);
options_ = set_default_option(options_,'XTickLabel',[]);
options_ = set_default_option(options_,'bayesian_irf',0);
options_ = set_default_option(options_,'bayesian_th_moments',0);
options_ = set_default_option(options_,'TeX',0);
options_ = set_default_option(options_,'irf',0);
options_ = set_default_option(options_,'relative_irf',0);
options_ = set_default_option(options_,'order',1);
options_ = set_default_option(options_,'ar',5);
options_ = set_default_option(options_,'dr_algo',0);
options_ = set_default_option(options_,'linear',1);
options_ = set_default_option(options_,'drop',0);
options_ = set_default_option(options_,'replic',1);
options_ = set_default_option(options_,'hp_filter',0);
options_ = set_default_option(options_,'forecast',0);
options_ = set_default_option(options_,'smoother',0);
options_ = set_default_option(options_,'moments_varendo',0);
options_ = set_default_option(options_,'filtered_vars',0);
options_ = set_default_option(options_,'kalman_algo',1);
options_ = set_default_option(options_,'kalman_tol',10^(-12));
options_ = set_default_option(options_,'posterior_mode_estimation',1);
options_ = set_default_option(options_,'MaxNumberOfBytes',10000);
%% Add something to the parser ++>
M_.dname = M_.fname; % The user should be able to choose another name
% for the directory...
optim_options = optimset('display','iter','LargeScale','off', ...
'MaxFunEvals',100000,'TolFun',1e-8,'TolX',1e-6);
if isfield(options_,'optim_opt')
eval(['optim_options = optimset(optim_options,' options_.optim_opt ');']);
end
pnames = [' ';'beta ';'gamm ';'norm ';'invg ';'unif ';'invg2'];
n_varobs = size(options_.varobs,1);
[xparam1,estim_params_,bayestopt_,lb,ub] = set_prior(estim_params_);
if any(bayestopt_.pshape > 0)
plot_priors
else
options_.mh_replic = 0;
end
bounds = prior_bounds(bayestopt_);
bounds(:,1)=max(bounds(:,1),lb);
bounds(:,2)=min(bounds(:,2),ub);
if any(xparam1 < bounds(:,1)) | any(xparam1 > bounds(:,2))
find(xparam1 < bounds(:,1))
find(xparam1 > bounds(:,2))
error('Initial parameter values are outside parameter bounds')
end
lb = bounds(:,1);
ub = bounds(:,2);
bayestopt_.lb = lb;
bayestopt_.ub = ub;
if isempty(trend_coeff_)
bayestopt_.with_trend = 0;
else
bayestopt_.with_trend = 1;
bayestopt_.trend_coeff = {};
for i=1:n_varobs
if i > length(trend_coeff_) | isempty(trend_coeff_{i})
bayestopt_.trend_coeff{i} = '0';
else
bayestopt_.trend_coeff{i} = trend_coeff_{i};
end
end
end
bayestopt_.penalty = 1e8; % penalty
nvx = estim_params_.nvx;
nvn = estim_params_.nvn;
ncx = estim_params_.ncx;
ncn = estim_params_.ncn;
np = estim_params_.np ;
nx = nvx+nvn+ncx+ncn+np;
%% Static solver
if exist([M_.fname '_steadystate'])
bayestopt_.static_solve = [M_.fname '_steadystate'];
else
bayestopt_.static_solve = 'dynare_solve';
end
dr = set_state_space([]);
%% Initialization with unit-root variables
if ~isempty(options_.unit_root_vars)
n_ur = size(options_.unit_root_vars,1);
i_ur = zeros(n_ur,1);
for i=1:n_ur
i1 = strmatch(deblank(options_.unit_root_vars(i,:)),M_.endo_names(dr.order_var,:),'exact');
if isempty(i1)
error('Undeclared variable in unit_root_vars statement')
end
i_ur(i) = i1;
end
if M_.maximum_lag > 1
l1 = flipud([cumsum(M_.lead_lag_incidence(1:M_.maximum_lag-1,dr.order_var),1);ones(1,M_.endo_nbr)]);
n1 = nnz(l1);
bayestopt_.Pinf = zeros(n1,n1);
l2 = find(l1');
l3 = zeros(M_.endo_nbr,M_.maximum_lag);
l3(i_ur,:) = l1(:,i_ur)';
l3 = l3(:);
i_ur1 = find(l3(l2));
i_stable = ones(M_.endo_nbr,1);
i_stable(i_ur) = zeros(n_ur,1);
i_stable = find(i_stable);
bayestopt_.Pinf(i_ur1,i_ur1) = diag(ones(1,length(i_ur1)));
bayestopt_.i_var_stable = i_stable;
l3 = zeros(M_.endo_nbr,M_.maximum_lag);
l3(i_stable,:) = l1(:,i_stable)';
l3 = l3(:);
bayestopt_.i_T_var_stable = find(l3(l2));
else
n1 = M_.endo_nbr;
bayestopt_.Pinf = zeros(n1,n1);
bayestopt_.Pinf(i_ur,i_ur) = diag(ones(1,length(i_ur)));
l1 = ones(M_.endo_nbr,1);
l1(i_ur,:) = zeros(length(i_ur),1);
bayestopt_.i_T_var_stable = find(l1);
end
options_.lik_init = 3;
end % if ~isempty(options_.unit_root_vars)
if isempty(options_.datafile)
error('ESTIMATION: datafile option is missing')
end
if isempty(options_.varobs)
error('ESTIMATION: VAROBS is missing')
end
%% If jscale isn't specified for an estimated parameter, use
%% global option options_.jscale, set to 0.2, by default
k = find(isnan(bayestopt_.jscale));
bayestopt_.jscale(k) = options_.mh_jscale;
%% Read and demean data
if exist(options_.datafile)
instr = options_.datafile;
else
instr = ['load ' options_.datafile];
end
eval(instr);
rawdata = [];
k = [];
k1 = [];
for i=1:n_varobs
rawdata = [rawdata eval(deblank(options_.varobs(i,:)))];
k = [k strmatch(deblank(options_.varobs(i,:)),M_.endo_names(dr.order_var,:), ...
'exact')];
k1 = [k1 strmatch(deblank(options_.varobs(i,:)),M_.endo_names, 'exact')];
end
bayestopt_.mf = k;
bayestopt_.mfys = k1;
options_ = set_default_option(options_,'nobs',size(rawdata,1)-options_.first_obs+1);
gend = options_.nobs;
rawdata = rawdata(options_.first_obs:options_.first_obs+gend-1,:);
if options_.loglinear == 1
rawdata = log(rawdata);
end
if options_.prefilter == 1
bayestopt_.mean_varobs = mean(rawdata,1);
data = transpose(rawdata-ones(gend,1)*bayestopt_.mean_varobs);
else
data = transpose(rawdata);
end
if ~isreal(rawdata)
error(['There are complex values in the data. Probably a wrong' ...
' transformation'])
end
if length(options_.mode_file) > 0 & options_.posterior_mode_estimation
eval(['load ' options_.mode_file ';']');
end
initial_estimation_checks(xparam1,gend,data);
%% Estimation of the posterior mode or likelihood mode
if options_.mode_compute > 0 & options_.posterior_mode_estimation
fh=str2func('DsgeLikelihood');
if options_.mode_compute == 1
[xparam1,fval,exitflag,output,lamdba,grad,hessian_fmincon] = ...
fmincon(fh,xparam1,[],[],[],[],lb,ub,[],optim_options,gend,data);
elseif options_.mode_compute == 2
% asamin('set','maximum_cost_repeat',0);
[fval,xparam1,grad,hessian_asamin,exitflag] = ...
asamin('minimize','DsgeLikelihood',xparam1,lb,ub,- ...
ones(size(xparam1)),gend,data);
elseif options_.mode_compute == 3
[xparam1,fval,exitflag] = fminunc(fh,xparam1,optim_options,gend, ...
data);
elseif options_.mode_compute == 4
H0 = 1e-4*eye(nx);
crit = 1e-7;
nit = 1000;
verbose = 2;
[fval,xparam1,grad,hessian_csminwel,itct,fcount,retcodehat] = ...
csminwel('DsgeLikelihood',xparam1,H0,[],crit,nit,gend,data);
disp(sprintf('Objective function at mode: %f',fval))
disp(sprintf('Objective function at mode: %f',DsgeLikelihood(xparam1,gend,data)))
elseif options_.mode_compute == 5
flag = 0;
[xparam1, hh, gg, fval] = newrat('DsgeLikelihood',xparam1,[],[],flag,gend,data);
eval(['save ' M_.fname '_mode xparam1 hh gg fval;']);
end
if options_.mode_compute ~= 5
hh = reshape(hessian('DsgeLikelihood',xparam1,gend,data),nx,nx);
eval(['save ' M_.fname '_mode xparam1 hh fval;']);
end
eval(['save ' M_.fname '_mode xparam1 hh;']);
end
if options_.mode_check == 1 & options_.posterior_mode_estimation
mode_check(xparam1,0,hh,gend,data,lb,ub);
end
if options_.posterior_mode_estimation
hh = generalized_cholesky(hh);
invhess = inv(hh);
stdh = sqrt(diag(invhess));
else
invhess = eye(length(xparam1));
end
if any(bayestopt_.pshape > 0) & options_.posterior_mode_estimation
disp(' ')
disp('RESULTS FROM POSTERIOR MAXIMIZATION')
tstath = zeros(nx,1);
for i = 1:nx
tstath(i) = abs(xparam1(i))/stdh(i);
end
tit1 = sprintf('%10s %7s %8s %7s %6s %4s %6s\n',' ','prior mean', ...
'mode','s.d.','t-stat','prior','pstdev');
if np
ip = nvx+nvn+ncx+ncn+1;
disp('parameters')
disp(tit1)
for i=1:np
name = bayestopt_.name{ip};
disp(sprintf('%12s %7.3f %8.4f %7.4f %7.4f %4s %6.4f', ...
name, ...
bayestopt_.pmean(ip),xparam1(ip),stdh(ip),tstath(ip), ...
pnames(bayestopt_.pshape(ip)+1,:), ...
bayestopt_.pstdev(ip)));
eval(['oo_.posterior_mode.parameters.' name ' = xparam1(ip);']);
eval(['oo_.posterior_std.parameters.' name ' = stdh(ip);']);
ip = ip+1;
end
end
if nvx
ip = 1;
disp('standard deviation of shocks')
disp(tit1)
for i=1:nvx
k = estim_params_.var_exo(i,1);
name = deblank(M_.exo_names(k,:));
disp(sprintf('%12s %7.3f %8.4f %7.4f %7.4f %4s %6.4f', ...
name,bayestopt_.pmean(ip),xparam1(ip), ...
stdh(ip),tstath(ip),pnames(bayestopt_.pshape(ip)+1,:), ...
bayestopt_.pstdev(ip)));
M_.Sigma_e(k,k) = xparam1(ip)*xparam1(ip);
eval(['oo_.posterior_mode.shocks_std.' name ' = xparam1(ip);']);
eval(['oo_.posterior_std.shocks_std.' name ' = stdh(ip);']);
ip = ip+1;
end
end
if nvn
disp('standard deviation of measurement errors')
disp(tit1)
ip = nvx+1;
for i=1:nvn
name = deblank(options_.varobs(estim_params_.var_endo(i,1),:));
disp(sprintf('%12s %7.3f %8.4f %7.4f %7.4f %4s %6.4f', ...
name,bayestopt_.pmean(ip), ...
xparam1(ip),stdh(ip),tstath(ip), ...
pnames(bayestopt_.pshape(ip)+1,:), ...
bayestopt_.pstdev(ip)));
eval(['oo_.posterior_mode.measurement_errors_std.' name ' = xparam1(ip);']);
eval(['oo_.posterior_std.measurement_errors_std.' name ' = stdh(ip);']);
ip = ip+1;
end
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 = [deblank(M_.exo_names(k1,:)) ',' deblank(M_.exo_names(k2,:))];
NAME = [deblank(M_.exo_names(k1,:)) '_' deblank(M_.exo_names(k2,:))];
disp(sprintf('%12s %7.3f %8.4f %7.4f %7.4f %4s %6.4f', name, ...
bayestopt_.pmean(ip),xparam1(ip),stdh(ip),tstath(ip), ...
pnames(bayestopt_.pshape(ip)+1,:), bayestopt_.pstdev(ip)));
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_.posterior_mode.shocks_corr.' NAME ' = xparam1(ip);']);
eval(['oo_.posterior_std.shocks_corr.' NAME ' = stdh(ip);']);
ip = ip+1;
end
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 = [deblank(M_.endo_names(k1,:)) ',' deblank(M_.endo_names(k2,:))];
NAME = [deblank(M_.endo_names(k1,:)) '_' deblank(M_.endo_names(k2,:))];
disp(sprintf('%12s %7.3f %8.4f %7.4f %7.4f %4s %6.4f', name, ...
bayestopt_.pmean(ip),xparam1(ip),stdh(ip),tstath(ip), ...
pnames(bayestopt_.pshape(ip)+1,:), bayestopt_.pstdev(ip)));
eval(['oo_.posterior_mode.measurement_errors_corr.' NAME ' = xparam1(ip);']);
eval(['oo_.posterior_std.measurement_errors_corr.' NAME ' = stdh(ip);']);
ip = ip+1;
end
end
%% Laplace approximation to the marginal log density:
md_Laplace = .5*size(xparam1,1)*log(2*pi) + .5*log(det(invhess)) ...
- DsgeLikelihood(xparam1,gend,data);
oo_.MarginalDensity.LaplaceApproximation = md_Laplace;
disp(' ')
disp(sprintf('Log data density [Laplace approximation] is %f.',md_Laplace))
disp(' ')
elseif ~any(bayestopt_.pshape > 0) & options_.posterior_mode_estimation
disp(' ')
disp('RESULTS FROM MAXIMUM LIKELIHOOD')
tstath = zeros(nx,1);
for i = 1:nx
tstath(i) = abs(xparam1(i))/stdh(i);
end
tit1 = sprintf('%10s %10s %7s %6s\n',' ','Estimate','s.d.','t-stat');
if np
ip = nvx+nvn+ncx+ncn+1;
disp('parameters')
disp(tit1)
for i=1:np
name = bayestopt_.name{ip};
disp(sprintf('%12s %8.4f %7.4f %7.4f', ...
name,xparam1(ip),stdh(ip),tstath(ip)));
eval(['oo_.mle_mode.parameters.' name ' = xparam1(ip);']);
eval(['oo_.mle_std.parameters.' name ' = stdh(ip);']);
ip = ip+1;
end
end
if nvx
ip = 1;
disp('standard deviation of shocks')
disp(tit1)
for i=1:nvx
k = estim_params_.var_exo(i,1);
name = deblank(M_.exo_names(k,:));
disp(sprintf('%12s %8.4f %7.4f %7.4f',name,xparam1(ip),stdh(ip),tstath(ip)));
M_.Sigma_e(k,k) = xparam1(ip)*xparam1(ip);
eval(['oo_.mle_mode.shocks_std.' name ' = xparam1(ip);']);
eval(['oo_.mle_std.shocks_std.' name ' = stdh(ip);']);
ip = ip+1;
end
end
if nvn
disp('standard deviation of measurement errors')
disp(tit1)
ip = nvx+1;
for i=1:nvn
name = deblank(options_.varobs(estim_params_.var_endo(i,1),:));
disp(sprintf('%12s %8.4f %7.4f %7.4f',name,xparam1(ip),stdh(ip),tstath(ip)))
eval(['oo_.mle_mode.measurement_errors_std.' name ' = xparam1(ip);']);
eval(['oo_.mle_std.measurement_errors_std.' name ' = stdh(ip);']);
ip = ip+1;
end
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 = [deblank(M_.exo_names(k1,:)) ',' deblank(M_.exo_names(k2,:))];
NAME = [deblank(M_.exo_names(k1,:)) '_' deblank(M_.exo_names(k2,:))];
disp(sprintf('%12s %8.4f %7.4f %7.4f', name,xparam1(ip),stdh(ip),tstath(ip)));
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_.mle_mode.shocks_corr.' NAME ' = xparam1(ip);']);
eval(['oo_.mle_std.shocks_corr.' NAME ' = stdh(ip);']);
ip = ip+1;
end
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 = [deblank(M_.endo_names(k1,:)) ',' deblank(M_.endo_names(k2,:))];
NAME = [deblank(M_.endo_names(k1,:)) '_' deblank(M_.endo_names(k2,:))];
disp(sprintf('%12s %8.4f %7.4f %7.4f',name,xparam1(ip),stdh(ip),tstath(ip)));
eval(['oo_.mle_mode.measurement_error_corr.' NAME ' = xparam1(ip);']);
eval(['oo_.mle_std.measurement_error_corr.' NAME ' = stdh(ip);']);
ip = ip+1;
end
end
end
if any(bayestopt_.pshape > 0) & options_.TeX %% Bayesian estimation (posterior mode) Latex output
if np
filename = [M_.fname '_Posterior_Mode_1.TeX'];
fidTeX = fopen(filename,'w');
fprintf(fidTeX,'%% TeX-table generated by dynare_estimation (Dynare).\n');
fprintf(fidTeX,'%% RESULTS FROM POSTERIOR MAXIMIZATION (parameters)\n');
fprintf(fidTeX,['%% ' datestr(now,0)]);
fprintf(fidTeX,' \n');
fprintf(fidTeX,' \n');
fprintf(fidTeX,'{\\tiny \n')
fprintf(fidTeX,'\\begin{table}\n');
fprintf(fidTeX,'\\centering\n');
fprintf(fidTeX,'\\begin{tabular}{l|lcccc} \n');
fprintf(fidTeX,'\\hline\\hline \\\\ \n');
fprintf(fidTeX,' & Prior distribution & Prior mean & Prior s.d. & Posterior mode & s.d. \\\\ \n');
fprintf(fidTeX,'\\hline \\\\ \n');
ip = nvx+nvn+ncx+ncn+1;
for i=1:np
fprintf(fidTeX,'$%s$ & %s & %7.3f & %6.4f & %8.4f & %7.4f \\\\ \n',...
deblank(estim_params_.tex(i,:)),...
deblank(pnames(bayestopt_.pshape(ip)+1,:)),...
bayestopt_.pmean(ip),...
estim_params_.param_vals(i,6),...
xparam1(ip),...
stdh(ip));
ip = ip + 1;
end
fprintf(fidTeX,'\\hline\\hline \n');
fprintf(fidTeX,'\\end{tabular}\n ');
fprintf(fidTeX,'\\caption{Results from posterior parameters (parameters)}\n ');
fprintf(fidTeX,'\\label{Table:Posterior:1}\n');
fprintf(fidTeX,'\\end{table}\n');
fprintf(fidTeX,'} \n')
fprintf(fidTeX,'%% End of TeX file.\n');
fclose(fidTeX);
end
if nvx
TeXfile = [M_.fname '_Posterior_Mode_2.TeX'];
fidTeX = fopen(TeXfile,'w');
fprintf(fidTeX,'%% TeX-table generated by dynare_estimation (Dynare).\n');
fprintf(fidTeX,'%% RESULTS FROM POSTERIOR MAXIMIZATION (standard deviation of structural shocks)\n');
fprintf(fidTeX,['%% ' datestr(now,0)]);
fprintf(fidTeX,' \n');
fprintf(fidTeX,' \n');
fprintf(fidTeX,'{\\tiny \n');
fprintf(fidTeX,'\\begin{table}\n');
fprintf(fidTeX,'\\centering\n');
fprintf(fidTeX,'\\begin{tabular}{l|lcccc} \n');
fprintf(fidTeX,'\\hline\\hline \\\\ \n');
fprintf(fidTeX,' & Prior distribution & Prior mean & Prior s.d. & Posterior mode & s.d. \\\\ \n')
fprintf(fidTeX,'\\hline \\\\ \n');
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'],...
deblank(M_.exo_names_tex(k,:)),...
deblank(pnames(bayestopt_.pshape(ip)+1,:)),...
bayestopt_.pmean(ip),...
estim_params_.var_exo(i,7),...
xparam1(ip), ...
stdh(ip));
ip = ip+1;
end
fprintf(fidTeX,'\\hline\\hline \n');
fprintf(fidTeX,'\\end{tabular}\n ');
fprintf(fidTeX,'\\caption{Results from posterior parameters (standard deviation of structural shocks)}\n ');
fprintf(fidTeX,'\\label{Table:Posterior:2}\n');
fprintf(fidTeX,'\\end{table}\n');
fprintf(fidTeX,'} \n')
fprintf(fidTeX,'%% End of TeX file.\n');
fclose(fidTeX);
end
if nvn
TeXfile = [M_.fname '_Posterior_Mode_3.TeX'];
fidTeX = fopen(TeXfile,'w');
fprintf(fidTeX,'%% TeX-table generated by dynare_estimation (Dynare).\n');
fprintf(fidTeX,'%% RESULTS FROM POSTERIOR MAXIMIZATION (standard deviation of measurement errors)\n');
fprintf(fidTeX,['%% ' datestr(now,0)]);
fprintf(fidTeX,' \n');
fprintf(fidTeX,' \n');
fprintf(fidTeX,'\\begin{table}\n');
fprintf(fidTeX,'\\centering\n');
fprintf(fidTeX,'\\begin{tabular}{l|lcccc} \n');
fprintf(fidTeX,'\\hline\\hline \\\\ \n');
fprintf(fidTeX,' & Prior distribution & Prior mean & Prior s.d. & Posterior mode & s.d. \\\\ \n')
fprintf(fidTeX,'\\hline \\\\ \n');
ip = nvx+1;
for i=1:nvn
fprintf(fidTeX,'$%s$ & %4s & %7.3f & %6.4f & %8.4f & %7.4f \\\\ \n',...
deblank(options_.varobs_TeX(estim_params_.var_endo(i,1),:)), ...
deblank(pnames(bayestopt_.pshape(ip)+1,:)), ...
bayestopt_.pmean(ip), ...
estim_params_.var_endo(i,7),...
xparam1(ip),...
stdh(ip));
ip = ip+1;
end
fprintf(fidTeX,'\\hline\\hline \n');
fprintf(fidTeX,'\\end{tabular}\n ');
fprintf(fidTeX,'\\caption{Results from posterior parameters (standard deviation of measurement errors)}\n ');
fprintf(fidTeX,'\\label{Table:Posterior:3}\n');
fprintf(fidTeX,'\\end{table}\n');
fprintf(fidTeX,'%% End of TeX file.\n');
fclose(fidTeX);
end
if ncx
TeXfile = [M_.fname '_Posterior_Mode_4.TeX'];
fidTeX = fopen(TeXfile,'w');
fprintf(fidTeX,'%% TeX-table generated by dynare_estimation (Dynare).\n');
fprintf(fidTeX,'%% RESULTS FROM POSTERIOR MAXIMIZATION (correlation of structural shocks)\n');
fprintf(fidTeX,['%% ' datestr(now,0)]);
fprintf(fidTeX,' \n');
fprintf(fidTeX,' \n');
fprintf(fidTeX,'\\begin{table}\n');
fprintf(fidTeX,'\\centering\n');
fprintf(fidTeX,'\\begin{tabular}{l|lcccc} \n');
fprintf(fidTeX,'\\hline\\hline \\\\ \n');
fprintf(fidTeX,' & Prior distribution & Prior mean & Prior s.d. & Posterior mode & s.d. \\\\ \n')
fprintf(fidTeX,'\\hline \\\\ \n');
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'],...
[deblank(M_.exo_names_tex(k1,:)) ',' deblank(M_.exo_names_tex(k2,:))], ...
deblank(pnames(bayestopt_.pshape(ip)+1,:)), ...
bayestopt_.pmean(ip), ...
estim_params_.corrx(i,8), ...
xparam1(ip), ...
stdh(ip));
ip = ip+1;
end
fprintf(fidTeX,'\\hline\\hline \n');
fprintf(fidTeX,'\\end{tabular}\n ');
fprintf(fidTeX,'\\caption{Results from posterior parameters (correlation of structural shocks)}\n ');
fprintf(fidTeX,'\\label{Table:Posterior:4}\n');
fprintf(fidTeX,'\\end{table}\n');
fprintf(fidTeX,'%% End of TeX file.\n');
fclose(fidTeX);
end
if ncn
TeXfile = [M_.fname '_Posterior_Mode_5.TeX'];
fidTeX = fopen(TeXfile,'w');
fprintf(fidTeX,'%% TeX-table generated by dynare_estimation (Dynare).\n');
fprintf(fidTeX,'%% RESULTS FROM POSTERIOR MAXIMIZATION (correlation of measurement errors)\n');
fprintf(fidTeX,['%% ' datestr(now,0)]);
fprintf(fidTeX,' \n');
fprintf(fidTeX,' \n');
fprintf(fidTeX,'\\begin{table}\n');
fprintf(fidTeX,'\\centering\n');
fprintf(fidTeX,'\\begin{tabular}{l|lcccc} \n');
fprintf(fidTeX,'\\hline\\hline \\\\ \n');
fprintf(fidTeX,' & Prior distribution & Prior mean & Prior s.d. & Posterior mode & s.d. \\\\ \n')
fprintf(fidTeX,'\\hline \\\\ \n');
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',...
[deblank(M_.endo_names_tex(k1,:)) ',' deblank(M_.endo_names_tex(k2,:))], ...
pnames(bayestopt_.pshape(ip)+1,:), ...
bayestopt_.pmean(ip), ...
estim_params_.corrn(i,8), ...
xparam1(ip), ...
stdh(ip));
ip = ip+1;
end
fprintf(fidTeX,'\\hline\\hline \n');
fprintf(fidTeX,'\\end{tabular}\n ');
fprintf(fidTeX,'\\caption{Results from posterior parameters (correlation of measurement errors)}\n ');
fprintf(fidTeX,'\\label{Table:Posterior:5}\n');
fprintf(fidTeX,'\\end{table}\n');
fprintf(fidTeX,'%% End of TeX file.\n');
fclose(fidTeX);
end
end
if (any(bayestopt_.pshape >0 ) & options_.mh_replic) | (any(bayestopt_.pshape >0 ) & options_.load_mh_file) %% not ML estimation
bounds = prior_bounds(bayestopt_);
bayestopt_.lb = bounds(:,1);
bayestopt_.ub = bounds(:,2);
if any(xparam1 < bounds(:,1)) | any(xparam1 > bounds(:,2))
find(xparam1 < bounds(:,1))
find(xparam1 > bounds(:,2))
error('Mode values are outside prior bounds. Reduce prior_trunc.')
end
if options_.mh_replic
metropolis(xparam1,invhess,gend,data,rawdata,bounds);
end
if ~options_.nodiagnostic
McMCDiagnostics;
end
%% Here i discard first half of the draws:
CutSample();
%% Estimation of the marginal density from the Mh draws:
marginal = marginal_density;
%%
GetPosteriorParametersStatistics();
return
end
if ~((any(bayestopt_.pshape > 0) & options_.mh_replic) | (any(bayestopt_.pshape ...
> 0) & options_.load_mh_file)) | ~options_.smoother
%% ML estimation, or posterior mode without metropolis-hastings or metropolis without bayesian smooth variables
options_.lik_algo = 2;
[atT,innov,measurement_error,filtered_state_vector,ys,trend_coeff] = DsgeSmoother(xparam1,gend,data);
for i=1:M_.endo_nbr
eval(['oo_.SmoothedVariables.' deblank(M_.endo_names(dr.order_var(i),:)) ' = atT(i,:)'';']);
eval(['oo_.FilteredVariables.' deblank(M_.endo_names(dr.order_var(i),:)) ' = filtered_state_vector(i,:)'';']);
end
[nbplt,nr,nc,lr,lc,nstar] = pltorg(M_.exo_nbr);
if options_.TeX
fidTeX = fopen([M_.fname '_SmoothedShocks.TeX'],'w');
fprintf(fidTeX,'%% TeX eps-loader file generated by dynare_estimation.m (Dynare).\n');
fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
fprintf(fidTeX,' \n');
end
if nbplt == 1
hh = figure('Name','Smoothed shocks');
NAMES = [];
if options_.TeX, TeXNAMES = [], end
for i=1:M_.exo_nbr
subplot(nr,nc,i);
plot(1:gend,innov(i,:),'-k','linewidth',1)
hold on
plot([1 gend],[0 0],'-r','linewidth',.5)
hold off
xlim([1 gend])
name = deblank(M_.exo_names(i,:));
NAMES = strvcat(NAMES,name);
if ~isempty(options_.XTick)
set(gca,'XTick',options_.XTick)
set(gca,'XTickLabel',options_.XTickLabel)
end
if options_.TeX
texname = M_.exo_names_tex(i,1);
TeXNAMES = strvcat(TeXNAMES,['$ ' deblank(texname) ' $']);
end
title(name,'Interpreter','none')
eval(['oo_.SmoothedShocks.' deblank(M_.exo_names(i,:)) ' = innov(i,:)'';']);
end
eval(['print -depsc2 ' M_.fname '_SmoothedShocks' int2str(1)]);
eval(['print -dpdf ' M_.fname '_SmoothedShocks' int2str(1)]);
saveas(hh,[M_.fname '_SmoothedShocks' int2str(1) '.fig']);
if options_.nograph, close(hh), end
if options_.TeX
fprintf(fidTeX,'\\begin{figure}[H]\n');
for jj = 1:M_.exo_nbr
fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(jj,:)),deblank(TeXNAMES(jj,:)));
end
fprintf(fidTeX,'\\centering \n');
fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_SmoothedShocks%s}\n',M_.fname,int2str(1));
fprintf(fidTeX,'\\caption{Smoothed shocks.}');
fprintf(fidTeX,'\\label{Fig:SmoothedShocks:%s}\n',int2str(1));
fprintf(fidTeX,'\\end{figure}\n');
fprintf(fidTeX,'\n');
fprintf(fidTeX,'%% End of TeX file.\n');
fclose(fidTeX);
end
else
for plt = 1:nbplt-1
hh = figure('Name','Smoothed shocks');
set(0,'CurrentFigure',hh)
NAMES = [];
if options_.TeX, TeXNAMES = [], end
for i=1:nstar
k = (plt-1)*nstar+i;
subplot(nr,nc,i);
plot([1 gend],[0 0],'-r','linewidth',.5)
hold on
plot(1:gend,innov(k,:),'-k','linewidth',1)
hold off
name = deblank(M_.exo_names(k,:));
NAMES = strvcat(NAMES,name);
if ~isempty(options_.XTick)
set(gca,'XTick',options_.XTick)
set(gca,'XTickLabel',options_.XTickLabel)
end
xlim([1 gend])
if options_.TeX
texname = M_.exo_names_tex(k,:);
TeXNAMES = strvcat(TeXNAMES,['$ ' deblank(texname) ' $']);
end
title(name,'Interpreter','none')
eval(['oo_.SmoothedShocks.' deblank(name) ' = innov(k,:)'';']);
end
eval(['print -depsc2 ' M_.fname '_SmoothedShocks' int2str(plt)]);
eval(['print -dpdf ' M_.fname '_SmoothedShocks' int2str(plt)]);
saveas(hh,[M_.fname '_SmoothedShocks' int2str(plt) '.fig']);
if options_.nograph, close(hh), end
if options_.TeX
fprintf(fidTeX,'\\begin{figure}[H]\n');
for jj = 1:nstar
fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(jj,:)),deblank(TeXNAMES(jj,:)));
end
fprintf(fidTeX,'\\centering \n');
fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_SmoothedShocks%s}\n',M_.fname,int2str(plt));
fprintf(fidTeX,'\\caption{Smoothed shocks.}');
fprintf(fidTeX,'\\label{Fig:SmoothedShocks:%s}\n',int2str(plt));
fprintf(fidTeX,'\\end{figure}\n');
fprintf(fidTeX,'\n');
end
end
hh = figure('Name','Smoothed shocks');
set(0,'CurrentFigure',hh)
NAMES = [];
if options_.TeX, TeXNAMES = [], end
for i=1:M_.exo_nbr-(nbplt-1)*nstar
k = (nbplt-1)*nstar+i;
if lr ~= 0
subplot(lr,lc,i);
else
subplot(nr,nc,i);
end
plot([1 gend],[0 0],'-r','linewidth',0.5)
hold on
plot(1:gend,innov(k,:),'-k','linewidth',1)
hold off
name = deblank(M_.exo_names(k,:));
NAMES = strvcat(NAMES,name);
if ~isempty(options_.XTick)
set(gca,'XTick',options_.XTick)
set(gca,'XTickLabel',options_.XTickLabel)
end
xlim([1 gend])
if options_.TeX
texname = M_.exo_names_tex(k,:);
TeXNAMES = strvcat(TeXNAMES,['$ ' deblank(texname) ' $']);
end
title(name,'Interpreter','none')
eval(['oo_.SmoothedShocks.' deblank(name) ' = innov(k,:)'';']);
end
eval(['print -depsc2 ' M_.fname '_SmoothedShocks' int2str(nbplt)]);
eval(['print -dpdf ' M_.fname '_SmoothedShocks' int2str(nbplt)]);
saveas(hh,[M_.fname '_SmoothedShocks' int2str(nbplt) '.fig']);
if options_.nograph, close(hh), end
if options_.TeX
fprintf(fidTeX,'\\begin{figure}[H]\n');
for jj = 1:size(NAMES,1);
fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(jj,:)),deblank(TeXNAMES(jj,:)));
end
fprintf(fidTeX,'\\centering \n');
fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_SmoothedShocks%s}\n',M_.fname,int2str(nbplt));
fprintf(fidTeX,'\\caption{Smoothed shocks.}');
fprintf(fidTeX,'\\label{Fig:SmoothedShocks:%s}\n',int2str(nbplt));
fprintf(fidTeX,'\\end{figure}\n');
fprintf(fidTeX,'\n');
fprintf(fidTeX,'%% End of TeX file.\n');
fclose(fidTeX);
end
end
%%
%% Smooth observational errors...
%%
yf = zeros(gend,n_varobs);
if options_.prefilter == 1
yf = atT(bayestopt_.mf,:)+repmat(transpose(mean_varobs),1,gend);
elseif options_.loglinear == 1
yf = atT(bayestopt_.mf,:)+repmat(log(ys(bayestopt_.mfys)),1,gend)+...
trend_coeff*[1:gend];
else
yf = atT(bayestopt_.mf,:)+repmat(ys(bayestopt_.mfys),1,gend)+...
trend_coeff*[1:gend];
end
if nvn
number_of_plots_to_draw = 0;
index = [];
for i=1:n_varobs
if max(abs(measurement_error(10:end))) > 0.000000001
number_of_plots_to_draw = number_of_plots_to_draw + 1;
index = cat(1,index,i);
end
eval(['oo_.SmoothedMeasurementErrors.' deblank(options_.varobs(i,:)) ...
' = measurement_error(i,:)'';']);
end
[nbplt,nr,nc,lr,lc,nstar] = pltorg(number_of_plots_to_draw);
if options_.TeX
fidTeX = fopen([M_.fname '_SmoothedObservationErrors.TeX'],'w');
fprintf(fidTeX,'%% TeX eps-loader file generated by dynare_estimation.m (Dynare).\n');
fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
fprintf(fidTeX,' \n');
end
if nbplt == 1
hh = figure('Name','Smoothed observation errors');
set(0,'CurrentFigure',hh)
NAMES = [];
if options_.TeX, TeXNAMES = [], end
for i=1:number_of_plots_to_draw
subplot(nr,nc,i);
plot(1:gend,measurement_error(index(i),:),'-k','linewidth',1)
hold on
plot([1 gend],[0 0],'-r','linewidth',.5)
hold off
name = deblank(options_.varobs(index(i),:));
NAMES = strvcat(NAMES,name);
if ~isempty(options_.XTick)
set(gca,'XTick',options_.XTick)
set(gca,'XTickLabel',options_.XTickLabel)
end
if options_.TeX
texname = options_.varobs_TeX(index(i),:);
TeXNAMES = strvcat(TeXNAMES,['$ ' deblank(texname) ' $']);
end
title(name,'Interpreter','none')
end
eval(['print -depsc2 ' M_.fname '_SmoothedObservationErrors' int2str(1)]);
eval(['print -dpdf ' M_.fname '_SmoothedObservationErrors' int2str(1)]);
saveas(hh,[M_.fname '_SmoothedObservationErrors' int2str(1) '.fig']);
if options_.nograph, close(hh), end
if options_.TeX
fprintf(fidTeX,'\\begin{figure}[H]\n');
for jj = 1:number_of_plots_to_draw
fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(jj,:)),deblank(TeXNAMES(jj,:)));
end
fprintf(fidTeX,'\\centering \n');
fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_SmoothedObservationErrors%s}\n',M_.fname,int2str(1));
fprintf(fidTeX,'\\caption{Smoothed observation errors.}');
fprintf(fidTeX,'\\label{Fig:SmoothedObservationErrors:%s',int2str(1));
fprintf(fidTeX,'\\end{figure}\n');
fprintf(fidTeX,'\n');
fprintf(fidTeX,'%% End of TeX file.\n');
fclose(fidTeX);
end
else
for plt = 1:nbplt-1
hh = figure('Name','Smoothed observation errors');
set(0,'CurrentFigure',hh)
NAMES = [];
if options_.TeX, TeXNAMES = [], end
for i=1:nstar
k = (plt-1)*nstar+i;
subplot(nr,nc,i);
plot([1 gend],[0 0],'-r','linewidth',.5)
hold on
plot(1:gend,measurement_error(index(k),:),'-k','linewidth',1)
hold off
name = deblank(options_.varobs(index(k),:));
NAMES = strvcat(NAMES,name);
if ~isempty(options_.XTick)
set(gca,'XTick',options_.XTick)
set(gca,'XTickLabel',options_.XTickLabel)
end
if options_.TeX
texname = options_.varobs_TeX(k,:);
TeXNAMES = strvcat(TeXNAMES,['$ ' deblank(texname) ' $']);
end
title(name,'Interpreter','none')
end
eval(['print -depsc2 ' M_.fname '_SmoothedObservationErrors' int2str(plt)]);
eval(['print -dpdf ' M_.fname '_SmoothedObservationErrors' int2str(plt)]);
saveas(hh,[M_.fname '_SmoothedObservationErrors' int2str(plt) '.fig']);
if options_.nograph, close(hh), end
if options_.TeX
fprintf(fidTeX,'\\begin{figure}[H]\n');
for jj = 1:nstar
fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(jj,:)),deblank(TeXNAMES(jj,:)));
end
fprintf(fidTeX,'\\centering \n');
fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_SmoothedObservationErrors%s}\n',M_.fname,int2str(plt));
fprintf(fidTeX,'\\caption{Smoothed observation errors.}');
fprintf(fidTeX,'\\label{Fig:SmoothedObservationErrors:%s}\n',int2str(plt));
fprintf(fidTeX,'\\end{figure}\n');
fprintf(fidTeX,'\n');
end
end
hh = figure('Name','Smoothed observation errors');
set(0,'CurrentFigure',hh)
NAMES = [];
if options_.TeX, TeXNAMES = [], end
for i=1:number_of_plots_to_draw-(nbplt-1)*nstar
k = (nbplt-1)*nstar+i;
if lr ~= 0
subplot(lr,lc,i);
else
subplot(nr,nc,i);
end
plot([1 gend],[0 0],'-r','linewidth',0.5)
hold on
plot(1:gend,measurement_error(index(k),:),'-k','linewidth',1)
hold off
name = deblank(options_.varobs(index(k),:));
NAMES = strvcat(NAMES,name);
if ~isempty(options_.XTick)
set(gca,'XTick',options_.XTick)
set(gca,'XTickLabel',options_.XTickLabel)
end
if options_.TeX
texname = options_.varobs_TeX(index(k),:);
TeXNAMES = strvcat(TeXNAMES,['$ ' deblank(texname) ' $']);
end
title(name,'Interpreter','none');
end
eval(['print -depsc2 ' M_.fname '_SmoothedObservationErrors' int2str(nbplt)]);
eval(['print -dpdf ' M_.fname '_SmoothedObservationErrors' int2str(nbplt)]);
saveas(hh,[M_.fname '_SmoothedObservationErrors' int2str(nbplt) '.fig']);
if options_.nograph, close(hh), end
if options_.TeX
fprintf(fidTeX,'\\begin{figure}[H]\n');
for jj = 1:size(NAMES,1);
fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(jj,:)),deblank(TeXNAMES(jj,:)));
end
fprintf(fidTeX,'\\centering \n');
fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_SmoothedObservedErrors%s}\n',M_.fname,int2str(nbplt));
fprintf(fidTeX,'\\caption{Smoothed observed errors.}');
fprintf(fidTeX,'\\label{Fig:SmoothedObservedErrors:%s}\n',int2str(nbplt));
fprintf(fidTeX,'\\end{figure}\n');
fprintf(fidTeX,'\n');
fprintf(fidTeX,'%% End of TeX file.\n');
fclose(fidTeX);
end
end
end
%%
%% Historical and smoothed variabes
%%
[nbplt,nr,nc,lr,lc,nstar] = pltorg(n_varobs);
if options_.TeX
fidTeX = fopen([M_.fname '_HistoricalAndSmoothedVariables.TeX'],'w');
fprintf(fidTeX,'%% TeX eps-loader file generated by dynare_estimation.m (Dynare).\n');
fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
fprintf(fidTeX,' \n');
end
if nbplt == 1
hh = figure('Name','Historical and smoothed variables');
NAMES = [];
if options_.TeX, TeXNAMES = [], end
for i=1:n_varobs
subplot(nr,nc,i);
plot(1:gend,yf(i,:),'-r','linewidth',1)
hold on
plot(1:gend,rawdata(:,i),'-k','linewidth',1)
hold off
name = deblank(options_.varobs(i,:));
NAMES = strvcat(NAMES,name);
if ~isempty(options_.XTick)
set(gca,'XTick',options_.XTick)
set(gca,'XTickLabel',options_.XTickLabel)
end
xlim([1 gend])
if options_.TeX
texname = options_.varobs_TeX(i,1);
TeXNAMES = strvcat(TeXNAMES,['$ ' deblank(texname) ' $']);
end
title(name,'Interpreter','none')
end
eval(['print -depsc2 ' M_.fname '_HistoricalAndSmoothedVariables' int2str(1)]);
eval(['print -dpdf ' M_.fname '_HistoricalAndSmoothedVariables' int2str(1)]);
saveas(hh,[M_.fname '_HistoricalAndSmoothedVariables' int2str(1) '.fig']);
if options_.nograph, close(hh), end
if options_.TeX
fprintf(fidTeX,'\\begin{figure}[H]\n');
for jj = 1:n_varobs
fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(jj,:)),deblank(TeXNAMES(jj,:)));
end
fprintf(fidTeX,'\\centering \n');
fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_HistoricalAndSmoothedVariables%s}\n',M_.fname,int2str(1));
fprintf(fidTeX,'\\caption{Historical and smoothed variables.}');
fprintf(fidTeX,'\\label{Fig:HistoricalAndSmoothedVariables:%s}\n',int2str(1));
fprintf(fidTeX,'\\end{figure}\n');
fprintf(fidTeX,'\n');
fprintf(fidTeX,'%% End of TeX file.\n');
fclose(fidTeX);
end
else
for plt = 1:nbplt-1
hh = figure('Name','Historical and smoothed variables');
set(0,'CurrentFigure',hh)
NAMES = [];
if options_.TeX, TeXNAMES = [], end
for i=1:nstar
k = (plt-1)*nstar+i;
subplot(nr,nc,i);
plot(1:gend,yf(k,:),'-r','linewidth',1)
hold on
plot(1:gend,rawdata(:,k),'-k','linewidth',1)
hold off
name = deblank(options_.varobs(k,:));
NAMES = strvcat(NAMES,name);
if ~isempty(options_.XTick)
set(gca,'XTick',options_.XTick)
set(gca,'XTickLabel',options_.XTickLabel)
end
xlim([1 gend])
if options_.TeX
texname = options_.varobs_TeX(k,:);
TeXNAMES = strvcat(TeXNAMES,['$ ' deblank(texname) ' $']);
end
title(name,'Interpreter','none')
end
eval(['print -depsc2 ' M_.fname '_HistoricalAndSmoothedVariables' int2str(plt)]);
eval(['print -dpdf ' M_.fname '_HistoricalAndSmoothedVariables' int2str(plt)]);
saveas(hh,[M_.fname '_HistoricalAndSmoothedVariables' int2str(plt) '.fig']);
if options_.nograph, close(hh), end
if options_.TeX
fprintf(fidTeX,'\\begin{figure}[H]\n');
for jj = 1:nstar
fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(jj,:)),deblank(TeXNAMES(jj,:)));
end
fprintf(fidTeX,'\\centering \n');
fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_HistoricalAndSmoothedVariables%s}\n',M_.fname,int2str(plt));
fprintf(fidTeX,'\\caption{Historical and smoothed variables.}');
fprintf(fidTeX,'\\label{Fig:HistoricalAndSmoothedVariables:%s}\n',int2str(plt));
fprintf(fidTeX,'\\end{figure}\n');
fprintf(fidTeX,'\n');
end
end
hh = figure('Name','Historical and smoothed variables');
set(0,'CurrentFigure',hh)
NAMES = [];
if options_.TeX, TeXNAMES = [], end
for i=1:n_varobs-(nbplt-1)*nstar
k = (nbplt-1)*nstar+i;
if lr ~= 0
subplot(lr,lc,i);
else
subplot(nr,nc,i);
end
plot(1:gend,yf(k,:),'-r','linewidth',1)
hold on
plot(1:gend,rawdata(:,k),'-k','linewidth',1)
hold off
name = deblank(options_.varobs(k,:));
NAMES = strvcat(NAMES,name);
if ~isempty(options_.XTick)
set(gca,'XTick',options_.XTick)
set(gca,'XTickLabel',options_.XTickLabel)
end
xlim([1 gend])
if options_.TeX
texname = options_.varobs_TeX(k,:);
TeXNAMES = strvcat(TeXNAMES,['$ ' deblank(texname) ' $']);
end
title(name,'Interpreter','none');
end
eval(['print -depsc2 ' M_.fname '_HistoricalAndSmoothedVariables' int2str(nbplt)]);
eval(['print -dpdf ' M_.fname '_HistoricalAndSmoothedVariables' int2str(nbplt)]);
saveas(hh,[M_.fname '_HistoricalAndSmoothedVariables' int2str(nbplt) '.fig']);
if options_.nograph, close(hh), end
if options_.TeX
fprintf(fidTeX,'\\begin{figure}[H]\n');
for jj = 1:size(NAMES,1);
fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(jj,:)),deblank(TeXNAMES(jj,:)));
end
fprintf(fidTeX,'\\centering \n');
fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_HistoricalAndSmoothedVariables%s}\n',M_.fname,int2str(nbplt));
fprintf(fidTeX,'\\caption{Historical and smoothed variables.}');
fprintf(fidTeX,'\\label{Fig:HistoricalAndSmoothedVariables:%s}\n',int2str(nbplt));
fprintf(fidTeX,'\\end{figure}\n');
fprintf(fidTeX,'\n');
fprintf(fidTeX,'%% End of TeX file.\n');
fclose(fidTeX);
end
end
end % <-- if ML estimation, posterior mode without metropolis-hastings or metropolis
% without bayesian posterior forecasts.
% SA 07-31-2004 * Added TeX output.
% * Prior plots are done by calling plot_priors.m.
% * All the computations related to the metropolis-hastings are made
% in a new version of metropolis.m.
% * Corrected a bug related to prior's bounds.
% * ...
% * If you do not want to see all the figures generated by dynare, you can use the option
% nograph. The figures will be done and saved in formats eps, pdf and fig (so that you
% should be able to modify the plots within matlab) but each figure will be erased from the
% workspace when completed.
% SA 08-04-2004 Corrected a bug related to the display of the Smooth shocks and variables plots,
% for ML and posterior mode estimation.
% SA 09-03-2004 Compilation of TeX appendix moved to dynare.m.
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