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GetPosteriorParametersStatistics.m
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dynare_estimation_1.m 53.33 KiB
function dynare_estimation_1(var_list_,dname)
% function dynare_estimation_1(var_list_,dname)
% runs the estimation of the model
%
% INPUTS
% var_list_: selected endogenous variables vector
% dname: alternative directory name
%
% OUTPUTS
% none
%
% SPECIAL REQUIREMENTS
% none
% Copyright (C) 2003-2013 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/>.
global M_ options_ oo_ estim_params_ bayestopt_ dataset_
% Set particle filter flag.
if options_.order > 1
if options_.particle.status && options_.order==2
disp(' ')
disp('Estimation using a non linear filter!')
disp(' ')
elseif options_.particle.status && options_.order>2
error(['Non linear filter are not implemented with order ' int2str(options_.order) ' approximation of the model!'])
elseif ~options_.particle.status && options_.order==2
error('For estimating the model with a second order approximation using a non linear filter, one should have options_.particle.status=1;')
else
error(['Cannot estimate a model with an order ' int2str(options_.order) ' approximation!'])
end
end
if ~options_.dsge_var
if options_.particle.status
objective_function = str2func('non_linear_dsge_likelihood');
else
objective_function = str2func('dsge_likelihood');
end
else
objective_function = str2func('DsgeVarLikelihood');
end
[dataset_,xparam1, M_, options_, oo_, estim_params_,bayestopt_] = dynare_estimation_init(var_list_, dname, [], M_, options_, oo_, estim_params_, bayestopt_);
% Set sigma_e_is_diagonal flag (needed if the shocks block is not declared in the mod file).
M_.sigma_e_is_diagonal = 1;
if estim_params_.ncx || ~isequal(nnz(M_.Sigma_e),length(M_.Sigma_e))
M_.sigma_e_is_diagonal = 0;
end
% Set the correlation matrix if necessary.
if ~isequal(estim_params_.ncx,nnz(tril(M_.Sigma_e,-1)))
M_.Correlation_matrix = diag(1./sqrt(diag(M_.Sigma_e)))*M_.Sigma_e*diag(1./sqrt(diag(M_.Sigma_e)));
% Remove NaNs appearing because of variances calibrated to zero. if any(isnan(M_.Correlation_matrix))
zero_variance_idx = find(~diag(M_.Sigma_e));
for i=1:length(zero_variance_idx)
M_.Correlation_matrix(zero_variance_idx(i),:) = 0;
M_.Correlation_matrix(:,zero_variance_idx(i)) = 0;
end
end
end
data = dataset_.data;
rawdata = dataset_.rawdata;
data_index = dataset_.missing.aindex;
missing_value = dataset_.missing.state;
% Set number of observations
gend = options_.nobs;
% Set the number of observed variables.
n_varobs = size(options_.varobs,1);
% Get the number of parameters to be estimated.
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.
%% Set the names of the priors.
pnames = [' ';'beta ';'gamm ';'norm ';'invg ';'unif ';'invg2'];
%% Set parameters bounds
lb = bayestopt_.lb;
ub = bayestopt_.ub;
dr = oo_.dr;
%% load mode file is necessary
if ~isempty(options_.mode_file) && ~options_.mh_posterior_mode_estimation
load(options_.mode_file);
end
%% load optimal_mh_scale parameter if previous run was with
%% mode_compute=6
mh_scale_fname = [M_.fname '_optimal_mh_scale_parameter.mat'];
if exist(mh_scale_fname)
if options_.mode_compute == 0
tmp = load(mh_scale_fname,'Scale');
bayestopt_.mh_jscale = tmp.Scale;
clear tmp;
else
% remove the file if mode_compute ~= 0
delete('mh_scale_fname')
end
end
if ~isempty(estim_params_)
set_parameters(xparam1);
end
% compute sample moments if needed (bvar-dsge)
if options_.dsge_var
if dataset_.missing.state
error('I cannot estimate a DSGE-VAR model with missing observations!')
end
if options_.noconstant
evalin('base',...
['[mYY,mXY,mYX,mXX,Ydata,Xdata] = ' ...
'var_sample_moments(options_.first_obs,' ...
'options_.first_obs+options_.nobs-1,options_.dsge_varlag,-1,' ...
'options_.datafile, options_.varobs,options_.xls_sheet,options_.xls_range);'])
else% The steady state is non zero ==> a constant in the VAR is needed!
evalin('base',['[mYY,mXY,mYX,mXX,Ydata,Xdata] = ' ...
'var_sample_moments(options_.first_obs,' ... 'options_.first_obs+options_.nobs-1,options_.dsge_varlag,0,' ...
'options_.datafile, options_.varobs,options_.xls_sheet,options_.xls_range);'])
end
end
oo_ = initial_estimation_checks(objective_function,xparam1,dataset_,M_,estim_params_,options_,bayestopt_,oo_);
if isequal(options_.mode_compute,0) && isempty(options_.mode_file) && options_.mh_posterior_mode_estimation==0
if options_.smoother == 1
[atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,decomp] = DsgeSmoother(xparam1,gend,data,data_index,missing_value);
oo_.Smoother.SteadyState = ys;
oo_.Smoother.TrendCoeffs = trend_coeff;
if options_.filter_covariance
oo_.Smoother.variance = P;
end
i_endo = bayestopt_.smoother_saved_var_list;
if options_.nk ~= 0
oo_.FilteredVariablesKStepAhead = ...
aK(options_.filter_step_ahead,i_endo,:);
if ~isempty(PK)
oo_.FilteredVariablesKStepAheadVariances = ...
PK(options_.filter_step_ahead,i_endo,i_endo,:);
end
if ~isempty(decomp)
oo_.FilteredVariablesShockDecomposition = ...
decomp(options_.filter_step_ahead,i_endo,:,:);
end
end
for i=bayestopt_.smoother_saved_var_list'
i1 = dr.order_var(bayestopt_.smoother_var_list(i));
eval(['oo_.SmoothedVariables.' deblank(M_.endo_names(i1,:)) ...
' = atT(i,:)'';']);
if options_.nk > 0
eval(['oo_.FilteredVariables.' deblank(M_.endo_names(i1,:)) ...
' = squeeze(aK(1,i,:));']);
end
eval(['oo_.UpdatedVariables.' deblank(M_.endo_names(i1,:)) ' = updated_variables(i,:)'';']);
end
for i=1:M_.exo_nbr
eval(['oo_.SmoothedShocks.' deblank(M_.exo_names(i,:)) ' = innov(i,:)'';']);
end
end
return
end
%% Estimation of the posterior mode or likelihood mode
if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
switch options_.mode_compute
case 1
if exist('OCTAVE_VERSION')
error('Option mode_compute=1 is not available under Octave')
elseif ~user_has_matlab_license('optimization_toolbox')
error('Option mode_compute=1 requires the Optimization Toolbox')
end
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
if options_.analytic_derivation,
optim_options = optimset(optim_options,'GradObj','on','TolX',1e-7);
end
[xparam1,fval,exitflag,output,lamdba,grad,hessian_fmincon] = ...
fmincon(objective_function,xparam1,[],[],[],[],lb,ub,[],optim_options,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
case 2
error('ESTIMATION: mode_compute=2 option (Lester Ingber''s Adaptive Simulated Annealing) is no longer available')
case 3 if exist('OCTAVE_VERSION') && ~user_has_octave_forge_package('optim')
error('Option mode_compute=3 requires the optim package')
elseif ~exist('OCTAVE_VERSION') && ~user_has_matlab_license('optimization_toolbox')
error('Option mode_compute=3 requires the Optimization Toolbox')
end
optim_options = optimset('display','iter','MaxFunEvals',100000,'TolFun',1e-8,'TolX',1e-6);
if isfield(options_,'optim_opt')
eval(['optim_options = optimset(optim_options,' options_.optim_opt ');']);
end
if options_.analytic_derivation,
optim_options = optimset(optim_options,'GradObj','on');
end
if ~exist('OCTAVE_VERSION')
[xparam1,fval,exitflag] = fminunc(objective_function,xparam1,optim_options,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
else
% Under Octave, use a wrapper, since fminunc() does not have a 4th arg
func = @(x) objective_function(x, dataset_,options_,M_,estim_params_,bayestopt_,oo_);
[xparam1,fval,exitflag] = fminunc(func,xparam1,optim_options);
end
case 4
H0 = 1e-4*eye(nx);
crit = 1e-7;
nit = 1000;
verbose = 2;
if options_.analytic_derivation,
analytic_grad=1;
else
analytic_grad=[];
end
[fval,xparam1,grad,hessian_csminwel,itct,fcount,retcodehat] = ...
csminwel1(objective_function,xparam1,H0,analytic_grad,crit,nit,options_.gradient_method,options_.gradient_epsilon,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
disp(sprintf('Objective function at mode: %f',fval))
case 5
if isfield(options_,'hess')
flag = options_.hess;
else
flag = 1;
end
if isfield(options_,'ftol')
crit = options_.ftol;
else
crit = 1.e-5;
end
if options_.analytic_derivation,
analytic_grad=1;
ana_deriv = options_.analytic_derivation;
options_.analytic_derivation = -1;
crit = 1.e-7;
flag = 0;
else
analytic_grad=0;
end
if isfield(options_,'nit')
nit = options_.nit;
else
nit=1000;
end
[xparam1,hh,gg,fval,invhess] = newrat(objective_function,xparam1,analytic_grad,crit,nit,flag,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
if options_.analytic_derivation,
options_.analytic_derivation = ana_deriv;
end
parameter_names = bayestopt_.name;
save([M_.fname '_mode.mat'],'xparam1','hh','gg','fval','invhess','parameter_names');
case 6
fval = feval(objective_function,xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
OldMode = fval;
if ~exist('MeanPar','var') MeanPar = xparam1;
end
if exist('hh','var')
CovJump = inv(hh);
else% The covariance matrix is initialized with the prior
% covariance (a diagonal matrix) %%Except for infinite variances ;-)
varinit = 'prior';
if strcmpi(varinit,'prior')
stdev = bayestopt_.p2;
indx = find(isinf(stdev));
stdev(indx) = ones(length(indx),1)*sqrt(10);
vars = stdev.^2;
CovJump = diag(vars);
elseif strcmpi(varinit,'eye')
vars = ones(length(bayestopt_.p2),1)*0.1;
CovJump = diag(vars);
else
disp('gmhmaxlik :: Error!')
return
end
end
OldPostVar = CovJump;
Scale = options_.mh_jscale;
for i=1:options_.gmhmaxlik.iterations
if i == 1
if options_.gmhmaxlik.iterations>1
flag = '';
else
flag = 'LastCall';
end
[xparam1,PostVar,Scale,PostMean] = ...
gmhmaxlik(objective_function,xparam1,[lb ub],options_.gmhmaxlik,Scale,flag,MeanPar,CovJump,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
fval = feval(objective_function,xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
options_.mh_jscale = Scale;
mouvement = max(max(abs(PostVar-OldPostVar)));
disp(' ')
disp('========================================================== ')
disp([' Change in the covariance matrix = ' num2str(mouvement) '.'])
disp([' Mode improvement = ' num2str(abs(OldMode-fval))])
disp([' New value of jscale = ' num2str(Scale)])
disp('========================================================== ')
OldMode = fval;
else
OldPostVar = PostVar;
if i<options_.gmhmaxlik.iterations
flag = '';
else
flag = 'LastCall';
end
[xparam1,PostVar,Scale,PostMean] = ...
gmhmaxlik(objective_function,xparam1,[lb ub],...
options_.gmhmaxlik,Scale,flag,PostMean,PostVar,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
fval = feval(objective_function,xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
options_.mh_jscale = Scale;
mouvement = max(max(abs(PostVar-OldPostVar)));
fval = feval(objective_function,xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
disp(' ')
disp('========================================================== ')
disp([' Change in the covariance matrix = ' num2str(mouvement) '.'])
disp([' Mode improvement = ' num2str(abs(OldMode-fval))])
disp([' New value of jscale = ' num2str(Scale)])
disp('========================================================== ')
OldMode = fval;
end
hh = inv(PostVar);
save([M_.fname '_mode.mat'],'xparam1','hh');
save([M_.fname '_optimal_mh_scale_parameter.mat'],'Scale');
bayestopt_.jscale = ones(length(xparam1),1)*Scale;
end
disp(' ') disp(['Optimal value of the scale parameter = ' num2str(Scale)])
disp(' ')
disp(['Final value of the log posterior (or likelihood): ' num2str(fval)])
disp(' ')
parameter_names = bayestopt_.name;
save([M_.fname '_mode.mat'],'xparam1','hh','parameter_names');
case 7
% Matlab's simplex (Optimization toolbox needed).
if exist('OCTAVE_VERSION') && ~user_has_octave_forge_package('optim')
error('Option mode_compute=7 requires the optim package')
elseif ~exist('OCTAVE_VERSION') && ~user_has_matlab_license('optimization_toolbox')
error('Option mode_compute=7 requires the Optimization Toolbox')
end
optim_options = optimset('display','iter','MaxFunEvals',1000000,'MaxIter',6000,'TolFun',1e-8,'TolX',1e-6);
if isfield(options_,'optim_opt')
eval(['optim_options = optimset(optim_options,' options_.optim_opt ');']);
end
[xparam1,fval,exitflag] = fminsearch(objective_function,xparam1,optim_options,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
case 8
% Dynare implementation of the simplex algorithm.
[xparam1,fval,exitflag] = simplex_optimization_routine(objective_function,xparam1,options_.simplex,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
case 9
H0 = 1e-4*ones(nx,1);
warning('off','CMAES:NonfinitenessRange');
warning('off','CMAES:InitialSigma');
[x, fval, COUNTEVAL, STOPFLAG, OUT, BESTEVER] = cmaes(func2str(objective_function),xparam1,H0,options_.cmaes,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
xparam1=BESTEVER.x;
disp(sprintf('\n Objective function at mode: %f',fval))
case 101
myoptions=soptions;
myoptions(2)=1e-6; %accuracy of argument
myoptions(3)=1e-6; %accuracy of function (see Solvopt p.29)
myoptions(5)= 1.0;
[xparam1,fval]=solvopt(xparam1,objective_function,[],myoptions,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
case 102
%simulating annealing
% LB=zeros(size(xparam1))-20;
% UB=zeros(size(xparam1))+20;
LB = xparam1 - 1;
UB = xparam1 + 1;
neps=10;
% Set input parameters.
maxy=0;
epsilon=1.0e-9;
rt_=.10;
t=15.0;
ns=10;
nt=10;
maxevl=100000000;
idisp =1;
npar=length(xparam1);
disp(['size of param',num2str(length(xparam1))])
c=.1*ones(npar,1);
%* Set input values of the input/output parameters.*
vm=1*ones(npar,1);
disp(['number of parameters= ' num2str(npar) 'max= ' num2str(maxy) 't= ' num2str(t)]);
disp(['rt_= ' num2str(rt_) 'eps= ' num2str(epsilon) 'ns= ' num2str(ns)]);
disp(['nt= ' num2str(nt) 'neps= ' num2str(neps) 'maxevl= ' num2str(maxevl)]);
% disp(['iprint= ' num2str(iprint) 'seed= ' num2str(seed)]);
disp ' ';
disp ' ';
disp(['starting values(x) ' num2str(xparam1')]);
disp(['initial step length(vm) ' num2str(vm')]);
disp(['lower bound(lb)', 'initial conditions', 'upper bound(ub)' ]);
disp([LB xparam1 UB]);
disp(['c vector ' num2str(c')]);
[xparam1, fval, nacc, nfcnev, nobds, ier, t, vm] = sa(objective_function,xparam1,maxy,rt_,epsilon,ns,nt ...
,neps,maxevl,LB,UB,c,idisp ,t,vm,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
case 'prior'
hh = diag(bayestopt_.p2.^2);
otherwise
if ischar(options_.mode_compute)
[xparam1, fval, retcode ] = feval(options_.mode_compute,objective_function,xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
else
error(['dynare_estimation:: mode_compute = ' int2str(options_.mode_compute) ' option is unknown!'])
end
end
if ~isequal(options_.mode_compute,6) && ~isequal(options_.mode_compute,'prior')
if options_.cova_compute == 1
if options_.analytic_derivation && strcmp(func2str(objective_function),'dsge_likelihood'),
ana_deriv = options_.analytic_derivation;
options_.analytic_derivation = 2;
[junk1, junk2, hh] = feval(objective_function,xparam1, ...
dataset_,options_,M_,estim_params_,bayestopt_,oo_);
options_.analytic_derivation = ana_deriv;
else
hh = reshape(hessian(objective_function,xparam1, ...
options_.gstep,dataset_,options_,M_,estim_params_,bayestopt_,oo_),nx,nx);
end
end
end
parameter_names = bayestopt_.name;
if options_.cova_compute
save([M_.fname '_mode.mat'],'xparam1','hh','parameter_names');
else
save([M_.fname '_mode.mat'],'xparam1','parameter_names');
end
end
if options_.cova_compute == 0
hh = [];%NaN(length(xparam1),length(xparam1));
end
if ~options_.mh_posterior_mode_estimation && options_.cova_compute
try
chol(hh);
catch
disp(' ')
disp('POSTERIOR KERNEL OPTIMIZATION PROBLEM!')
disp(' (minus) the hessian matrix at the "mode" is not positive definite!')
disp('=> posterior variance of the estimated parameters are not positive.')
disp('You should try to change the initial values of the parameters using')
disp('the estimated_params_init block, or use another optimization routine.')
warning('The results below are most likely wrong!');
end
end
if options_.mode_check == 1 && ~options_.mh_posterior_mode_estimation
ana_deriv = options_.analytic_derivation;
options_.analytic_derivation = 0;
mode_check(objective_function,xparam1,hh,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
options_.analytic_derivation = ana_deriv;
end
oo_.posterior.optimization.mode = xparam1;
oo_.posterior.optimization.variance = [];
if ~options_.mh_posterior_mode_estimation
if options_.cova_compute
invhess = inv(hh);
stdh = sqrt(diag(invhess));
oo_.posterior.optimization.variance = invhess;
end
else
variances = bayestopt_.p2.*bayestopt_.p2;
idInf = isinf(variances); variances(idInf) = 1;
invhess = options_.mh_posterior_mode_estimation*diag(variances);
xparam1 = bayestopt_.p5;
idNaN = isnan(xparam1);
xparam1(idNaN) = bayestopt_.p1(idNaN);
xparam1 = transpose(xparam1);
end
if ~options_.cova_compute
stdh = NaN(length(xparam1),1);
end
if any(bayestopt_.pshape > 0) && ~options_.mh_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
header_width = row_header_width(M_,estim_params_,bayestopt_);
tit1 = sprintf('%-*s %7s %8s %7s %6s %4s %6s\n',header_width-2,' ','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('%-*s %7.3f %8.4f %7.4f %7.4f %4s %6.4f', ...
header_width,name, ...
bayestopt_.p1(ip),xparam1(ip),stdh(ip),tstath(ip), ...
pnames(bayestopt_.pshape(ip)+1,:), ...
bayestopt_.p2(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('%-*s %7.3f %8.4f %7.4f %7.4f %4s %6.4f', ...
header_width,name,bayestopt_.p1(ip),xparam1(ip), ...
stdh(ip),tstath(ip),pnames(bayestopt_.pshape(ip)+1,:), ...
bayestopt_.p2(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('%-*s %7.3f %8.4f %7.4f %7.4f %4s %6.4f', ...
header_width,name,bayestopt_.p1(ip), ...
xparam1(ip),stdh(ip),tstath(ip), ...
pnames(bayestopt_.pshape(ip)+1,:), ...
bayestopt_.p2(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('%-*s %7.3f %8.4f %7.4f %7.4f %4s %6.4f', ...
header_width,name,bayestopt_.p1(ip),xparam1(ip),stdh(ip),tstath(ip), ...
pnames(bayestopt_.pshape(ip)+1,:), bayestopt_.p2(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('%-*s %7.3f %8.4f %7.4f %7.4f %4s %6.4f', ...
header_width,name,bayestopt_.p1(ip),xparam1(ip),stdh(ip),tstath(ip), ...
pnames(bayestopt_.pshape(ip)+1,:), bayestopt_.p2(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:
if options_.cova_compute
estim_params_nbr = size(xparam1,1);
scale_factor = -sum(log10(diag(invhess)));
log_det_invhess = -estim_params_nbr*log(scale_factor)+log(det(scale_factor*invhess));
likelihood = feval(objective_function,xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
oo_.MarginalDensity.LaplaceApproximation = .5*estim_params_nbr*log(2*pi) + .5*log_det_invhess - likelihood;
disp(' ')
disp(sprintf('Log data density [Laplace approximation] is %f.',oo_.MarginalDensity.LaplaceApproximation))
disp(' ')
end
elseif ~any(bayestopt_.pshape > 0) && ~options_.mh_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
header_width = row_header_width(M_,estim_params_,bayestopt_);
tit1 = sprintf('%-*s %10s %7s %6s\n',header_width-2,' ','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('%-*s %8.4f %7.4f %7.4f', ...
header_width,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('%-*s %8.4f %7.4f %7.4f',header_width,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('%-*s %8.4f %7.4f %7.4f',header_width,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('%-*s %8.4f %7.4f %7.4f', header_width,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('%-*s %8.4f %7.4f %7.4f',header_width,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
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');
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,'\\begin{center}\n');
fprintf(fidTeX,'\\begin{longtable}{l|lcccc} \n');
fprintf(fidTeX,'\\caption{Results from posterior maximization (parameters)}\n ');
fprintf(fidTeX,'\\label{Table:Posterior:1}\\\\\n');
fprintf(fidTeX,'\\hline\\hline \\\\ \n');
fprintf(fidTeX,' & Prior distribution & Prior mean & Prior s.d. & Posterior mode & s.d. \\\\ \n');
fprintf(fidTeX,'\\hline \\endfirsthead \n');
fprintf(fidTeX,'\\caption{(continued)}\n ');
fprintf(fidTeX,'\\label{Table:Posterior:1}\\\\\n');
fprintf(fidTeX,'\\hline\\hline \\\\ \n');
fprintf(fidTeX,' & Prior distribution & Prior mean & Prior s.d. & Posterior mode & s.d. \\\\ \n');
fprintf(fidTeX,'\\hline \\endhead \n');
fprintf(fidTeX,'\\hline \\multicolumn{6}{r}{(Continued on next page)} \\\\ \\hline \\endfoot \n');
fprintf(fidTeX,'\\hline \\hline \\endlastfoot \n');
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),:),...
deblank(pnames(bayestopt_.pshape(ip)+1,:)),...
bayestopt_.p1(ip),...
bayestopt_.p2(ip),...
xparam1(ip),...
stdh(ip));
ip = ip + 1;
end
fprintf(fidTeX,'\\end{longtable}\n ');
fprintf(fidTeX,'\\end{center}\n');
fprintf(fidTeX,'%% End of TeX file.\n');
fclose(fidTeX);
end
if nvx
TeXfile = [OutputDirectoryName '/' 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,'\\begin{center}\n');
fprintf(fidTeX,'\\begin{longtable}{l|lcccc} \n');
fprintf(fidTeX,'\\caption{Results from posterior maximization (standard deviation of structural shocks)}\n ');
fprintf(fidTeX,'\\label{Table:Posterior:2}\\\\\n');
fprintf(fidTeX,'\\hline\\hline \\\\ \n');
fprintf(fidTeX,' & Prior distribution & Prior mean & Prior s.d. & Posterior mode & s.d. \\\\ \n');
fprintf(fidTeX,'\\hline \\endfirsthead \n');
fprintf(fidTeX,'\\caption{(continued)}\n ');
fprintf(fidTeX,'\\label{Table:Posterior:2}\\\\\n');
fprintf(fidTeX,'\\hline\\hline \\\\ \n');
fprintf(fidTeX,' & Prior distribution & Prior mean & Prior s.d. & Posterior mode & s.d. \\\\ \n');
fprintf(fidTeX,'\\hline \\endhead \n');
fprintf(fidTeX,'\\hline \\multicolumn{6}{r}{(Continued on next page)} \\\\ \\hline \\endfoot \n');
fprintf(fidTeX,'\\hline \\hline \\endlastfoot \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_.p1(ip),...
bayestopt_.p2(ip),...
xparam1(ip), ...
stdh(ip));
ip = ip+1;
end
fprintf(fidTeX,'\\end{longtable}\n ');
fprintf(fidTeX,'\\end{center}\n'); fprintf(fidTeX,'%% End of TeX file.\n');
fclose(fidTeX);
end
if nvn
TeXfile = [OutputDirectoryName '/' 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{center}\n');
fprintf(fidTeX,'\\begin{longtable}{l|lcccc} \n');
fprintf(fidTeX,'\\caption{Results from posterior maximization (standard deviation of measurement errors)}\n ');
fprintf(fidTeX,'\\label{Table:Posterior:3}\\\\\n');
fprintf(fidTeX,'\\hline\\hline \\\\ \n');
fprintf(fidTeX,' & Prior distribution & Prior mean & Prior s.d. & Posterior mode & s.d. \\\\ \n');
fprintf(fidTeX,'\\hline \\endfirsthead \n');
fprintf(fidTeX,'\\caption{(continued)}\n ');
fprintf(fidTeX,'\\label{Table:Posterior:3}\\\\\n');
fprintf(fidTeX,'\\hline\\hline \\\\ \n');
fprintf(fidTeX,' & Prior distribution & Prior mean & Prior s.d. & Posterior mode & s.d. \\\\ \n');
fprintf(fidTeX,'\\hline \\endhead \n');
fprintf(fidTeX,'\\hline \\multicolumn{6}{r}{(Continued on next page)} \\\\ \\hline \\endfoot \n');
fprintf(fidTeX,'\\hline \\hline \\endlastfoot \n');
ip = nvx+1;
for i=1:nvn
idx = strmatch(options_.varobs(estim_params_.var_endo(i,1),:),M_.endo_names);
fprintf(fidTeX,'$%s$ & %4s & %7.3f & %6.4f & %8.4f & %7.4f \\\\ \n',...
deblank(M_.endo_names_tex(idx,:)), ...
deblank(pnames(bayestopt_.pshape(ip)+1,:)), ...
bayestopt_.p1(ip), ...
bayestopt_.p2(ip),...
xparam1(ip),...
stdh(ip));
ip = ip+1;
end
fprintf(fidTeX,'\\end{longtable}\n ');
fprintf(fidTeX,'\\end{center}\n');
fprintf(fidTeX,'%% End of TeX file.\n');
fclose(fidTeX);
end
if ncx
TeXfile = [OutputDirectoryName '/' 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{center}\n');
fprintf(fidTeX,'\\begin{longtable}{l|lcccc} \n');
fprintf(fidTeX,'\\caption{Results from posterior parameters (correlation of structural shocks)}\n ');
fprintf(fidTeX,'\\label{Table:Posterior:4}\\\\\n');
fprintf(fidTeX,'\\hline\\hline \\\\ \n');
fprintf(fidTeX,' & Prior distribution & Prior mean & Prior s.d. & Posterior mode & s.d. \\\\ \n');
fprintf(fidTeX,'\\hline \\endfirsthead \n');
fprintf(fidTeX,'\\caption{(continued)}\n ');
fprintf(fidTeX,'\\label{Table:Posterior:4}\\\\\n');
fprintf(fidTeX,'\\hline\\hline \\\\ \n');
fprintf(fidTeX,' & Prior distribution & Prior mean & Prior s.d. & Posterior mode & s.d. \\\\ \n');
fprintf(fidTeX,'\\hline \\endhead \n');
fprintf(fidTeX,'\\hline \\multicolumn{6}{r}{(Continued on next page)} \\\\ \\hline \\endfoot \n');
fprintf(fidTeX,'\\hline \\hline \\endlastfoot \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_.p1(ip), ...
bayestopt_.p2(ip), ...
xparam1(ip), ...
stdh(ip));
ip = ip+1;
end
fprintf(fidTeX,'\\end{longtable}\n ');
fprintf(fidTeX,'\\end{center}\n');
fprintf(fidTeX,'%% End of TeX file.\n');
fclose(fidTeX);
end
if ncn
TeXfile = [OutputDirectoryName '/' 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{center}\n');
fprintf(fidTeX,'\\begin{longtabe}{l|lcccc} \n');
fprintf(fidTeX,'\\caption{Results from posterior parameters (correlation of measurement errors)}\n ');
fprintf(fidTeX,'\\label{Table:Posterior:5}\\\\\n');
fprintf(fidTeX,'\\hline\\hline \\\\ \n');
fprintf(fidTeX,' & Prior distribution & Prior mean & Prior s.d. & Posterior mode & s.d. \\\\ \n');
fprintf(fidTeX,'\\hline \\endfirsthead \n');
fprintf(fidTeX,'\\caption{(continued)}\n ');
fprintf(fidTeX,'\\label{Table:Posterior:5}\\\\\n');
fprintf(fidTeX,'\\hline\\hline \\\\ \n');
fprintf(fidTeX,' & Prior distribution & Prior mean & Prior s.d. & Posterior mode & s.d. \\\\ \n');
fprintf(fidTeX,'\\hline \\endhead \n');
fprintf(fidTeX,'\\hline \\multicolumn{6}{r}{(Continued on next page)} \\\\ \\hline \\endfoot \n');
fprintf(fidTeX,'\\hline \\hline \\endlastfoot \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_.p1(ip), ...
bayestopt_.p2(ip), ...
xparam1(ip), ...
stdh(ip));
ip = ip+1;
end
fprintf(fidTeX,'\\end{longtable}\n ');
fprintf(fidTeX,'\\end{center}\n');
fprintf(fidTeX,'%% End of TeX file.\n');
fclose(fidTeX);
end
end
if np > 0
pindx = estim_params_.param_vals(:,1);
save([M_.fname '_params.mat'],'pindx');
end
if (any(bayestopt_.pshape >0 ) && options_.mh_replic) || ...
(any(bayestopt_.pshape >0 ) && options_.load_mh_file) %% not ML estimation
bounds = prior_bounds(bayestopt_,options_);
bounds(:,1)=max(bounds(:,1),lb);
bounds(:,2)=min(bounds(:,2),ub);
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
% runs MCMC
if options_.mh_replic
if options_.load_mh_file && options_.use_mh_covariance_matrix
invhess = compute_mh_covariance_matrix;
end
ana_deriv = options_.analytic_derivation;
options_.analytic_derivation = 0;
if options_.cova_compute
feval(options_.posterior_sampling_method,objective_function,options_.proposal_distribution,xparam1,invhess,bounds,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
else
error('I Cannot start the MCMC because the hessian of the posterior kernel at the mode was not computed.')
end
options_.analytic_derivation = ana_deriv;
end
if options_.mh_posterior_mode_estimation
CutSample(M_, options_, estim_params_);
return
else
if ~options_.nodiagnostic && options_.mh_replic > 1000 && options_.mh_nblck > 1
McMCDiagnostics(options_, estim_params_, M_);
end
%% Here i discard first half of the draws:
CutSample(M_, options_, estim_params_);
%% Estimation of the marginal density from the Mh draws:
if options_.mh_replic
[marginal,oo_] = marginal_density(M_, options_, estim_params_, oo_);
oo_ = GetPosteriorParametersStatistics(estim_params_, M_, options_, bayestopt_, oo_);
if ~options_.nograph
oo_ = PlotPosteriorDistributions(estim_params_, M_, options_, bayestopt_, oo_);
end
[oo_.posterior.metropolis.mean,oo_.posterior.metropolis.variance] ...
= GetPosteriorMeanVariance(M_,options_.mh_drop);
else
load([M_.fname '_results'],'oo_');
end
metropolis_draw(1);
if options_.bayesian_irf
PosteriorIRF('posterior');
end
if options_.moments_varendo
oo_ = compute_moments_varendo('posterior',options_,M_,oo_,var_list_);
end
if options_.smoother || ~isempty(options_.filter_step_ahead) || options_.forecast
prior_posterior_statistics('posterior',dataset_);
end
xparam = get_posterior_parameters('mean');
M_ = set_all_parameters(xparam,estim_params_,M_);
end
end
if options_.particle.status
return
end
if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.pshape ...
> 0) && options_.load_mh_file)) ...
|| ~options_.smoother ) && options_.partial_information == 0 % to be fixed
%% ML estimation, or posterior mode without metropolis-hastings or metropolis without bayesian smooth variable
[atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,decomp] = DsgeSmoother(xparam1,dataset_.info.ntobs,dataset_.data,dataset_.missing.aindex,dataset_.missing.state);
oo_.Smoother.SteadyState = ys;
oo_.Smoother.TrendCoeffs = trend_coeff;
oo_.Smoother.variance = P;
i_endo = bayestopt_.smoother_saved_var_list;
if options_.nk ~= 0
oo_.FilteredVariablesKStepAhead = aK(options_.filter_step_ahead, ...
i_endo,:);
if isfield(options_,'kalman_algo')
if ~isempty(PK)
oo_.FilteredVariablesKStepAheadVariances = ... PK(options_.filter_step_ahead,i_endo,i_endo,:);
end
if ~isempty(decomp)
oo_.FilteredVariablesShockDecomposition = ...
decomp(options_.filter_step_ahead,i_endo,:,:);
end
end
end
for i=bayestopt_.smoother_saved_var_list'
i1 = dr.order_var(bayestopt_.smoother_var_list(i));
eval(['oo_.SmoothedVariables.' deblank(M_.endo_names(i1,:)) ' = ' ...
'atT(i,:)'';']);
if options_.nk > 0
eval(['oo_.FilteredVariables.' deblank(M_.endo_names(i1,:)) ...
' = squeeze(aK(1,i,:));']);
end
eval(['oo_.UpdatedVariables.' deblank(M_.endo_names(i1,:)) ...
' = updated_variables(i,:)'';']);
end
for i=1:M_.exo_nbr
eval(['oo_.SmoothedShocks.' deblank(M_.exo_names(i,:)) ' = innov(i,:)'';']);
end
if ~options_.nograph,
[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
for plt = 1:nbplt,
hh = dyn_figure(options_,'Name','Smoothed shocks');
NAMES = [];
if options_.TeX, TeXNAMES = []; end
nstar0=min(nstar,M_.exo_nbr-(plt-1)*nstar);
for i=1:nstar0,
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,:));
if isempty(NAMES)
NAMES = name;
else
NAMES = char(NAMES,name);
end
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,:);
if isempty(TeXNAMES)
TeXNAMES = ['$ ' deblank(texname) ' $'];
else
TeXNAMES = char(TeXNAMES,['$ ' deblank(texname) ' $']);
end
end
title(name,'Interpreter','none')
end
dyn_saveas(hh,[M_.fname '_SmoothedShocks' int2str(plt)],options_);
if options_.TeX
fprintf(fidTeX,'\\begin{figure}[H]\n');
for jj = 1:nstar0
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
if options_.TeX
fprintf(fidTeX,'\n');
fprintf(fidTeX,'%% End of TeX file.\n');
fclose(fidTeX);
end
end
%%
%% Smooth observational errors...
%%
if options_.prefilter == 1
yf = atT(bayestopt_.mf,:)+repmat(dataset_.descriptive.mean',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)) > 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
if ~options_.nograph
[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
for plt = 1:nbplt
hh = dyn_figure(options_,'Name','Smoothed observation errors');
NAMES = [];
if options_.TeX, TeXNAMES = []; end
for i=1:min(nstar,number_of_plots_to_draw-(nbplt-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),:));
if isempty(NAMES)
NAMES = name;
else
NAMES = char(NAMES,name);
end
if ~isempty(options_.XTick)
set(gca,'XTick',options_.XTick)
set(gca,'XTickLabel',options_.XTickLabel)
end
if options_.TeX
idx = strmatch(options_.varobs(index(k),:),M_.endo_names,'exact');
texname = M_.endo_names_tex(idx,:);
if isempty(TeXNAMES)
TeXNAMES = ['$ ' deblank(texname) ' $']; else
TeXNAMES = char(TeXNAMES,['$ ' deblank(texname) ' $']);
end
end
title(name,'Interpreter','none')
end
dyn_saveas(hh,[M_.fname '_SmoothedObservationErrors' int2str(plt)],options_);
if options_.TeX
fprintf(fidTeX,'\\begin{figure}[H]\n');
for jj = 1:nstar
fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(jj,:)),deblank(TeXNAMES(jj,:)));
end
fprintf(fidTeX,'\\centering \n');
fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_SmoothedObservationErrors%s}\n',M_.fname,int2str(plt));
fprintf(fidTeX,'\\caption{Smoothed observation errors.}');
fprintf(fidTeX,'\\label{Fig:SmoothedObservationErrors:%s}\n',int2str(plt));
fprintf(fidTeX,'\\end{figure}\n');
fprintf(fidTeX,'\n');
end
end
if options_.TeX
fprintf(fidTeX,'\n');
fprintf(fidTeX,'%% End of TeX file.\n');
fclose(fidTeX);
end
end
end
%%
%% Historical and smoothed variabes
%%
if ~options_.nograph
[nbplt,nr,nc,lr,lc,nstar] = pltorg(n_varobs);
if options_.TeX
fidTeX = fopen([M_.fname '_HistoricalAndSmoothedVariables.TeX'],'w');
fprintf(fidTeX,'%% TeX eps-loader file generated by dynare_estimation.m (Dynare).\n');
fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
fprintf(fidTeX,' \n');
end
for plt = 1:nbplt,
hh = dyn_figure(options_,'Name','Historical and smoothed variables');
NAMES = [];
if options_.TeX, TeXNAMES = []; end
nstar0=min(nstar,n_varobs-(plt-1)*nstar);
for i=1:nstar0,
k = (plt-1)*nstar+i;
subplot(nr,nc,i);
plot(1:gend,yf(k,:),'--r','linewidth',1)
hold on
plot(1:gend,rawdata(:,k),'--k','linewidth',1)
hold off
name = deblank(options_.varobs(k,:));
if isempty(NAMES)
NAMES = name;
else
NAMES = char(NAMES,name);
end
if ~isempty(options_.XTick)
set(gca,'XTick',options_.XTick)
set(gca,'XTickLabel',options_.XTickLabel)
end
xlim([1 gend])
if options_.TeX
idx = strmatch(options_.varobs(k,:),M_.endo_names,'exact');
texname = M_.endo_names_tex(idx,:);
if isempty(TeXNAMES)
TeXNAMES = ['$ ' deblank(texname) ' $'];
else
TeXNAMES = char(TeXNAMES,['$ ' deblank(texname) ' $']);
end
end title(name,'Interpreter','none')
end
dyn_saveas(hh,[M_.fname '_HistoricalAndSmoothedVariables' int2str(plt)],options_);
if options_.TeX
fprintf(fidTeX,'\\begin{figure}[H]\n');
for jj = 1:nstar0,
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
if options_.TeX
fprintf(fidTeX,'\n');
fprintf(fidTeX,'%% End of TeX file.\n');
fclose(fidTeX);
end
end
end
if options_.forecast > 0 && options_.mh_replic == 0 && ~options_.load_mh_file
dyn_forecast(var_list_,'smoother');
end
if np > 0
pindx = estim_params_.param_vals(:,1);
save([M_.fname '_pindx.mat'] ,'pindx');
end