irfmatching: add options and checks to options_mom_
Willi Mutschler
parent
613b3869e1
commit
c45c0efeba
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@ -1,33 +1,33 @@
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function options_mom_ = default_option_mom_values(options_mom_, options_, dname, doBayesianEstimation)
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% function options_mom_ = default_option_mom_values(options_mom_, options_, dname, doBayesianEstimation)
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% Returns structure containing the options for method_of_moments command
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% options_mom_ is local and contains default and user-specified values for
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% all settings needed for the method of moments estimation. Some options,
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% though, are set by the preprocessor into options_ and we copy these over.
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% The idea is to be independent of options_ and have full control of the
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% estimation instead of possibly having to deal with options chosen somewhere
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% else in the mod file.
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% =========================================================================
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% options_mom_ = default_option_mom_values(options_mom_, options_, dname, doBayesianEstimation)
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% -------------------------------------------------------------------------
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% Returns structure containing the options for method_of_moments command.
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% Note 1: options_mom_ is local and contains default and user-specified
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% values for all settings needed for the method of moments estimation.
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% Some options, though, are set by the preprocessor into options_ and we
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% copy these over. The idea is to be independent of options_ and have full
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% control of the estimation instead of possibly having to deal with options
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% chosen somewhere else in the mod file.
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% Note 2: we call a "mode" the minimum of the objective function, i.e.
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% the parameter vector that minimizes the distance between the moments/irfs
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% computed from the model and the moments/irfs computed from the data.
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% -------------------------------------------------------------------------
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% INPUTS
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% o options_mom_: [structure] information about all (user-specified and updated) settings used in estimation (options_mom_)
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% o options_: [structure] information on global options
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% o dname: [string] name of directory to store results
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% o doBayesianEstimation [boolean] indicator whether we do Bayesian estimation
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% o options_mom_: [structure] all user-specified settings (from the method_of_moments command)
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% o options_: [structure] global options
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% o dname: [string] default name of directory to store results
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% o doBayesianEstimation [boolean] indicator whether we do Bayesian estimation
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% -------------------------------------------------------------------------
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% OUTPUTS
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% o oo_: [structure] storage for results (oo_)
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% o options_mom_: [structure] information about all (user-specified and updated) settings used in estimation (options_mom_)
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% o options_mom_: [structure] all user-specified and updated settings required for method_of_moments estimation
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% -------------------------------------------------------------------------
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% This function is called by
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% o mom.run
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% -------------------------------------------------------------------------
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% This function calls
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% o set_default_option
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% o user_has_matlab_license
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% o user_has_octave_forge_package
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% o set_default_option
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% o user_has_matlab_license
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% o user_has_octave_forge_package
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% -------------------------------------------------------------------------
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% Copyright © 2023 Dynare Team
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@ -46,34 +46,43 @@ function options_mom_ = default_option_mom_values(options_mom_, options_, dname,
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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 <https://www.gnu.org/licenses/>.
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% =========================================================================
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mom_method = options_mom_.mom.mom_method; % this is a required option
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% -------------------------------------------------------------------------
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% LIMITATIONS
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% -------------------------------------------------------------------------
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if options_.logged_steady_state || options_.loglinear
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error('method_of_moments: The loglinear option is not supported. Please append the required logged variables as auxiliary equations.')
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error('method_of_moments: The loglinear option is not supported. Please append the required logged variables as auxiliary equations.');
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else
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options_mom_.logged_steady_state = 0;
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options_mom_.loglinear = false;
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end
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options_mom_.hessian.use_penalized_objective = false; % penalized objective not yet
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% options related to variable declarations
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if isfield(options_,'trend_coeffs')
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error('method_of_moments: %s does not allow for trend in data',mom_method)
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if isfield(options_mom_,'hessian') && options_mom_.hessian.use_penalized_objective
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warning('method_of_moments: The ''use_penalized_objective_for_hessian'' option is not supported yet and will be skipped.');
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end
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options_mom_.hessian.use_penalized_objective = false; % penalized objective not yet supported
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if isfield(options_,'trend_coeffs')
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error('method_of_moments: %s does not allow for trend in data',mom_method);
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end
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% options related to endogenous prior restrictions are not supported
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if ~isempty(options_.endogenous_prior_restrictions.irf) && ~isempty(options_.endogenous_prior_restrictions.moment)
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fprintf('method_of_moments: Endogenous prior restrictions are not supported yet and will be skipped.\n')
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warning('method_of_moments: Endogenous prior restrictions are not supported yet and will be skipped.');
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end
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options_mom_.endogenous_prior_restrictions.irf = {};
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options_mom_.endogenous_prior_restrictions.moment = {};
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options_mom_.mom.analytic_jacobian_optimizers = [1, 3, 4, 13, 101]; % these are currently supported optimizers that are able to use the analytical_jacobian option
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if isfield(options_mom_,'bayesian_irf') && options_mom_.bayesian_irf % do we need this at all??
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warning('method_of_moments: The ''bayesian_irf'' option is not supported yet and will be skipped.');
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end
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options_mom_.bayesian_irf = false;
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if strcmp(mom_method,'IRF_MATCHING')
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if isfield(options_mom_.mom,'penalized_estimator') && options_mom_.mom.penalized_estimator
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warning('method_of_moments: The ''penalized_estimator'' option is not supported yet for IRF_MATCHING and will be ignored.');
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end
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options_mom_.mom.penalized_estimator = false;
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end
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% -------------------------------------------------------------------------
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% OPTIONS POSSIBLY SET BY THE USER
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@ -87,7 +96,6 @@ options_mom_ = set_default_option(options_mom_,'nograph',false); % do no
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options_mom_ = set_default_option(options_mom_,'noprint',false); % do not print output to console
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options_mom_ = set_default_option(options_mom_,'TeX',false); % print TeX tables and graphics
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options_mom_.mom = set_default_option(options_mom_.mom,'verbose',false); % display and store intermediate estimation results
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%options_mom_ = set_default_option(options_mom_,'verbosity',false); %
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if doBayesianEstimation
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options_mom_ = set_default_option(options_mom_,'plot_priors',true); % control plotting of priors
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options_mom_ = set_default_option(options_mom_,'prior_trunc',1e-10); % probability of extreme values of the prior density that is ignored when computing bounds for the parameters
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@ -111,6 +119,16 @@ end
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if strcmp(mom_method,'GMM')
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options_mom_.mom = set_default_option(options_mom_.mom,'analytic_standard_errors',false); % compute standard errors numerically (0) or analytically (1). Analytical derivatives are only available for GMM.
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end
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if strcmp(mom_method,'IRF_MATCHING')
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if ~isfield(options_mom_.mom,'irf_matching_file')
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options_mom_.mom.irf_matching_file = []; % irf_matching file enables to transform model IRFs before matching them to data IRFs
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end
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options_mom_.mom.irf_matching_file = set_default_option(options_mom_.mom.irf_matching_file,'name','');
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options_mom_.mom = set_default_option(options_mom_.mom,'simulation_method','STOCH_SIMUL'); % simulation method used to compute IRFs
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options_mom_ = set_default_option(options_mom_,'add_tiny_number_to_cholesky',1e-14); % add tiny number to Cholesky factor to avoid numerical problems when computing IRFs
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options_mom_ = set_default_option(options_mom_,'drop',100); % truncation / burnin for order>1 irf simulations
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options_mom_ = set_default_option(options_mom_,'relative_irf',false); % requests the computation of normalized IRFs
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end
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% data related options
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if strcmp(mom_method,'GMM') || strcmp(mom_method,'SMM')
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@ -123,12 +141,14 @@ if strcmp(mom_method,'GMM') || strcmp(mom_method,'SMM')
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end
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% optimization related
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if (isoctave && user_has_octave_forge_package('optim')) || (~isoctave && user_has_matlab_license('optimization_toolbox'))
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if strcmp(mom_method,'GMM') || strcmp(mom_method,'SMM')
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if strcmp(mom_method,'GMM') || strcmp(mom_method,'SMM')
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if (isoctave && user_has_octave_forge_package('optim')) || (~isoctave && user_has_matlab_license('optimization_toolbox'))
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options_mom_ = set_default_option(options_mom_,'mode_compute',13); % specifies lsqnonlin as default optimizer for minimization
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else
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options_mom_ = set_default_option(options_mom_,'mode_compute',5); % specifies newrat as fallback default option for minimization
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end
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else
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options_mom_ = set_default_option(options_mom_,'mode_compute',4); % specifies csminwel as fallback default option for minimization
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elseif strcmp(mom_method,'IRF_MATCHING')
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options_mom_ = set_default_option(options_mom_,'mode_compute',5); % specifies newrat as fallback default option for minimization
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end
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options_mom_ = set_default_option(options_mom_,'additional_optimizer_steps',[]); % vector of additional mode-finders run after mode_compute
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options_mom_ = set_default_option(options_mom_,'optim_opt',[]); % a list of NAME and VALUE pairs to set options for the optimization routines. Available options depend on mode_compute
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@ -136,9 +156,20 @@ options_mom_ = set_default_option(options_mom_,'silent_optimizer',false);
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options_mom_ = set_default_option(options_mom_,'huge_number',1e7); % value for replacing the infinite bounds on parameters by finite numbers. Used by some optimizers for numerical reasons
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options_mom_.mom = set_default_option(options_mom_.mom,'analytic_jacobian',false); % use analytic Jacobian in optimization, only available for GMM and gradient-based optimizers
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options_mom_.optimizer_vec = [options_mom_.mode_compute;num2cell(options_mom_.additional_optimizer_steps)];
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options_mom_.mom.analytic_jacobian_optimizers = [1, 3, 4, 13, 101]; % these are currently supported optimizers that are able to use the analytic_jacobian option
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options_mom_.analytic_derivation = 0; % force to 0 as we check this seperately in dynare_minimize_objective.m
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options_mom_ = set_default_option(options_mom_,'mode_file',''); % name of the file containing initial values for the mode
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options_mom_ = set_default_option(options_mom_,'cova_compute',true); % 1: computed covariance via Hessian after the computation of the mode, 0: turn off computation of covariance matrix
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% perturbation related
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options_mom_ = set_default_option(options_mom_,'order',1); % order of Taylor approximation in perturbation
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if strcmp(mom_method,'IRF_MATCHING') % number of simulated series used to compute IRFs
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if options_mom_.order == 1
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options_mom_ = set_default_option(options_mom_,'replic',1);
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else
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options_mom_ = set_default_option(options_mom_,'replic',50);
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end
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end
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options_mom_ = set_default_option(options_mom_,'pruning',false); % use pruned state space system at order>1
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options_mom_ = set_default_option(options_mom_,'aim_solver',false); % use AIM algorithm to compute perturbation approximation instead of mjdgges
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options_mom_ = set_default_option(options_mom_,'k_order_solver',false); % use k_order_perturbation instead of mjdgges
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@ -161,12 +192,121 @@ options_mom_ = set_default_option(options_mom_,'lyapunov_complex_threshold',1e-1
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options_mom_ = set_default_option(options_mom_,'lyapunov_fixed_point_tol',1e-10); % convergence criterion used in the fixed point Lyapunov solver
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options_mom_ = set_default_option(options_mom_,'lyapunov_doubling_tol',1e-16); % convergence criterion used in the doubling algorithm
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% mode check plot
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% Bayesian MCMC related
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if doBayesianEstimation
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options_mom_ = set_default_option(options_mom_,'mh_replic',0); % number of draws in Metropolis-Hastings and slice samplers
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options_mom_ = set_default_option(options_mom_,'mh_posterior_mode_estimation',false); % skip optimizer-based mode-finding and instead compute the mode based on a run of a MCMC
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options_mom_ = set_default_option(options_mom_,'load_mh_file',false); % add to previous Metropolis-Hastings or slice simulations instead of starting from scratch
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options_mom_ = set_default_option(options_mom_,'load_results_after_load_mh',false); % load the previously computed convergence diagnostics, marginal data density, and posterior statistics from an existing mom_results file instead of recomputing them
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if options_mom_.mh_replic > 0 || options_mom_.load_mh_file
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options_mom_ = set_default_option(options_mom_,'sub_draws',[]);
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options_mom_ = set_default_option(options_mom_,'posterior_max_subsample_draws',1200);
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options_mom_ = set_default_option(options_mom_,'mh_nblck',2); % number of parallel chains for Metropolis-Hastings or slice algorithm
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options_mom_ = set_default_option(options_mom_,'mh_drop',0.5); % fraction of initially generated parameter vectors to be dropped as a burn-in before using posterior simulations
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options_mom_ = set_default_option(options_mom_,'mh_conf_sig',0.9); % confidence/HPD interval used for the computation of prior and posterior statistics
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options_mom_ = set_default_option(options_mom_,'mh_recover',false); % attempts to recover a Metropolis-Hastings simulation that crashed prematurely
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options_mom_ = set_default_option(options_mom_,'MCMC_jumping_covariance','hessian'); % which covariance to use for the proposal density of the MCMC sampler
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if ~isfield(options_mom_,'mh_initialize_from_previous_mcmc')
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options_mom_.mh_initialize_from_previous_mcmc.status = false; % pick initial values for new MCMC from a previous one
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end
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options_mom_.mh_initialize_from_previous_mcmc = set_default_option(options_mom_.mh_initialize_from_previous_mcmc,'directory',''); % pick initial values for new MCMC from a previous one: directory
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options_mom_.mh_initialize_from_previous_mcmc = set_default_option(options_mom_.mh_initialize_from_previous_mcmc,'record',''); % pick initial values for new MCMC from a previous one: record file name
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options_mom_.mh_initialize_from_previous_mcmc = set_default_option(options_mom_.mh_initialize_from_previous_mcmc,'prior',''); % pick initial values for new MCMC from a previous one: prior file name
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if ~isfield(options_mom_,'posterior_sampler_options')
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options_mom_.posterior_sampler_options = [];
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end
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options_mom_.posterior_sampler_options = set_default_option(options_mom_.posterior_sampler_options,'posterior_sampling_method','random_walk_metropolis_hastings'); % selects the sampler used to sample from the posterior distribution during Bayesian estimation
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options_mom_.posterior_sampler_options = set_default_option(options_mom_.posterior_sampler_options,'sampling_opt',[]); % used to set options for the posterior sampling methods
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switch options_mom_.posterior_sampler_options.posterior_sampling_method
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case 'random_walk_metropolis_hastings'
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if ~isfield(options_mom_.posterior_sampler_options,'rwmh')
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options_mom_.posterior_sampler_options.rwmh = [];
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end
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options_mom_.posterior_sampler_options.rwmh = set_default_option(options_mom_.posterior_sampler_options.rwmh,'proposal_distribution','rand_multivariate_normal');
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options_mom_.posterior_sampler_options.rwmh = set_default_option(options_mom_.posterior_sampler_options.rwmh,'student_degrees_of_freedom',3);
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options_mom_.posterior_sampler_options.rwmh = set_default_option(options_mom_.posterior_sampler_options.rwmh,'use_mh_covariance_matrix',false);
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options_mom_.posterior_sampler_options.rwmh = set_default_option(options_mom_.posterior_sampler_options.rwmh,'save_tmp_file',false);
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case 'tailored_random_block_metropolis_hastings'
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if ~isfield(options_mom_.posterior_sampler_options,'tarb')
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options_mom_.posterior_sampler_options.tarb = [];
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end
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options_mom_.posterior_sampler_options.tarb = set_default_option(options_mom_.posterior_sampler_options.tarb,'proposal_distribution','rand_multivariate_normal');
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options_mom_.posterior_sampler_options.tarb = set_default_option(options_mom_.posterior_sampler_options.tarb,'student_degrees_of_freedom',3);
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options_mom_.posterior_sampler_options.tarb = set_default_option(options_mom_.posterior_sampler_options.tarb,'mode_compute',4);
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options_mom_.posterior_sampler_options.tarb = set_default_option(options_mom_.posterior_sampler_options.tarb,'new_block_probability',0.25);
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options_mom_.posterior_sampler_options.tarb = set_default_option(options_mom_.posterior_sampler_options.tarb,'optim_opt','');
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options_mom_.posterior_sampler_options.tarb = set_default_option(options_mom_.posterior_sampler_options.tarb,'save_tmp_file',true);
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case 'slice'
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if ~isfield(options_mom_.posterior_sampler_options,'slice')
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options_mom_.posterior_sampler_options.slice = [];
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end
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options_mom_.posterior_sampler_options.slice = set_default_option(options_mom_.posterior_sampler_options.slice,'proposal_distribution','');
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options_mom_.posterior_sampler_options.slice = set_default_option(options_mom_.posterior_sampler_options.slice,'rotated',0);
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options_mom_.posterior_sampler_options.slice = set_default_option(options_mom_.posterior_sampler_options.slice,'slice_initialize_with_mode',false); % must be used with rotated
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options_mom_.posterior_sampler_options.slice = set_default_option(options_mom_.posterior_sampler_options.slice,'use_mh_covariance_matrix',false); % must be used with rotated
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options_mom_.posterior_sampler_options.slice = set_default_option(options_mom_.posterior_sampler_options.slice,'WR',[]);
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options_mom_.posterior_sampler_options.slice = set_default_option(options_mom_.posterior_sampler_options.slice,'mode_files',[]);
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options_mom_.posterior_sampler_options.slice = set_default_option(options_mom_.posterior_sampler_options.slice,'mode',[]);
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options_mom_.posterior_sampler_options.slice = set_default_option(options_mom_.posterior_sampler_options.slice,'initial_step_size',0.8);
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options_mom_.posterior_sampler_options.slice = set_default_option(options_mom_.posterior_sampler_options.slice,'save_tmp_file',true);
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case 'independent_metropolis_hastings'
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if ~isfield(options_mom_.posterior_sampler_options,'imh')
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options_mom_.posterior_sampler_options.imh = [];
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end
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options_mom_.posterior_sampler_options.imh = set_default_option(options_mom_.posterior_sampler_options.imh,'proposal_distribution','rand_multivariate_normal');
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options_mom_.posterior_sampler_options.imh = set_default_option(options_mom_.posterior_sampler_options.imh,'use_mh_covariance_matrix',false);
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options_mom_.posterior_sampler_options.imh = set_default_option(options_mom_.posterior_sampler_options.imh,'save_tmp_file',false);
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end
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if ~strcmp(options_mom_.posterior_sampler_options.posterior_sampling_method,'slice')
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options_mom_ = set_default_option(options_mom_,'mh_init_scale_factor',2);
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options_mom_ = set_default_option(options_mom_,'mh_jscale',[]);
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end
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% mh_tune_jscale options
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if strcmp(options_mom_.posterior_sampler_options.posterior_sampling_method,'random_walk_metropolis_hastings')
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if ~isfield(options_mom_,'mh_tune_jscale')
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options_mom_.mh_tune_jscale = [];
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end
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options_mom_.mh_tune_jscale = set_default_option(options_mom_.mh_tune_jscale,'status',false);
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options_mom_.mh_tune_jscale = set_default_option(options_mom_.mh_tune_jscale,'target',0.33);
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options_mom_.mh_tune_jscale = set_default_option(options_mom_.mh_tune_jscale,'guess',[]);
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options_mom_.mh_tune_jscale.maxiter = options_.mh_tune_jscale.maxiter;
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options_mom_.mh_tune_jscale.rho = options_.mh_tune_jscale.rho;
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options_mom_.mh_tune_jscale.stepsize = options_.mh_tune_jscale.stepsize;
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options_mom_.mh_tune_jscale.c1 = options_.mh_tune_jscale.c1;
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options_mom_.mh_tune_jscale.c2 = options_.mh_tune_jscale.c2;
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options_mom_.mh_tune_jscale.c3 = options_.mh_tune_jscale.c3;
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end
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% convergence diagnostics
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options_mom_ = set_default_option(options_mom_,'nodiagnostic',false);
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if ~isfield(options_mom_,'convergence')
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options_mom_.convergence = [];
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end
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if ~isfield(options_mom_.convergence,'geweke')
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options_mom_.convergence.geweke = [];
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end
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if ~isfield(options_mom_.convergence,'rafterylewis')
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options_mom_.convergence.rafterylewis = [];
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end
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if ~isfield(options_mom_.convergence,'brooksgelman')
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options_mom_.convergence.brooksgelman = [];
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end
|
||||
options_mom_.convergence.geweke = set_default_option(options_mom_.convergence.geweke,'taper_steps', [4 8 15]);
|
||||
options_mom_.convergence.geweke = set_default_option(options_mom_.convergence.geweke,'geweke_interval', [0.2 0.5]);
|
||||
options_mom_.convergence.rafterylewis = set_default_option(options_mom_.convergence.rafterylewis,'indicator', false);
|
||||
options_mom_.convergence.rafterylewis = set_default_option(options_mom_.convergence.rafterylewis,'qrs', [0.025 0.005 0.95]);
|
||||
options_mom_.convergence.brooksgelman = set_default_option(options_mom_.convergence.brooksgelman,'plotrows',3);
|
||||
end
|
||||
end
|
||||
|
||||
% mode check plot options
|
||||
options_mom_.mode_check.nolik = false; % we don't do likelihood (also this initializes mode_check substructure)
|
||||
options_mom_.mode_check = set_default_option(options_mom_.mode_check,'status',false); % plot the target function for values around the computed minimum for each estimated parameter in turn. This is helpful to diagnose problems with the optimizer.
|
||||
options_mom_.mode_check = set_default_option(options_mom_.mode_check,'neighbourhood_size',.5); % width of the window around the computed minimum to be displayed on the diagnostic plots. This width is expressed in percentage deviation. The Inf value is allowed, and will trigger a plot over the entire domain
|
||||
options_mom_.mode_check = set_default_option(options_mom_.mode_check,'symmetric_plots',true); % ensure that the check plots are symmetric around the minimum. A value of 0 allows to have asymmetric plots, which can be useful if the minimum is close to a domain boundary, or in conjunction with neighbourhood_size = Inf when the domain is not the entire real line
|
||||
options_mom_.mode_check = set_default_option(options_mom_.mode_check,'number_of_points',20); % number of points around the minimum where the target function is evaluated (for each parameter)
|
||||
options_mom_.mode_check = set_default_option(options_mom_.mode_check,'status',false); % plot the target function for values around the computed mode for each estimated parameter in turn. This is helpful to diagnose problems with the optimizer.
|
||||
options_mom_.mode_check = set_default_option(options_mom_.mode_check,'neighbourhood_size',.5); % width of the window around the computed mode to be displayed on the diagnostic plots. This width is expressed in percentage deviation. The Inf value is allowed, and will trigger a plot over the entire domain
|
||||
options_mom_.mode_check = set_default_option(options_mom_.mode_check,'symmetric_plots',true); % ensure that the check plots are symmetric around the mode. A value of 0 allows to have asymmetric plots, which can be useful if the mode is close to a domain boundary, or in conjunction with neighbourhood_size = Inf when the domain is not the entire real line
|
||||
options_mom_.mode_check = set_default_option(options_mom_.mode_check,'number_of_points',20); % number of points around the mode where the target function is evaluated (for each parameter)
|
||||
|
||||
|
||||
% -------------------------------------------------------------------------
|
||||
|
|
@ -180,8 +320,12 @@ options_mom_.obs_nbr = length(options_mom_.varobs); % number of observed variabl
|
|||
|
||||
% related to call of dynare
|
||||
options_mom_.console_mode = options_.console_mode;
|
||||
if options_mom_.console_mode
|
||||
options_mom_.nodisplay = true;
|
||||
end
|
||||
options_mom_.parallel = options_.parallel;
|
||||
options_mom_.parallel_info = options_.parallel_info;
|
||||
options_mom_.debug = options_.debug; % debug option is needed by some functions, e.g. check_plot
|
||||
|
||||
% related to estimated_params and estimated_params_init blocks
|
||||
options_mom_.use_calibration_initialization = options_.use_calibration_initialization;
|
||||
|
|
@ -250,19 +394,82 @@ end
|
|||
options_mom_.gstep = options_.gstep; % needed by hessian.m
|
||||
options_mom_.trust_region_initial_step_bound_factor = options_.trust_region_initial_step_bound_factor; % used in dynare_solve for trust_region
|
||||
|
||||
% other
|
||||
% miscellaneous
|
||||
options_mom_.threads = options_.threads;
|
||||
options_mom_.MaxNumberOfBytes = options_.MaxNumberOfBytes;
|
||||
%options_mom_.MaximumNumberOfMegaBytes = options_.MaximumNumberOfMegaBytes;
|
||||
options_mom_.marginal_data_density = options_.marginal_data_density;
|
||||
|
||||
|
||||
% -------------------------------------------------------------------------
|
||||
% DEFAULT VALUES
|
||||
% -------------------------------------------------------------------------
|
||||
|
||||
options_mom_.analytic_derivation = 0;
|
||||
options_mom_.mom.compute_derivs = false; % flag to compute derivs in objective function (might change for GMM with either analytic_standard_errors or analytic_jacobian (dependent on optimizer))
|
||||
options_mom_.mom.vector_output = false; % specifies whether the objective function returns a vector
|
||||
options_mom_.analytic_derivation_mode = 0; % needed by get_perturbation_params_derivs.m, ie use efficient sylvester equation method to compute analytical derivatives as in Ratto & Iskrev (2012)
|
||||
options_mom_.initialize_estimated_parameters_with_the_prior_mode = 0; % needed by set_prior.m
|
||||
options_mom_.figures = options_.figures; % needed by plot_priors.m
|
||||
options_mom_.ramsey_policy = false; % needed by evaluate_steady_state
|
||||
options_mom_.risky_steadystate = false; % needed by resol
|
||||
options_mom_.jacobian_flag = true; % needed by dynare_solve
|
||||
options_mom_.figures = options_.figures; % needed by plot_priors.m
|
||||
options_mom_.ramsey_policy = false; % needed by evaluate_steady_state
|
||||
options_mom_.risky_steadystate = false; % needed by resol
|
||||
options_mom_.jacobian_flag = true; % needed by dynare_solve
|
||||
options_mom_.use_mh_covariance_matrix = false; % needed by posterior_sampler, get's overwritten by same option in options_mom_.posterior_sampler_options
|
||||
|
||||
|
||||
% -------------------------------------------------------------------------
|
||||
% CHECKS ON SETTINGS
|
||||
% -------------------------------------------------------------------------
|
||||
if strcmp(mom_method,'GMM') || strcmp(mom_method,'SMM')
|
||||
if numel(options_mom_.nobs) > 1
|
||||
error('method_of_moments: Recursive estimation is not supported. Please set an integer as ''nobs''!');
|
||||
end
|
||||
if numel(options_mom_.first_obs) > 1
|
||||
error('method_of_moments: Recursive estimation is not supported. Please set an integer as ''first_obs''!');
|
||||
end
|
||||
end
|
||||
if options_mom_.order < 1
|
||||
error('method_of_moments: The order of the Taylor approximation cannot be 0!')
|
||||
end
|
||||
if options_mom_.order > 2
|
||||
fprintf('Dynare will use ''k_order_solver'' as the order>2\n');
|
||||
options_mom_.k_order_solver = true;
|
||||
end
|
||||
if strcmp(mom_method,'SMM')
|
||||
if options_mom_.mom.simulation_multiple < 1
|
||||
fprintf('The simulation horizon is shorter than the data. Dynare resets the simulation_multiple to 7.\n')
|
||||
options_mom_.mom.simulation_multiple = 7;
|
||||
end
|
||||
end
|
||||
if strcmp(mom_method,'GMM')
|
||||
% require pruning with GMM at higher order
|
||||
if options_mom_.order > 1 && ~options_mom_.pruning
|
||||
fprintf('GMM at higher order only works with pruning, so we set pruning option to 1.\n');
|
||||
options_mom_.pruning = true;
|
||||
end
|
||||
if options_mom_.order > 3
|
||||
error('method_of_moments: Perturbation orders higher than 3 are not implemented for GMM estimation, try using SMM!');
|
||||
end
|
||||
end
|
||||
if strcmp(mom_method,'IRF_MATCHING') && doBayesianEstimation
|
||||
if isfield(options_mom_,'mh_tune_jscale') && options_mom_.mh_tune_jscale.status && (options_mom_.mh_tune_jscale.maxiter<options_mom_.mh_tune_jscale.stepsize)
|
||||
warning('method_of_moments: You specified mh_tune_jscale, but the maximum number of iterations is smaller than the step size. No update will take place.')
|
||||
end
|
||||
if options_mom_.load_results_after_load_mh
|
||||
if ~exist([options_mom_.dirname filesep 'method_of_moments' filesep M_.fname '_mom_results.mat'],'file')
|
||||
fprintf('\nYou specified the ''load_results_after_load_mh'' option, but no ''%s_mom_results.mat'' file\n',M_.fname);
|
||||
fprintf('was found in the folder %s%smethod_of_moments.\n',options_mom_.dirname,filesep);
|
||||
fprintf('Results will be recomputed and option ''load_results_after_load_mh'' is reset to false.\n');
|
||||
options_mom_.load_results_after_load_mh = false;
|
||||
end
|
||||
end
|
||||
if options_mom_.mh_replic>0 && options_mom_.mh_nblck<1
|
||||
error('method_of_moments: Bayesian MCMC estimation cannot be conducted with ''mh_nblocks''=0!')
|
||||
end
|
||||
end
|
||||
if options_mom_.mom.analytic_jacobian && ~strcmp(mom_method,'GMM')
|
||||
options_mom_.mom.analytic_jacobian = false;
|
||||
fprintf('\n''analytic_jacobian'' option will be dismissed as it only works with GMM.\n');
|
||||
end
|
||||
if strcmp(options_mom_.mom.mom_method,'IRF_MATCHING')
|
||||
if any(cellfun(@(x) isnumeric(x) && any(x == 13), options_mom_.optimizer_vec))
|
||||
error('method_of_moments: lsqnonlin (mode_compute=13) is not yet supported for IRF Matching!');
|
||||
end
|
||||
end
|
||||
|
|
@ -183,55 +183,22 @@ if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_meth
|
|||
end
|
||||
|
||||
|
||||
% -------------------------------------------------------------------------
|
||||
% checks on settings
|
||||
% -------------------------------------------------------------------------
|
||||
if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_method,'SMM')
|
||||
if numel(options_mom_.nobs) > 1
|
||||
error('method_of_moments: Recursive estimation and forecast for samples is not supported. Please set an integer as ''nobs''!');
|
||||
end
|
||||
if numel(options_mom_.first_obs) > 1
|
||||
error('method_of_moments: Recursive estimation and forecast for samples is not supported. Please set an integer as ''first_obs''!');
|
||||
end
|
||||
end
|
||||
if options_mom_.order < 1
|
||||
error('method_of_moments: The order of the Taylor approximation cannot be 0!')
|
||||
end
|
||||
if options_mom_.order > 2
|
||||
fprintf('Dynare will use ''k_order_solver'' as the order>2\n');
|
||||
options_mom_.k_order_solver = true;
|
||||
end
|
||||
if strcmp(options_mom_.mom.mom_method,'SMM')
|
||||
if options_mom_.mom.simulation_multiple < 1
|
||||
fprintf('The simulation horizon is shorter than the data. Dynare resets the simulation_multiple to 7.\n')
|
||||
options_mom_.mom.simulation_multiple = 7;
|
||||
end
|
||||
end
|
||||
if strcmp(options_mom_.mom.mom_method,'GMM')
|
||||
% require pruning with GMM at higher order
|
||||
if options_mom_.order > 1 && ~options_mom_.pruning
|
||||
fprintf('GMM at higher order only works with pruning, so we set pruning option to 1.\n');
|
||||
options_mom_.pruning = true;
|
||||
end
|
||||
if options_mom_.order > 3
|
||||
error('method_of_moments: Perturbation orders higher than 3 are not implemented for GMM estimation, try using SMM!');
|
||||
end
|
||||
end
|
||||
if options_mom_.mom.analytic_jacobian && ~strcmp(options_mom_.mom.mom_method,'GMM')
|
||||
options_mom_.mom.analytic_jacobian = false;
|
||||
fprintf('\n''analytic_jacobian'' option will be dismissed as it only works with GMM.\n');
|
||||
end
|
||||
|
||||
|
||||
% -------------------------------------------------------------------------
|
||||
% initializations
|
||||
% -------------------------------------------------------------------------
|
||||
% create output directories to store results
|
||||
M_.dname = options_mom_.dirname;
|
||||
CheckPath(M_.dname,'.');
|
||||
CheckPath('method_of_moments',M_.dname);
|
||||
CheckPath('graphs',options_mom_.dirname);
|
||||
% initialize options that might change
|
||||
options_mom_.mom.compute_derivs = false; % flag to compute derivs in objective function (might change for GMM with either analytic_standard_errors or analytic_jacobian (dependent on optimizer))
|
||||
options_mom_.mom.vector_output = false; % specifies whether the objective function returns a vector
|
||||
CheckPath('graphs',M_.dname);
|
||||
|
||||
if doBayesianEstimation
|
||||
oo_.mom.posterior.optimization.mode = [];
|
||||
oo_.mom.posterior.optimization.Variance = [];
|
||||
oo_.mom.posterior.optimization.log_density=[];
|
||||
end
|
||||
doBayesianEstimationMCMC = doBayesianEstimation && ( (options_mom_.mh_replic>0) || options_mom_.load_mh_file );
|
||||
invhess = [];
|
||||
% decision rule
|
||||
oo_.dr = set_state_space(oo_.dr,M_); % get state-space representation
|
||||
options_mom_.mom.obs_var = []; % create index of observed variables in DR order
|
||||
|
|
|
|||
Loading…
Reference in New Issue