576 lines
25 KiB
Matlab
576 lines
25 KiB
Matlab
function [dataset_, dataset_info, xparam1, hh, M_, options_, oo_, estim_params_,bayestopt_, bounds] = dynare_estimation_init(var_list_, dname, gsa_flag, M_, options_, oo_, estim_params_, bayestopt_)
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% function dynare_estimation_init(var_list_, gsa_flag)
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% performs initialization tasks before estimation or
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% global sensitivity analysis
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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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% gsa_flag: flag for GSA operation (optional)
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% M_: structure storing the model information
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% options_: structure storing the options
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% oo_: structure storing the results
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% estim_params_: structure storing information about estimated
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% parameters
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% bayestopt_: structure storing information about priors
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% optim: structure storing optimization bounds
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% OUTPUTS
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% dataset_: the dataset after required transformation
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% dataset_info: Various informations about the dataset (descriptive statistics and missing observations).
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% xparam1: initial value of estimated parameters as returned by
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% set_prior() or loaded from mode-file
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% hh: hessian matrix at the loaded mode (or empty matrix)
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% M_: structure storing the model information
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% options_: structure storing the options
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% oo_: structure storing the results
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% estim_params_: structure storing information about estimated
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% parameters
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% bayestopt_: structure storing information about priors
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%
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% SPECIAL REQUIREMENTS
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% none
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% Copyright (C) 2003-2014 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 objective_function_penalty_base
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hh = [];
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if isempty(gsa_flag)
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gsa_flag = 0;
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else
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% Decide if a DSGE or DSGE-VAR has to be estimated.
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if ~isempty(strmatch('dsge_prior_weight',M_.param_names))
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options_.dsge_var = 1;
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end
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% Get the list of the endogenous variables for which posterior statistics wil be computed
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var_list_ = check_list_of_variables(options_, M_, var_list_);
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options_.varlist = var_list_;
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end
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if options_.dsge_var && options_.presample~=0
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error('DSGE-VAR does not support the presample option.')
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end
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% Set the number of observed variables.
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options_.number_of_observed_variables = length(options_.varobs);
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% Test if observed variables are declared.
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if ~options_.number_of_observed_variables
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error('VAROBS is missing!')
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end
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% Check that each declared observed variable is also an endogenous variable.
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for i = 1:options_.number_of_observed_variables
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id = strmatch(options_.varobs{i}, M_.endo_names, 'exact');
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if isempty(id)
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error(['Unknown variable (' options_.varobs{i} ')!'])
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end
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end
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% Check that a variable is not declared as observed more than once.
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if length(unique(options_.varobs))<length(options_.varobs)
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for i = 1:options_.number_of_observed_variables
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if length(strmatch(options_.varobs{i},options_.varobs,'exact'))>1
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error(['A variable cannot be declared as observed more than once (' options_.varobs{i} ')!'])
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end
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end
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end
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% Check the perturbation order (nonlinear filters with third order perturbation, or higher order, are not yet implemented).
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if options_.order>2
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error(['I cannot estimate a model with a ' int2str(options_.order) ' order approximation of the model!'])
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end
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% Set options_.lik_init equal to 3 if diffuse filter is used or kalman_algo refers to a diffuse filter algorithm.
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if isequal(options_.diffuse_filter,1) || (options_.kalman_algo>2)
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if isequal(options_.lik_init,2)
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error(['options diffuse_filter, lik_init and/or kalman_algo have contradictory settings'])
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else
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options_.lik_init = 3;
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end
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end
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% If options_.lik_init == 1
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% set by default options_.qz_criterium to 1-1e-6
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% and check options_.qz_criterium < 1-eps if options_.lik_init == 1
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% Else
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% set by default options_.qz_criterium to 1+1e-6
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if isequal(options_.lik_init,1)
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if isempty(options_.qz_criterium)
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options_.qz_criterium = 1-1e-6;
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elseif options_.qz_criterium > 1-eps
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error(['Estimation: option qz_criterium is too large for estimating ' ...
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'a stationary model. If your model contains unit roots, use ' ...
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'option diffuse_filter'])
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end
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elseif isempty(options_.qz_criterium)
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options_.qz_criterium = 1+1e-6;
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end
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% Set options related to filtered variables.
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if ~isequal(options_.filtered_vars,0) && isempty(options_.filter_step_ahead)
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options_.filter_step_ahead = 1;
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end
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if ~isequal(options_.filtered_vars,0) && isequal(options_.filter_step_ahead,0)
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options_.filter_step_ahead = 1;
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end
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if ~isequal(options_.filter_step_ahead,0)
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options_.nk = max(options_.filter_step_ahead);
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end
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% Set the name of the directory where (intermediary) results will be saved.
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if isempty(dname)
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M_.dname = M_.fname;
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else
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M_.dname = dname;
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end
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% Set priors over the estimated parameters.
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if ~isempty(estim_params_)
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[xparam1,estim_params_,bayestopt_,lb,ub,M_] = set_prior(estim_params_,M_,options_);
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end
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if ~isempty(bayestopt_) && any(bayestopt_.pshape==0) && any(bayestopt_.pshape~=0)
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error('Estimation must be either fully ML or fully Bayesian. Maybe you forgot to specify a prior distribution.')
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end
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% Check if a _prior_restrictions.m file exists
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if exist([M_.fname '_prior_restrictions.m'])
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options_.prior_restrictions.status = 1;
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options_.prior_restrictions.routine = str2func([M_.fname '_prior_restrictions']);
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end
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% Check that the provided mode_file is compatible with the current estimation settings.
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if ~isempty(estim_params_) && ~isempty(options_.mode_file) && ~options_.mh_posterior_mode_estimation
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number_of_estimated_parameters = length(xparam1);
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mode_file = load(options_.mode_file);
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if number_of_estimated_parameters>length(mode_file.xparam1)
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% More estimated parameters than parameters in the mode file.
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skipline()
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disp(['The posterior mode file ' options_.mode_file ' has been generated using another specification of the model or another model!'])
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disp(['Your mode file contains estimates for ' int2str(length(mode_file.xparam1)) ' parameters, while you are attempting to estimate ' int2str(number_of_estimated_parameters) ' parameters:'])
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md = []; xd = [];
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for i=1:number_of_estimated_parameters
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id = strmatch(deblank(bayestopt_.name(i,:)),mode_file.parameter_names,'exact');
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if isempty(id)
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disp(['--> Estimated parameter ' bayestopt_.name{i} ' is not present in the loaded mode file (prior mean will be used, if possible).'])
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else
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xd = [xd; i];
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md = [md; id];
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end
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end
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for i=1:length(mode_file.xparam1)
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id = strmatch(mode_file.parameter_names{i},bayestopt_.name,'exact');
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if isempty(id)
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disp(['--> Parameter ' mode_file.parameter_names{i} ' is not estimated according to the current mod file.'])
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end
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end
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if ~options_.mode_compute
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% The posterior mode is not estimated.
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error('Please change the mode_file option, the list of estimated parameters or set mode_compute>0.')
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else
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% The posterior mode is estimated, the Hessian evaluated at the mode is not needed so we set values for the parameters missing in the mode file using the prior mean.
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if ~isempty(xd)
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xparam1(xd) = mode_file.xparam1(md);
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else
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error('Please remove the mode_file option.')
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end
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end
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elseif number_of_estimated_parameters<length(mode_file.xparam1)
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% Less estimated parameters than parameters in the mode file.
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skipline()
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disp(['The posterior mode file ' options_.mode_file ' has been generated using another specification of the model or another model!'])
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disp(['Your mode file contains estimates for ' int2str(length(mode_file.xparam1)) ' parameters, while you are attempting to estimate only ' int2str(number_of_estimated_parameters) ' parameters:'])
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md = []; xd = [];
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for i=1:number_of_estimated_parameters
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id = strmatch(deblank(bayestopt_.name(i,:)),mode_file.parameter_names,'exact');
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if isempty(id)
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disp(['--> Estimated parameter ' deblank(bayestopt_.name(i,:)) ' is not present in the loaded mode file (prior mean will be used, if possible).'])
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else
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xd = [xd; i];
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md = [md; id];
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end
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end
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for i=1:length(mode_file.xparam1)
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id = strmatch(mode_file.parameter_names{i},bayestopt_.name,'exact');
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if isempty(id)
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disp(['--> Parameter ' mode_file.parameter_names{i} ' is not estimated according to the current mod file.'])
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end
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end
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if ~options_.mode_compute
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% The posterior mode is not estimated. If possible, fix the mode_file.
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if isequal(length(xd),number_of_estimated_parameters)
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disp('==> Fix mode file (remove unused parameters).')
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xparam1 = mode_file.xparam1(md);
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if isfield(mode_file,'hh')
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hh = mode_file.hh(md,md);
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end
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else
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error('Please change the mode_file option, the list of estimated parameters or set mode_compute>0.')
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end
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else
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% The posterior mode is estimated, the Hessian evaluated at the mode is not needed so we set values for the parameters missing in the mode file using the prior mean.
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if ~isempty(xd)
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xparam1(xd) = mode_file.xparam1(md);
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else
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% None of the estimated parameters are present in the mode_file.
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error('Please remove the mode_file option.')
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end
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end
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else
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% The number of declared estimated parameters match the number of parameters in the mode file.
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% Check that the parameters in the mode file and according to the current mod file are identical.
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if ~isfield(mode_file,'parameter_names')
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disp(['The posterior mode file ' options_.mode_file ' has been generated using an older version of Dynare. It cannot be verified if it matches the present model. Proceed at your own risk.'])
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mode_file.parameter_names=deblank(bayestopt_.name); %set names
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end
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if isequal(mode_file.parameter_names, bayestopt_.name)
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xparam1 = mode_file.xparam1;
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if isfield(mode_file,'hh')
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hh = mode_file.hh;
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end
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else
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skipline()
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disp(['The posterior mode file ' options_.mode_file ' has been generated using another specification of the model or another model!'])
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% Check if this only an ordering issue or if the missing parameters can be initialized with the prior mean.
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md = []; xd = [];
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for i=1:number_of_estimated_parameters
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id = strmatch(deblank(bayestopt_.name(i,:)), mode_file.parameter_names,'exact');
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if isempty(id)
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disp(['--> Estimated parameter ' bayestopt_.name{i} ' is not present in the loaded mode file.'])
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else
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xd = [xd; i];
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md = [md; id];
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end
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end
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if ~options_.mode_compute
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% The posterior mode is not estimated
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if isequal(length(xd), number_of_estimated_parameters)
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% This is an ordering issue.
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xparam1 = mode_file.xparam1(md);
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if isfield(mode_file,'hh')
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hh = mode_file.hh(md,md);
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end
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else
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error('Please change the mode_file option, the list of estimated parameters or set mode_compute>0.')
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end
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else
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% The posterior mode is estimated, the Hessian evaluated at the mode is not needed so we set values for the parameters missing in the mode file using the prior mean.
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if ~isempty(xd)
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xparam1(xd) = mode_file.xparam1(md);
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if isfield(mode_file,'hh')
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hh(xd,xd) = mode_file.hh(md,md);
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end
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else
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% None of the estimated parameters are present in the mode_file.
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error('Please remove the mode_file option.')
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end
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end
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end
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end
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skipline()
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end
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%check for calibrated covariances before updating parameters
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if ~isempty(estim_params_)
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estim_params_=check_for_calibrated_covariances(xparam1,estim_params_,M_);
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end
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%%read out calibration that was set in mod-file and can be used for initialization
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xparam1_calib=get_all_parameters(estim_params_,M_); %get calibrated parameters
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if ~any(isnan(xparam1_calib)) %all estimated parameters are calibrated
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estim_params_.full_calibration_detected=1;
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else
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estim_params_.full_calibration_detected=0;
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end
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if options_.use_calibration_initialization %set calibration as starting values
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if ~isempty(bayestopt_) && all(bayestopt_.pshape==0) && any(all(isnan([xparam1_calib xparam1]),2))
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error('Estimation: When using the use_calibration option with ML, the parameters must be properly initialized.')
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else
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[xparam1,estim_params_]=do_parameter_initialization(estim_params_,xparam1_calib,xparam1); %get explicitly initialized parameters that have precedence to calibrated values
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end
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end
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if ~isempty(estim_params_) && ~all(strcmp(fieldnames(estim_params_),'full_calibration_detected'))
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if ~isempty(bayestopt_) && any(bayestopt_.pshape > 0)
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% Plot prior densities.
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if ~options_.nograph && options_.plot_priors
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plot_priors(bayestopt_,M_,estim_params_,options_)
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end
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% Set prior bounds
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bounds = prior_bounds(bayestopt_,options_);
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bounds.lb = max(bounds.lb,lb);
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bounds.ub = min(bounds.ub,ub);
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else % estimated parameters but no declared priors
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% No priors are declared so Dynare will estimate the model by
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% maximum likelihood with inequality constraints for the parameters.
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options_.mh_replic = 0;% No metropolis.
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bounds.lb = lb;
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bounds.ub = ub;
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end
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% Test if initial values of the estimated parameters are all between the prior lower and upper bounds.
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if options_.use_calibration_initialization
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try
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check_prior_bounds(xparam1,bounds,M_,estim_params_,options_,bayestopt_)
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catch
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e = lasterror();
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fprintf('Cannot use parameter values from calibration as they violate the prior bounds.')
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rethrow(e);
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end
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else
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check_prior_bounds(xparam1,bounds,M_,estim_params_,options_,bayestopt_)
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end
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end
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if isempty(estim_params_) || all(strcmp(fieldnames(estim_params_),'full_calibration_detected'))% If estim_params_ is empty (e.g. when running the smoother on a calibrated model)
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if ~options_.smoother
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error('Estimation: the ''estimated_params'' block is mandatory (unless you are running a smoother)')
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end
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xparam1 = [];
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bayestopt_.jscale = [];
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bayestopt_.pshape = [];
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bayestopt_.p1 = [];
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bayestopt_.p2 = [];
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bayestopt_.p3 = [];
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bayestopt_.p4 = [];
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bayestopt_.p5 = [];
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bayestopt_.p6 = [];
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bayestopt_.p7 = [];
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estim_params_.nvx = 0;
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estim_params_.nvn = 0;
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estim_params_.ncx = 0;
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estim_params_.ncn = 0;
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estim_params_.np = 0;
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bounds.lb = [];
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bounds.ub = [];
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end
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% storing prior parameters in results
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oo_.prior.mean = bayestopt_.p1;
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oo_.prior.mode = bayestopt_.p5;
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oo_.prior.variance = diag(bayestopt_.p2.^2);
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oo_.prior.hyperparameters.first = bayestopt_.p6;
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oo_.prior.hyperparameters.second = bayestopt_.p7;
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% Is there a linear trend in the measurement equation?
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if ~isfield(options_,'trend_coeffs') % No!
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bayestopt_.with_trend = 0;
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else% Yes!
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bayestopt_.with_trend = 1;
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bayestopt_.trend_coeff = {};
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trend_coeffs = options_.trend_coeffs;
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nt = length(trend_coeffs);
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for i=1:options_.number_of_observed_variables
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if i > length(trend_coeffs)
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bayestopt_.trend_coeff{i} = '0';
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else
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bayestopt_.trend_coeff{i} = trend_coeffs{i};
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end
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end
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end
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% Set the "size" of penalty.
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objective_function_penalty_base = 1e8;
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% Get informations about the variables of the model.
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dr = set_state_space(oo_.dr,M_,options_);
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oo_.dr = dr;
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nstatic = M_.nstatic; % Number of static variables.
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npred = M_.nspred; % Number of predetermined variables.
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nspred = M_.nspred; % Number of predetermined variables in the state equation.
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% Setting resticted state space (observed + predetermined variables)
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var_obs_index = [];
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k1 = [];
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for i=1:options_.number_of_observed_variables
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var_obs_index = [var_obs_index; strmatch(options_.varobs{i},M_.endo_names(dr.order_var,:),'exact')];
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k1 = [k1; strmatch(options_.varobs{i},M_.endo_names, 'exact')];
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end
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% Define union of observed and state variables
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k2 = union(var_obs_index,[M_.nstatic+1:M_.nstatic+M_.nspred]', 'rows');
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% Set restrict_state to postion of observed + state variables in expanded state vector.
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oo_.dr.restrict_var_list = k2;
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bayestopt_.restrict_var_list = k2;
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% set mf0 to positions of state variables in restricted state vector for likelihood computation.
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[junk,bayestopt_.mf0] = ismember([M_.nstatic+1:M_.nstatic+M_.nspred]',k2);
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% Set mf1 to positions of observed variables in restricted state vector for likelihood computation.
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[junk,bayestopt_.mf1] = ismember(var_obs_index,k2);
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% Set mf2 to positions of observed variables in expanded state vector for filtering and smoothing.
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bayestopt_.mf2 = var_obs_index;
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bayestopt_.mfys = k1;
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[junk,ic] = intersect(k2,nstatic+(1:npred)');
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oo_.dr.restrict_columns = [ic; length(k2)+(1:nspred-npred)'];
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k3 = [];
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k3p = [];
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if options_.selected_variables_only
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if options_.forecast > 0 && options_.mh_replic == 0 && ~options_.load_mh_file
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fprintf('\nEstimation: The selected_variables_only option is incompatible with classical forecasts. It will be ignored.\n')
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k3 = (1:M_.endo_nbr)';
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k3p = (1:M_.endo_nbr)';
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else
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for i=1:size(var_list_,1)
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k3 = [k3; strmatch(var_list_(i,:),M_.endo_names(dr.order_var,:), 'exact')];
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k3p = [k3; strmatch(var_list_(i,:),M_.endo_names, 'exact')];
|
|
end
|
|
end
|
|
else
|
|
k3 = (1:M_.endo_nbr)';
|
|
k3p = (1:M_.endo_nbr)';
|
|
end
|
|
|
|
% Define union of observed and state variables
|
|
if options_.block == 1
|
|
k1 = k1';
|
|
[k2, i_posA, i_posB] = union(k1', M_.state_var', 'rows');
|
|
% Set restrict_state to postion of observed + state variables in expanded state vector.
|
|
oo_.dr.restrict_var_list = [k1(i_posA) M_.state_var(sort(i_posB))];
|
|
% set mf0 to positions of state variables in restricted state vector for likelihood computation.
|
|
[junk,bayestopt_.mf0] = ismember(M_.state_var',oo_.dr.restrict_var_list);
|
|
% Set mf1 to positions of observed variables in restricted state vector for likelihood computation.
|
|
[junk,bayestopt_.mf1] = ismember(k1,oo_.dr.restrict_var_list);
|
|
% Set mf2 to positions of observed variables in expanded state vector for filtering and smoothing.
|
|
bayestopt_.mf2 = var_obs_index;
|
|
bayestopt_.mfys = k1;
|
|
oo_.dr.restrict_columns = [size(i_posA,1)+(1:size(M_.state_var,2))];
|
|
[k2, i_posA, i_posB] = union(k3p, M_.state_var', 'rows');
|
|
bayestopt_.smoother_var_list = [k3p(i_posA); M_.state_var(sort(i_posB))'];
|
|
[junk,junk,bayestopt_.smoother_saved_var_list] = intersect(k3p,bayestopt_.smoother_var_list(:));
|
|
[junk,ic] = intersect(bayestopt_.smoother_var_list,M_.state_var);
|
|
bayestopt_.smoother_restrict_columns = ic;
|
|
[junk,bayestopt_.smoother_mf] = ismember(k1, bayestopt_.smoother_var_list);
|
|
else
|
|
k2 = union(var_obs_index,[M_.nstatic+1:M_.nstatic+M_.nspred]', 'rows');
|
|
% Set restrict_state to postion of observed + state variables in expanded state vector.
|
|
oo_.dr.restrict_var_list = k2;
|
|
% set mf0 to positions of state variables in restricted state vector for likelihood computation.
|
|
[junk,bayestopt_.mf0] = ismember([M_.nstatic+1:M_.nstatic+M_.nspred]',k2);
|
|
% Set mf1 to positions of observed variables in restricted state vector for likelihood computation.
|
|
[junk,bayestopt_.mf1] = ismember(var_obs_index,k2);
|
|
% Set mf2 to positions of observed variables in expanded state vector for filtering and smoothing.
|
|
bayestopt_.mf2 = var_obs_index;
|
|
bayestopt_.mfys = k1;
|
|
[junk,ic] = intersect(k2,nstatic+(1:npred)');
|
|
oo_.dr.restrict_columns = [ic; length(k2)+(1:nspred-npred)'];
|
|
bayestopt_.smoother_var_list = union(k2,k3);
|
|
[junk,junk,bayestopt_.smoother_saved_var_list] = intersect(k3,bayestopt_.smoother_var_list(:));
|
|
[junk,ic] = intersect(bayestopt_.smoother_var_list,nstatic+(1:npred)');
|
|
bayestopt_.smoother_restrict_columns = ic;
|
|
[junk,bayestopt_.smoother_mf] = ismember(var_obs_index, bayestopt_.smoother_var_list);
|
|
end;
|
|
|
|
if options_.analytic_derivation,
|
|
if options_.lik_init == 3,
|
|
error('analytic derivation is incompatible with diffuse filter')
|
|
end
|
|
options_.analytic_derivation = 1;
|
|
if ~(exist('sylvester3','file')==2),
|
|
dynareroot = strrep(which('dynare'),'dynare.m','');
|
|
addpath([dynareroot 'gensylv'])
|
|
end
|
|
if estim_params_.np,
|
|
% check if steady state changes param values
|
|
M=M_;
|
|
M.params(estim_params_.param_vals(:,1)) = xparam1(estim_params_.nvx+estim_params_.ncx+estim_params_.nvn+estim_params_.ncn+1:end); %set parameters
|
|
M.params(estim_params_.param_vals(:,1)) = M.params(estim_params_.param_vals(:,1))*1.01; %vary parameters
|
|
if options_.diffuse_filter
|
|
steadystate_check_flag = 0;
|
|
else
|
|
steadystate_check_flag = 1;
|
|
end
|
|
[tmp1, params] = evaluate_steady_state(oo_.steady_state,M,options_,oo_,steadystate_check_flag);
|
|
change_flag=any(find(params-M.params));
|
|
if change_flag,
|
|
skipline();
|
|
if any(isnan(params))
|
|
disp('After computing the steadystate, the following parameters are still NaN: '),
|
|
disp(M.param_names(isnan(params),:))
|
|
end
|
|
if any(find(params(~isnan(params))-M.params(~isnan(params))))
|
|
disp('The steadystate file changed the values for the following parameters: '),
|
|
disp(M.param_names(find(params(~isnan(params))-M.params(~isnan(params))),:))
|
|
end
|
|
disp('The derivatives of jacobian and steady-state will be computed numerically'),
|
|
disp('(re-set options_.analytic_derivation_mode= -2)'),
|
|
options_.analytic_derivation_mode= -2;
|
|
end
|
|
end
|
|
end
|
|
|
|
% If jscale isn't specified for an estimated parameter, use global option options_.jscale, set to 0.2, by default.
|
|
k = find(isnan(bayestopt_.jscale));
|
|
bayestopt_.jscale(k) = options_.mh_jscale;
|
|
|
|
% Build the dataset
|
|
if ~isempty(options_.datafile)
|
|
[pathstr,name,ext] = fileparts(options_.datafile);
|
|
if strcmp(name,M_.fname)
|
|
error('Data-file and mod-file are not allowed to have the same name. Please change the name of the data file.')
|
|
end
|
|
end
|
|
|
|
[dataset_, dataset_info, newdatainterfaceflag] = makedataset(options_, options_.dsge_var*options_.dsge_varlag, gsa_flag);
|
|
|
|
% Set options_.nobs if needed
|
|
if newdatainterfaceflag
|
|
options_.nobs = dataset_.nobs;
|
|
end
|
|
|
|
% setting steadystate_check_flag option
|
|
if options_.diffuse_filter
|
|
steadystate_check_flag = 0;
|
|
else
|
|
steadystate_check_flag = 1;
|
|
end
|
|
|
|
% If the steady state of the observed variables is non zero, set noconstant equal 0 ()
|
|
M = M_;
|
|
nvx = estim_params_.nvx;
|
|
ncx = estim_params_.ncx;
|
|
nvn = estim_params_.nvn;
|
|
ncn = estim_params_.ncn;
|
|
if estim_params_.np
|
|
M.params(estim_params_.param_vals(:,1)) = xparam1(nvx+ncx+nvn+ncn+1:end);
|
|
end
|
|
[oo_.steady_state, params,info] = evaluate_steady_state(oo_.steady_state,M,options_,oo_,steadystate_check_flag);
|
|
|
|
if info(1)
|
|
fprintf('\ndynare_estimation_init:: The steady state at the initial parameters cannot be computed.')
|
|
print_info(info, 0, options_);
|
|
end
|
|
|
|
if all(abs(oo_.steady_state(bayestopt_.mfys))<1e-9)
|
|
options_.noconstant = 1;
|
|
else
|
|
options_.noconstant = 0;
|
|
% If the data are prefiltered then there must not be constants in the
|
|
% measurement equation of the DSGE model or in the DSGE-VAR model.
|
|
if options_.prefilter
|
|
skipline()
|
|
disp('You have specified the option "prefilter" to demean your data but the')
|
|
disp('steady state of of the observed variables is non zero.')
|
|
disp('Either change the measurement equations, by centering the observed')
|
|
disp('variables in the model block, or drop the prefiltering.')
|
|
error('The option "prefilter" is inconsistent with the non-zero mean measurement equations in the model.')
|
|
end
|
|
end
|