1394 lines
60 KiB
Matlab
1394 lines
60 KiB
Matlab
function dynare_estimation_1(var_list_,dname)
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% function dynare_estimation_1(var_list_,dname)
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% runs the estimation of the model
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%
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% INPUTS
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% var_list_: selected endogenous variables vector
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% dname: alternative directory name
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%
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% OUTPUTS
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% none
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%
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% SPECIAL REQUIREMENTS
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% none
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% Copyright (C) 2003-2011 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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global M_ options_ oo_ estim_params_ bayestopt_ dataset_
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if ~options_.dsge_var
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objective_function = str2func('DsgeLikelihood');
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else
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objective_function = str2func('DsgeVarLikelihood');
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end
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[dataset_,xparam1, M_, options_, oo_, estim_params_,bayestopt_, fake] = dynare_estimation_init(var_list_, dname, [], M_, options_, oo_, estim_params_, bayestopt_);
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data = dataset_.data;
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rawdata = dataset_.rawdata;
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% Set number of observations
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gend = options_.nobs;
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% Set the number of observed variables.
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n_varobs = size(options_.varobs,1);
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% Get the number of parameters to be estimated.
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nvx = estim_params_.nvx; % Variance of the structural innovations (number of parameters).
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nvn = estim_params_.nvn; % Variance of the measurement innovations (number of parameters).
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ncx = estim_params_.ncx; % Covariance of the structural innovations (number of parameters).
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ncn = estim_params_.ncn; % Covariance of the measurement innovations (number of parameters).
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np = estim_params_.np ; % Number of deep parameters.
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nx = nvx+nvn+ncx+ncn+np; % Total number of parameters to be estimated.
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%% Set the names of the priors.
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pnames = [' ';'beta ';'gamm ';'norm ';'invg ';'unif ';'invg2'];
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%% Set parameters bounds
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lb = bayestopt_.lb;
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ub = bayestopt_.ub;
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dr = oo_.dr;
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%% load mode file is necessary
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if ~isempty(options_.mode_file) && ~options_.mh_posterior_mode_estimation
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load(options_.mode_file);
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end
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if ~isempty(estim_params_)
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set_parameters(xparam1);
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end
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if all(abs(oo_.steady_state(bayestopt_.mfys))<1e-9)
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options_.noconstant = 1;
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else
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options_.noconstant = 0;
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end
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% compute sample moments if needed (bvar-dsge)
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if options_.dsge_var
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if dataset_.missing.state
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error('I cannot estimate a DSGE-VAR model with missing observations!')
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end
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if options_.noconstant
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evalin('base',...
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['[mYY,mXY,mYX,mXX,Ydata,Xdata] = ' ...
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'var_sample_moments(options_.first_obs,' ...
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'options_.first_obs+options_.nobs-1,options_.dsge_varlag,-1,' ...
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'options_.datafile, options_.varobs,options_.xls_sheet,options_.xls_range);'])
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else% The steady state is non zero ==> a constant in the VAR is needed!
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evalin('base',['[mYY,mXY,mYX,mXX,Ydata,Xdata] = ' ...
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'var_sample_moments(options_.first_obs,' ...
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'options_.first_obs+options_.nobs-1,options_.dsge_varlag,0,' ...
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'options_.datafile, options_.varobs,options_.xls_sheet,options_.xls_range);'])
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end
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end
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oo_ = initial_estimation_checks(xparam1,dataset_,M_,estim_params_,options_,bayestopt_,oo_);
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if isequal(options_.mode_compute,0) && isempty(options_.mode_file) && options_.mh_posterior_mode_estimation==0
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if options_.smoother == 1
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[atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,decomp] = DsgeSmoother(xparam1,gend,data,data_index,missing_value);
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oo_.Smoother.SteadyState = ys;
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oo_.Smoother.TrendCoeffs = trend_coeff;
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if options_.filter_covariance
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oo_.Smoother.variance = P;
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end
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i_endo = bayestopt_.smoother_saved_var_list;
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if options_.nk ~= 0
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oo_.FilteredVariablesKStepAhead = ...
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aK(options_.filter_step_ahead,i_endo,:);
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if ~isempty(PK)
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oo_.FilteredVariablesKStepAheadVariances = ...
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PK(options_.filter_step_ahead,i_endo,i_endo,:);
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end
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if ~isempty(decomp)
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oo_.FilteredVariablesShockDecomposition = ...
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decomp(options_.filter_step_ahead,i_endo,:,:);
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end
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end
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for i=bayestopt_.smoother_saved_var_list'
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i1 = dr.order_var(bayestopt_.smoother_var_list(i));
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eval(['oo_.SmoothedVariables.' deblank(M_.endo_names(i1,:)) ' = atT(i,:)'';']);
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eval(['oo_.FilteredVariables.' deblank(M_.endo_names(i1,:)) ' = squeeze(aK(1,i,:));']);
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eval(['oo_.UpdatedVariables.' deblank(M_.endo_names(i1,:)) ' = updated_variables(i,:)'';']);
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end
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for i=1:M_.exo_nbr
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eval(['oo_.SmoothedShocks.' deblank(M_.exo_names(i,:)) ' = innov(i,:)'';']);
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end
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end
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return
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end
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if isequal(options_.mode_compute,6)
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% Erase previously computed optimal mh scale parameter.
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delete([M_.fname '_optimal_mh_scale_parameter.mat'])
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end
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%% Estimation of the posterior mode or likelihood mode
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if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
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switch options_.mode_compute
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case 1
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optim_options = optimset('display','iter','LargeScale','off', ...
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'MaxFunEvals',100000,'TolFun',1e-8,'TolX',1e-6);
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if isfield(options_,'optim_opt')
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eval(['optim_options = optimset(optim_options,' options_.optim_opt ');']);
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end
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[xparam1,fval,exitflag,output,lamdba,grad,hessian_fmincon] = ...
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fmincon(objective_function,xparam1,[],[],[],[],lb,ub,[],optim_options,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
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case 2
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error('ESTIMATION: mode_compute=2 option (Lester Ingber''s Adaptive Simulated Annealing) is no longer available')
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case 3
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optim_options = optimset('display','iter','MaxFunEvals',100000,'TolFun',1e-8,'TolX',1e-6);
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if isfield(options_,'optim_opt')
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eval(['optim_options = optimset(optim_options,' options_.optim_opt ');']);
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end
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[xparam1,fval,exitflag] = fminunc(objective_function,xparam1,optim_options,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
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case 4
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H0 = 1e-4*eye(nx);
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crit = 1e-7;
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nit = 1000;
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verbose = 2;
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[fval,xparam1,grad,hessian_csminwel,itct,fcount,retcodehat] = ...
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csminwel1(objective_function,xparam1,H0,[],crit,nit,options_.gradient_method,options_.gradient_epsilon,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
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disp(sprintf('Objective function at mode: %f',fval))
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case 5
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if isfield(options_,'hess')
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flag = options_.hess;
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else
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flag = 1;
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end
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if ~exist('igg','var'), % by M. Ratto
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hh=[];
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gg=[];
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igg=[];
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end % by M. Ratto
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if isfield(options_,'ftol')
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crit = options_.ftol;
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else
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crit = 1.e-5;
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end
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if isfield(options_,'nit')
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nit = options_.nit;
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else
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nit=1000;
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end
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[xparam1,hh,gg,fval,invhess] = newrat(objective_function,xparam1,hh,gg,igg,crit,nit,flag,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
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parameter_names = bayestopt_.name;
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save([M_.fname '_mode.mat'],'xparam1','hh','gg','fval','invhess','parameter_names');
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case 6
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fval = feval(objective_function,xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
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OldMode = fval;
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if ~exist('MeanPar','var')
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MeanPar = xparam1;
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end
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if exist('hh','var')
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CovJump = inv(hh);
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else% The covariance matrix is initialized with the prior
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% covariance (a diagonal matrix) %%Except for infinite variances ;-)
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varinit = 'prior';
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if strcmpi(varinit,'prior')
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stdev = bayestopt_.p2;
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indx = find(isinf(stdev));
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stdev(indx) = ones(length(indx),1)*sqrt(10);
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vars = stdev.^2;
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CovJump = diag(vars);
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elseif strcmpi(varinit,'eye')
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vars = ones(length(bayestopt_.p2),1)*0.1;
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CovJump = diag(vars);
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else
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disp('gmhmaxlik :: Error!')
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return
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end
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end
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OldPostVar = CovJump;
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Scale = options_.mh_jscale;
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for i=1:options_.Opt6Iter
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if i == 1
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if options_.Opt6Iter > 1
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flag = '';
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else
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flag = 'LastCall';
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end
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[xparam1,PostVar,Scale,PostMean] = ...
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gmhmaxlik(objective_function,xparam1,[lb ub],options_.Opt6Numb,Scale,flag,MeanPar,CovJump,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
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fval = feval(objective_function,xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
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options_.mh_jscale = Scale;
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mouvement = max(max(abs(PostVar-OldPostVar)));
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disp(' ')
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disp('========================================================== ')
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disp([' Change in the covariance matrix = ' num2str(mouvement) '.'])
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disp([' Mode improvement = ' num2str(abs(OldMode-fval))])
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disp([' New value of jscale = ' num2str(Scale)])
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disp('========================================================== ')
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OldMode = fval;
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else
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OldPostVar = PostVar;
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if i<options_.Opt6Iter
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flag = '';
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else
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flag = 'LastCall';
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end
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[xparam1,PostVar,Scale,PostMean] = ...
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gmhmaxlik(objective_function,xparam1,[lb ub],...
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options_.Opt6Numb,Scale,flag,PostMean,PostVar,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
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fval = feval(objective_function,xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
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options_.mh_jscale = Scale;
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mouvement = max(max(abs(PostVar-OldPostVar)));
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fval = DsgeLikelihood(xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
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disp(['Change in the covariance matrix = ' num2str(mouvement) '.'])
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disp(['Mode improvement = ' num2str(abs(OldMode-fval))])
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OldMode = fval;
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end
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hh = inv(PostVar);
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save([M_.fname '_mode.mat'],'xparam1','hh');
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save([M_.fname '_optimal_mh_scale_parameter.mat'],'Scale');
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bayestopt_.jscale = ones(length(xparam1),1)*Scale;
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end
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case 7
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% Matlab's simplex (Optimization toolbox needed).
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optim_options = optimset('display','iter','MaxFunEvals',1000000,'MaxIter',6000,'TolFun',1e-8,'TolX',1e-6);
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if isfield(options_,'optim_opt')
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eval(['optim_options = optimset(optim_options,' options_.optim_opt ');']);
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end
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[xparam1,fval,exitflag] = fminsearch(objective_function,xparam1,optim_options,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
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case 8
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% Dynare implementation of the simplex algorithm.
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[xparam1,fval,exitflag] = simplex_optimization-routine(objective_function,xparam1,optim_options,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
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case 101
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myoptions=soptions;
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myoptions(2)=1e-6; %accuracy of argument
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myoptions(3)=1e-6; %accuracy of function (see Solvopt p.29)
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myoptions(5)= 1.0;
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[xparam1,fval]=solvopt(xparam1,objective_function,[],myoptions,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
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case 102
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%simulating annealing
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% LB=zeros(size(xparam1))-20;
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% UB=zeros(size(xparam1))+20;
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LB = xparam1 - 1;
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UB = xparam1 + 1;
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neps=10;
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% Set input parameters.
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maxy=0;
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epsilon=1.0e-9;
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rt_=.10;
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t=15.0;
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ns=10;
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nt=10;
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maxevl=100000000;
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idisp =1;
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npar=length(xparam1);
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disp(['size of param',num2str(length(xparam1))])
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c=.1*ones(npar,1);
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%* Set input values of the input/output parameters.*
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vm=1*ones(npar,1);
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disp(['number of parameters= ' num2str(npar) 'max= ' num2str(maxy) 't= ' num2str(t)]);
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disp(['rt_= ' num2str(rt_) 'eps= ' num2str(epsilon) 'ns= ' num2str(ns)]);
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disp(['nt= ' num2str(nt) 'neps= ' num2str(neps) 'maxevl= ' num2str(maxevl)]);
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% disp(['iprint= ' num2str(iprint) 'seed= ' num2str(seed)]);
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disp ' ';
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disp ' ';
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disp(['starting values(x) ' num2str(xparam1')]);
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disp(['initial step length(vm) ' num2str(vm')]);
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disp(['lower bound(lb)', 'initial conditions', 'upper bound(ub)' ]);
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disp([LB xparam1 UB]);
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disp(['c vector ' num2str(c')]);
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[xparam1, fval, nacc, nfcnev, nobds, ier, t, vm] = sa(objective_function,xparam1,maxy,rt_,epsilon,ns,nt ...
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,neps,maxevl,LB,UB,c,idisp ,t,vm,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
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case 'prior'
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hh = diag(bayestopt_.p2.^2);
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otherwise
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if ischar(options_.mode_compute)
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[xparam1, fval, retcode ] = feval(options_.mode_compute,objective_function,xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
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else
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error(['dynare_estimation:: mode_compute = ' int2str(options_.mode_compute) ' option is unknown!'])
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end
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end
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if ~isequal(options_.mode_compute,6) && ~isequal(options_.mode_compute,'prior')
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if options_.cova_compute == 1
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hh = reshape(hessian(objective_function,xparam1, ...
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options_.gstep,dataset_,options_,M_,estim_params_,bayestopt_,oo_),nx,nx);
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end
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end
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parameter_names = bayestopt_.name;
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if options_.cova_compute
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save([M_.fname '_mode.mat'],'xparam1','hh','parameter_names');
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else
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save([M_.fname '_mode.mat'],'xparam1','parameter_names');
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end
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end
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if options_.cova_compute == 0
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hh = [];%NaN(length(xparam1),length(xparam1));
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end
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if ~options_.mh_posterior_mode_estimation && options_.cova_compute
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try
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chol(hh);
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catch
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disp(' ')
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disp('POSTERIOR KERNEL OPTIMIZATION PROBLEM!')
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disp(' (minus) the hessian matrix at the "mode" is not positive definite!')
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disp('=> posterior variance of the estimated parameters are not positive.')
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disp('You should try to change the initial values of the parameters using')
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disp('the estimated_params_init block, or use another optimization routine.')
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warning('The results below are most likely wrong!');
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end
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end
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if options_.mode_check == 1 && ~options_.mh_posterior_mode_estimation
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mode_check('objective_function',xparam1,hh,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
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end
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if ~options_.mh_posterior_mode_estimation
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if options_.cova_compute
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invhess = inv(hh);
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stdh = sqrt(diag(invhess));
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end
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else
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variances = bayestopt_.p2.*bayestopt_.p2;
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idInf = isinf(variances);
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variances(idInf) = 1;
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invhess = options_.mh_posterior_mode_estimation*diag(variances);
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xparam1 = bayestopt_.p5;
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idNaN = isnan(xparam1);
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xparam1(idNaN) = bayestopt_.p1(idNaN);
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xparam1 = transpose(xparam1);
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end
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if ~options_.cova_compute
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stdh = NaN(length(xparam1),1);
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end
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if any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
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disp(' ')
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disp('RESULTS FROM POSTERIOR MAXIMIZATION')
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tstath = zeros(nx,1);
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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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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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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('%-*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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eval(['oo_.posterior_mode.parameters.' name ' = xparam1(ip);']);
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eval(['oo_.posterior_std.parameters.' name ' = stdh(ip);']);
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ip = ip+1;
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end
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end
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if nvx
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ip = 1;
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disp('standard deviation of shocks')
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disp(tit1)
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for i=1:nvx
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k = estim_params_.var_exo(i,1);
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name = deblank(M_.exo_names(k,:));
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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);
|
|
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', name, ...
|
|
header_width,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', name, ...
|
|
header_width,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');
|
|
|
|
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,'{\\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',...
|
|
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,'\\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 = [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,'{\\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_.p1(ip),...
|
|
bayestopt_.p2(ip),...
|
|
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 = [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{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
|
|
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,'\\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 = [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{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_.p1(ip), ...
|
|
bayestopt_.p2(ip), ...
|
|
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 = [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{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_.p1(ip), ...
|
|
bayestopt_.p2(ip), ...
|
|
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 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
|
|
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
|
|
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_);
|
|
oo_ = PlotPosteriorDistributions(estim_params_, M_, options_, bayestopt_, oo_);
|
|
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',data,gend,data_index,missing_value);
|
|
end
|
|
xparam = get_posterior_parameters('mean');
|
|
set_all_parameters(xparam);
|
|
end
|
|
end
|
|
|
|
if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.pshape ...
|
|
> 0) && options_.load_mh_file)) ...
|
|
|| ~options_.smoother ) && M_.endo_nbr^2*gend < 1e7 && 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,:)'';']);
|
|
eval(['oo_.FilteredVariables.' deblank(M_.endo_names(i1,:)) ' = squeeze(aK(1,i,:));']);
|
|
eval(['oo_.UpdatedVariables.' deblank(M_.endo_names(i1,:)) ...
|
|
' = updated_variables(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,:));
|
|
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
|
|
texname = M_.exo_names_tex(i,1);
|
|
if isempty(TeXNAMES)
|
|
TeXNAMES = ['$ ' deblank(texname) ' $'];
|
|
else
|
|
TeXNAMES = char(TeXNAMES,['$ ' deblank(texname) ' $']);
|
|
end
|
|
end
|
|
title(name,'Interpreter','none')
|
|
eval(['oo_.SmoothedShocks.' deblank(M_.exo_names(i,:)) ' = innov(i,:)'';']);
|
|
end
|
|
eval(['print -depsc2 ' M_.fname '_SmoothedShocks' int2str(1) '.eps']);
|
|
if ~exist('OCTAVE_VERSION')
|
|
eval(['print -dpdf ' M_.fname '_SmoothedShocks' int2str(1)]);
|
|
saveas(hh,[M_.fname '_SmoothedShocks' int2str(1) '.fig']);
|
|
end
|
|
if options_.nograph, close(hh), end
|
|
if options_.TeX
|
|
fprintf(fidTeX,'\\begin{figure}[H]\n');
|
|
for jj = 1:M_.exo_nbr
|
|
fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(jj,:)),deblank(TeXNAMES(jj,:)));
|
|
end
|
|
fprintf(fidTeX,'\\centering \n');
|
|
fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_SmoothedShocks%s}\n',M_.fname,int2str(1));
|
|
fprintf(fidTeX,'\\caption{Smoothed shocks.}');
|
|
fprintf(fidTeX,'\\label{Fig:SmoothedShocks:%s}\n',int2str(1));
|
|
fprintf(fidTeX,'\\end{figure}\n');
|
|
fprintf(fidTeX,'\n');
|
|
fprintf(fidTeX,'%% End of TeX file.\n');
|
|
fclose(fidTeX);
|
|
end
|
|
else
|
|
for plt = 1:nbplt-1
|
|
hh = figure('Name','Smoothed shocks');
|
|
set(0,'CurrentFigure',hh)
|
|
NAMES = [];
|
|
if options_.TeX, TeXNAMES = []; end
|
|
for i=1:nstar
|
|
k = (plt-1)*nstar+i;
|
|
subplot(nr,nc,i);
|
|
plot([1 gend],[0 0],'-r','linewidth',.5)
|
|
hold on
|
|
plot(1:gend,innov(k,:),'-k','linewidth',1)
|
|
hold off
|
|
name = deblank(M_.exo_names(k,:));
|
|
if isempty(NAMES)
|
|
NAMES = name;
|
|
else
|
|
NAMES = char(NAMES,name);
|
|
end
|
|
if ~isempty(options_.XTick)
|
|
set(gca,'XTick',options_.XTick)
|
|
set(gca,'XTickLabel',options_.XTickLabel)
|
|
end
|
|
xlim([1 gend])
|
|
if options_.TeX
|
|
texname = M_.exo_names_tex(k,:);
|
|
if isempty(TeXNAMES)
|
|
TeXNAMES = ['$ ' deblank(texname) ' $'];
|
|
else
|
|
TeXNAMES = char(TeXNAMES,['$ ' deblank(texname) ' $']);
|
|
end
|
|
end
|
|
title(name,'Interpreter','none')
|
|
eval(['oo_.SmoothedShocks.' deblank(name) ' = innov(k,:)'';']);
|
|
end
|
|
eval(['print -depsc2 ' M_.fname '_SmoothedShocks' int2str(plt) '.eps']);
|
|
if ~exist('OCTAVE_VERSION')
|
|
eval(['print -dpdf ' M_.fname '_SmoothedShocks' int2str(plt)]);
|
|
saveas(hh,[M_.fname '_SmoothedShocks' int2str(plt) '.fig']);
|
|
end
|
|
if options_.nograph, close(hh), end
|
|
if options_.TeX
|
|
fprintf(fidTeX,'\\begin{figure}[H]\n');
|
|
for jj = 1:nstar
|
|
fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(jj,:)),deblank(TeXNAMES(jj,:)));
|
|
end
|
|
fprintf(fidTeX,'\\centering \n');
|
|
fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_SmoothedShocks%s}\n',M_.fname,int2str(plt));
|
|
fprintf(fidTeX,'\\caption{Smoothed shocks.}');
|
|
fprintf(fidTeX,'\\label{Fig:SmoothedShocks:%s}\n',int2str(plt));
|
|
fprintf(fidTeX,'\\end{figure}\n');
|
|
fprintf(fidTeX,'\n');
|
|
end
|
|
end
|
|
hh = figure('Name','Smoothed shocks');
|
|
set(0,'CurrentFigure',hh)
|
|
NAMES = [];
|
|
if options_.TeX, TeXNAMES = []; end
|
|
for i=1:M_.exo_nbr-(nbplt-1)*nstar
|
|
k = (nbplt-1)*nstar+i;
|
|
if lr ~= 0
|
|
subplot(lr,lc,i);
|
|
else
|
|
subplot(nr,nc,i);
|
|
end
|
|
plot([1 gend],[0 0],'-r','linewidth',0.5)
|
|
hold on
|
|
plot(1:gend,innov(k,:),'-k','linewidth',1)
|
|
hold off
|
|
name = deblank(M_.exo_names(k,:));
|
|
if isempty(NAMES)
|
|
NAMES = name;
|
|
else
|
|
NAMES = char(NAMES,name);
|
|
end
|
|
if ~isempty(options_.XTick)
|
|
set(gca,'XTick',options_.XTick)
|
|
set(gca,'XTickLabel',options_.XTickLabel)
|
|
end
|
|
xlim([1 gend])
|
|
if options_.TeX
|
|
texname = M_.exo_names_tex(k,:);
|
|
if isempty(TeXNAMES)
|
|
TeXNAMES = ['$ ' deblank(texname) ' $'];
|
|
else
|
|
TeXNAMES = char(TeXNAMES,['$ ' deblank(texname) ' $']);
|
|
end
|
|
end
|
|
title(name,'Interpreter','none')
|
|
eval(['oo_.SmoothedShocks.' deblank(name) ' = innov(k,:)'';']);
|
|
end
|
|
eval(['print -depsc2 ' M_.fname '_SmoothedShocks' int2str(nbplt) '.eps']);
|
|
if ~exist('OCTAVE_VERSION')
|
|
eval(['print -dpdf ' M_.fname '_SmoothedShocks' int2str(nbplt)]);
|
|
saveas(hh,[M_.fname '_SmoothedShocks' int2str(nbplt) '.fig']);
|
|
end
|
|
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...
|
|
%%
|
|
if options_.noconstant
|
|
yf = zeros(n_varobs,gend);
|
|
else
|
|
if options_.prefilter == 1
|
|
yf = atT(bayestopt_.mf,:)+repmat(bayestopt_.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
|
|
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),:));
|
|
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(indx(i),:),M_.endo_names,'exact');
|
|
texname = M_.endo_names_tex(idx,:);
|
|
if isempty(TeXNAMES)
|
|
TeXNAMES = ['$ ' deblank(texname) ' $'];
|
|
else
|
|
TeXNAMES = char(TeXNAMES,['$ ' deblank(texname) ' $']);
|
|
end
|
|
end
|
|
title(name,'Interpreter','none')
|
|
end
|
|
eval(['print -depsc2 ' M_.fname '_SmoothedObservationErrors' int2str(1) '.eps']);
|
|
if ~exist('OCTAVE_VERSION')
|
|
eval(['print -dpdf ' M_.fname '_SmoothedObservationErrors' int2str(1)]);
|
|
saveas(hh,[M_.fname '_SmoothedObservationErrors' int2str(1) '.fig']);
|
|
end
|
|
if options_.nograph, close(hh), end
|
|
if options_.TeX
|
|
fprintf(fidTeX,'\\begin{figure}[H]\n');
|
|
for jj = 1:number_of_plots_to_draw
|
|
fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(jj,:)),deblank(TeXNAMES(jj,:)));
|
|
end
|
|
fprintf(fidTeX,'\\centering \n');
|
|
fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_SmoothedObservationErrors%s}\n',M_.fname,int2str(1));
|
|
fprintf(fidTeX,'\\caption{Smoothed observation errors.}');
|
|
fprintf(fidTeX,'\\label{Fig:SmoothedObservationErrors:%s',int2str(1));
|
|
fprintf(fidTeX,'\\end{figure}\n');
|
|
fprintf(fidTeX,'\n');
|
|
fprintf(fidTeX,'%% End of TeX file.\n');
|
|
fclose(fidTeX);
|
|
end
|
|
else
|
|
for plt = 1:nbplt-1
|
|
hh = figure('Name','Smoothed observation errors');
|
|
set(0,'CurrentFigure',hh)
|
|
NAMES = [];
|
|
if options_.TeX, TeXNAMES = []; end
|
|
for i=1:nstar
|
|
k = (plt-1)*nstar+i;
|
|
subplot(nr,nc,i);
|
|
plot([1 gend],[0 0],'-r','linewidth',.5)
|
|
hold on
|
|
plot(1:gend,measurement_error(index(k),:),'-k','linewidth',1)
|
|
hold off
|
|
name = deblank(options_.varobs(index(k),:));
|
|
if isempty(NAMES)
|
|
NAMES = name;
|
|
else
|
|
NAMES = char(NAMES,name);
|
|
end
|
|
if ~isempty(options_.XTick)
|
|
set(gca,'XTick',options_.XTick)
|
|
set(gca,'XTickLabel',options_.XTickLabel)
|
|
end
|
|
if options_.TeX
|
|
idx = strmatch(options_.varobs(k),M_.endo_names,'exact');
|
|
texname = M_.endo_names_tex(idx,:);
|
|
if isempty(TeXNAMES)
|
|
TeXNAMES = ['$ ' deblank(texname) ' $'];
|
|
else
|
|
TeXNAMES = char(TeXNAMES,['$ ' deblank(texname) ' $']);
|
|
end
|
|
end
|
|
title(name,'Interpreter','none')
|
|
end
|
|
eval(['print -depsc2 ' M_.fname '_SmoothedObservationErrors' int2str(plt) '.eps']);
|
|
if ~exist('OCTAVE_VERSION')
|
|
eval(['print -dpdf ' M_.fname '_SmoothedObservationErrors' int2str(plt)]);
|
|
saveas(hh,[M_.fname '_SmoothedObservationErrors' int2str(plt) '.fig']);
|
|
end
|
|
if options_.nograph, close(hh), end
|
|
if options_.TeX
|
|
fprintf(fidTeX,'\\begin{figure}[H]\n');
|
|
for jj = 1:nstar
|
|
fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(jj,:)),deblank(TeXNAMES(jj,:)));
|
|
end
|
|
fprintf(fidTeX,'\\centering \n');
|
|
fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_SmoothedObservationErrors%s}\n',M_.fname,int2str(plt));
|
|
fprintf(fidTeX,'\\caption{Smoothed observation errors.}');
|
|
fprintf(fidTeX,'\\label{Fig:SmoothedObservationErrors:%s}\n',int2str(plt));
|
|
fprintf(fidTeX,'\\end{figure}\n');
|
|
fprintf(fidTeX,'\n');
|
|
end
|
|
end
|
|
hh = figure('Name','Smoothed observation errors');
|
|
set(0,'CurrentFigure',hh)
|
|
NAMES = [];
|
|
if options_.TeX, TeXNAMES = []; end
|
|
for i=1:number_of_plots_to_draw-(nbplt-1)*nstar
|
|
k = (nbplt-1)*nstar+i;
|
|
if lr ~= 0
|
|
subplot(lr,lc,i);
|
|
else
|
|
subplot(nr,nc,i);
|
|
end
|
|
plot([1 gend],[0 0],'-r','linewidth',0.5)
|
|
hold on
|
|
plot(1:gend,measurement_error(index(k),:),'-k','linewidth',1)
|
|
hold off
|
|
name = deblank(options_.varobs(index(k),:));
|
|
if isempty(NAMES)
|
|
NAMES = name;
|
|
else
|
|
NAMES = char(NAMES,name);
|
|
end
|
|
if ~isempty(options_.XTick)
|
|
set(gca,'XTick',options_.XTick)
|
|
set(gca,'XTickLabel',options_.XTickLabel)
|
|
end
|
|
if options_.TeX
|
|
idx = strmatch(options_.varobs(index(k)),M_.endo_names,'exact');
|
|
texname = M_.endo_names_tex(idx,:);
|
|
if isempty(TeXNAMES)
|
|
TeXNAMES = ['$ ' deblank(texname) ' $'];
|
|
else
|
|
TeXNAMES = char(TeXNAMES,['$ ' deblank(texname) ' $']);
|
|
end
|
|
end
|
|
title(name,'Interpreter','none');
|
|
end
|
|
eval(['print -depsc2 ' M_.fname '_SmoothedObservationErrors' int2str(nbplt) '.eps']);
|
|
if ~exist('OCTAVE_VERSION')
|
|
eval(['print -dpdf ' M_.fname '_SmoothedObservationErrors' int2str(nbplt)]);
|
|
saveas(hh,[M_.fname '_SmoothedObservationErrors' int2str(nbplt) '.fig']);
|
|
end
|
|
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,:));
|
|
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(i,:),M_.endo_names,'exact');
|
|
texname = M_.endo_names_tex(idx,:);
|
|
if isempty(TeXNAMES)
|
|
TeXNAMES = ['$ ' deblank(texname) ' $'];
|
|
else
|
|
TeXNAMES = char(TeXNAMES,['$ ' deblank(texname) ' $']);
|
|
end
|
|
end
|
|
title(name,'Interpreter','none')
|
|
end
|
|
eval(['print -depsc2 ' M_.fname '_HistoricalAndSmoothedVariables' int2str(1) '.eps']);
|
|
if ~exist('OCTAVE_VERSION')
|
|
eval(['print -dpdf ' M_.fname '_HistoricalAndSmoothedVariables' int2str(1)]);
|
|
saveas(hh,[M_.fname '_HistoricalAndSmoothedVariables' int2str(1) '.fig']);
|
|
end
|
|
if options_.nograph, close(hh), end
|
|
if options_.TeX
|
|
fprintf(fidTeX,'\\begin{figure}[H]\n');
|
|
for jj = 1:n_varobs
|
|
fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(jj,:)),deblank(TeXNAMES(jj,:)));
|
|
end
|
|
fprintf(fidTeX,'\\centering \n');
|
|
fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_HistoricalAndSmoothedVariables%s}\n',M_.fname,int2str(1));
|
|
fprintf(fidTeX,'\\caption{Historical and smoothed variables.}');
|
|
fprintf(fidTeX,'\\label{Fig:HistoricalAndSmoothedVariables:%s}\n',int2str(1));
|
|
fprintf(fidTeX,'\\end{figure}\n');
|
|
fprintf(fidTeX,'\n');
|
|
fprintf(fidTeX,'%% End of TeX file.\n');
|
|
fclose(fidTeX);
|
|
end
|
|
else
|
|
for plt = 1:nbplt-1
|
|
hh = figure('Name','Historical and smoothed variables');
|
|
set(0,'CurrentFigure',hh)
|
|
NAMES = [];
|
|
if options_.TeX, TeXNAMES = []; end
|
|
for i=1:nstar
|
|
k = (plt-1)*nstar+i;
|
|
subplot(nr,nc,i);
|
|
plot(1:gend,yf(k,:),'-r','linewidth',1)
|
|
hold on
|
|
plot(1:gend,rawdata(:,k),'-k','linewidth',1)
|
|
hold off
|
|
name = deblank(options_.varobs(k,:));
|
|
if isempty(NAMES)
|
|
NAMES = name;
|
|
else
|
|
NAMES = char(NAMES,name);
|
|
end
|
|
if ~isempty(options_.XTick)
|
|
set(gca,'XTick',options_.XTick)
|
|
set(gca,'XTickLabel',options_.XTickLabel)
|
|
end
|
|
xlim([1 gend])
|
|
if options_.TeX
|
|
idx = strmatch(options_.varobs(k,:),M_.endo_names,'exact');
|
|
texname = M_.endo_names_tex(idx,:);
|
|
if isempty(TeXNAMES)
|
|
TeXNAMES = ['$ ' deblank(texname) ' $'];
|
|
else
|
|
TeXNAMES = char(TeXNAMES,['$ ' deblank(texname) ' $']);
|
|
end
|
|
end
|
|
title(name,'Interpreter','none')
|
|
end
|
|
eval(['print -depsc2 ' M_.fname '_HistoricalAndSmoothedVariables' int2str(plt) '.eps']);
|
|
if ~exist('OCTAVE_VERSION')
|
|
eval(['print -dpdf ' M_.fname '_HistoricalAndSmoothedVariables' int2str(plt)]);
|
|
saveas(hh,[M_.fname '_HistoricalAndSmoothedVariables' int2str(plt) '.fig']);
|
|
end
|
|
if options_.nograph, close(hh), end
|
|
if options_.TeX
|
|
fprintf(fidTeX,'\\begin{figure}[H]\n');
|
|
for jj = 1:nstar
|
|
fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(jj,:)),deblank(TeXNAMES(jj,:)));
|
|
end
|
|
fprintf(fidTeX,'\\centering \n');
|
|
fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_HistoricalAndSmoothedVariables%s}\n',M_.fname,int2str(plt));
|
|
fprintf(fidTeX,'\\caption{Historical and smoothed variables.}');
|
|
fprintf(fidTeX,'\\label{Fig:HistoricalAndSmoothedVariables:%s}\n',int2str(plt));
|
|
fprintf(fidTeX,'\\end{figure}\n');
|
|
fprintf(fidTeX,'\n');
|
|
end
|
|
end
|
|
hh = figure('Name','Historical and smoothed variables');
|
|
set(0,'CurrentFigure',hh)
|
|
NAMES = [];
|
|
if options_.TeX, TeXNAMES = []; end
|
|
for i=1:n_varobs-(nbplt-1)*nstar
|
|
k = (nbplt-1)*nstar+i;
|
|
if lr ~= 0
|
|
subplot(lr,lc,i);
|
|
else
|
|
subplot(nr,nc,i);
|
|
end
|
|
plot(1:gend,yf(k,:),'-r','linewidth',1)
|
|
hold on
|
|
plot(1:gend,rawdata(:,k),'-k','linewidth',1)
|
|
hold off
|
|
name = deblank(options_.varobs(k,:));
|
|
if isempty(NAMES)
|
|
NAMES = name;
|
|
else
|
|
NAMES = char(NAMES,name);
|
|
end
|
|
if ~isempty(options_.XTick)
|
|
set(gca,'XTick',options_.XTick)
|
|
set(gca,'XTickLabel',options_.XTickLabel)
|
|
end
|
|
xlim([1 gend])
|
|
if options_.TeX
|
|
idx = strmatch(options_.varobs(i,:),M_.endo_names,'exact');
|
|
texname = M_.endo_names_tex(idx,:);
|
|
if isempty(TeXNAMES)
|
|
TeXNAMES = ['$ ' deblank(texname) ' $'];
|
|
else
|
|
TeXNAMES = char(TeXNAMES,['$ ' deblank(texname) ' $']);
|
|
end
|
|
end
|
|
title(name,'Interpreter','none');
|
|
end
|
|
eval(['print -depsc2 ' M_.fname '_HistoricalAndSmoothedVariables' int2str(nbplt) '.eps']);
|
|
if ~exist('OCTAVE_VERSION')
|
|
eval(['print -dpdf ' M_.fname '_HistoricalAndSmoothedVariables' int2str(nbplt)]);
|
|
saveas(hh,[M_.fname '_HistoricalAndSmoothedVariables' int2str(nbplt) '.fig']);
|
|
end
|
|
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 options_.forecast > 0 && options_.mh_replic == 0 && ~options_.load_mh_file
|
|
forecast(var_list_,'smoother');
|
|
end
|
|
|
|
if np > 0
|
|
pindx = estim_params_.param_vals(:,1);
|
|
save([M_.fname '_pindx.mat'] ,'pindx');
|
|
end
|
|
|