diff --git a/matlab/+mom/default_option_mom_values.m b/matlab/+mom/default_option_mom_values.m
index 3c105e27d743a26cd996b27cbc04db6181238ac0..f465f76a08831e9ff7121b35eecea6adeede31e4 100644
--- a/matlab/+mom/default_option_mom_values.m
+++ b/matlab/+mom/default_option_mom_values.m
@@ -1,33 +1,33 @@
function options_mom_ = default_option_mom_values(options_mom_, options_, dname, doBayesianEstimation)
-% function options_mom_ = default_option_mom_values(options_mom_, options_, dname, doBayesianEstimation)
-
-% Returns structure containing the options for method_of_moments command
-
-% options_mom_ is local and contains default and user-specified values for
-% all settings needed for the method of moments estimation. Some options,
-% though, are set by the preprocessor into options_ and we copy these over.
-% The idea is to be independent of options_ and have full control of the
-% estimation instead of possibly having to deal with options chosen somewhere
-% else in the mod file.
-
-% =========================================================================
+% options_mom_ = default_option_mom_values(options_mom_, options_, dname, doBayesianEstimation)
+% -------------------------------------------------------------------------
+% Returns structure containing the options for method_of_moments command.
+% Note 1: options_mom_ is local and contains default and user-specified
+% values for all settings needed for the method of moments estimation.
+% Some options, though, are set by the preprocessor into options_ and we
+% copy these over. The idea is to be independent of options_ and have full
+% control of the estimation instead of possibly having to deal with options
+% chosen somewhere else in the mod file.
+% Note 2: we call a "mode" the minimum of the objective function, i.e.
+% the parameter vector that minimizes the distance between the moments/irfs
+% computed from the model and the moments/irfs computed from the data.
+% -------------------------------------------------------------------------
% INPUTS
-% o options_mom_: [structure] information about all (user-specified and updated) settings used in estimation (options_mom_)
-% o options_: [structure] information on global options
-% o dname: [string] name of directory to store results
-% o doBayesianEstimation [boolean] indicator whether we do Bayesian estimation
+% o options_mom_: [structure] all user-specified settings (from the method_of_moments command)
+% o options_: [structure] global options
+% o dname: [string] default name of directory to store results
+% o doBayesianEstimation [boolean] indicator whether we do Bayesian estimation
% -------------------------------------------------------------------------
% OUTPUTS
-% o oo_: [structure] storage for results (oo_)
-% o options_mom_: [structure] information about all (user-specified and updated) settings used in estimation (options_mom_)
+% o options_mom_: [structure] all user-specified and updated settings required for method_of_moments estimation
% -------------------------------------------------------------------------
% This function is called by
% o mom.run
% -------------------------------------------------------------------------
% This function calls
-% o set_default_option
-% o user_has_matlab_license
-% o user_has_octave_forge_package
+% o set_default_option
+% o user_has_matlab_license
+% o user_has_octave_forge_package
% -------------------------------------------------------------------------
% Copyright © 2023 Dynare Team
@@ -46,34 +46,43 @@ function options_mom_ = default_option_mom_values(options_mom_, options_, dname,
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
-% =========================================================================
mom_method = options_mom_.mom.mom_method; % this is a required option
+
% -------------------------------------------------------------------------
% LIMITATIONS
% -------------------------------------------------------------------------
if options_.logged_steady_state || options_.loglinear
- error('method_of_moments: The loglinear option is not supported. Please append the required logged variables as auxiliary equations.')
+ error('method_of_moments: The loglinear option is not supported. Please append the required logged variables as auxiliary equations.');
else
options_mom_.logged_steady_state = 0;
options_mom_.loglinear = false;
end
-options_mom_.hessian.use_penalized_objective = false; % penalized objective not yet
-% options related to variable declarations
+if isfield(options_mom_,'hessian') && options_mom_.hessian.use_penalized_objective
+ warning('method_of_moments: The ''use_penalized_objective_for_hessian'' option is not supported yet and will be skipped.');
+end
+options_mom_.hessian.use_penalized_objective = false; % penalized objective not yet supported
if isfield(options_,'trend_coeffs')
- error('method_of_moments: %s does not allow for trend in data',mom_method)
+ error('method_of_moments: %s does not allow for trend in data',mom_method);
end
-% options related to endogenous prior restrictions are not supported
if ~isempty(options_.endogenous_prior_restrictions.irf) && ~isempty(options_.endogenous_prior_restrictions.moment)
- fprintf('method_of_moments: Endogenous prior restrictions are not supported yet and will be skipped.\n')
+ warning('method_of_moments: Endogenous prior restrictions are not supported yet and will be skipped.');
end
options_mom_.endogenous_prior_restrictions.irf = {};
options_mom_.endogenous_prior_restrictions.moment = {};
-
-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
+if isfield(options_mom_,'bayesian_irf') && options_mom_.bayesian_irf % do we need this at all??
+ warning('method_of_moments: The ''bayesian_irf'' option is not supported yet and will be skipped.');
+end
+options_mom_.bayesian_irf = false;
+if strcmp(mom_method,'IRF_MATCHING')
+ if isfield(options_mom_.mom,'penalized_estimator') && options_mom_.mom.penalized_estimator
+ warning('method_of_moments: The ''penalized_estimator'' option is not supported yet for IRF_MATCHING and will be ignored.');
+ end
+ options_mom_.mom.penalized_estimator = false;
+end
% -------------------------------------------------------------------------
% OPTIONS POSSIBLY SET BY THE USER
@@ -87,7 +96,6 @@ options_mom_ = set_default_option(options_mom_,'nograph',false); % do no
options_mom_ = set_default_option(options_mom_,'noprint',false); % do not print output to console
options_mom_ = set_default_option(options_mom_,'TeX',false); % print TeX tables and graphics
options_mom_.mom = set_default_option(options_mom_.mom,'verbose',false); % display and store intermediate estimation results
-%options_mom_ = set_default_option(options_mom_,'verbosity',false); %
if doBayesianEstimation
options_mom_ = set_default_option(options_mom_,'plot_priors',true); % control plotting of priors
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
@@ -111,6 +119,16 @@ end
if strcmp(mom_method,'GMM')
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.
end
+if strcmp(mom_method,'IRF_MATCHING')
+ if ~isfield(options_mom_.mom,'irf_matching_file')
+ options_mom_.mom.irf_matching_file = []; % irf_matching file enables to transform model IRFs before matching them to data IRFs
+ end
+ options_mom_.mom.irf_matching_file = set_default_option(options_mom_.mom.irf_matching_file,'name','');
+ options_mom_.mom = set_default_option(options_mom_.mom,'simulation_method','STOCH_SIMUL'); % simulation method used to compute IRFs
+ 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
+ options_mom_ = set_default_option(options_mom_,'drop',100); % truncation / burnin for order>1 irf simulations
+ options_mom_ = set_default_option(options_mom_,'relative_irf',false); % requests the computation of normalized IRFs
+end
% data related options
if strcmp(mom_method,'GMM') || strcmp(mom_method,'SMM')
@@ -123,12 +141,14 @@ if strcmp(mom_method,'GMM') || strcmp(mom_method,'SMM')
end
% optimization related
-if (isoctave && user_has_octave_forge_package('optim')) || (~isoctave && user_has_matlab_license('optimization_toolbox'))
- if strcmp(mom_method,'GMM') || strcmp(mom_method,'SMM')
+if strcmp(mom_method,'GMM') || strcmp(mom_method,'SMM')
+ if (isoctave && user_has_octave_forge_package('optim')) || (~isoctave && user_has_matlab_license('optimization_toolbox'))
options_mom_ = set_default_option(options_mom_,'mode_compute',13); % specifies lsqnonlin as default optimizer for minimization
+ else
+ options_mom_ = set_default_option(options_mom_,'mode_compute',5); % specifies newrat as fallback default option for minimization
end
-else
- options_mom_ = set_default_option(options_mom_,'mode_compute',4); % specifies csminwel as fallback default option for minimization
+elseif strcmp(mom_method,'IRF_MATCHING')
+ options_mom_ = set_default_option(options_mom_,'mode_compute',5); % specifies newrat as fallback default option for minimization
end
options_mom_ = set_default_option(options_mom_,'additional_optimizer_steps',[]); % vector of additional mode-finders run after mode_compute
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
@@ -136,9 +156,20 @@ options_mom_ = set_default_option(options_mom_,'silent_optimizer',false);
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
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
options_mom_.optimizer_vec = [options_mom_.mode_compute;num2cell(options_mom_.additional_optimizer_steps)];
+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
+options_mom_.analytic_derivation = 0; % force to 0 as we check this seperately in dynare_minimize_objective.m
+options_mom_ = set_default_option(options_mom_,'mode_file',''); % name of the file containing initial values for the mode
+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
% perturbation related
options_mom_ = set_default_option(options_mom_,'order',1); % order of Taylor approximation in perturbation
+if strcmp(mom_method,'IRF_MATCHING') % number of simulated series used to compute IRFs
+ if options_mom_.order == 1
+ options_mom_ = set_default_option(options_mom_,'replic',1);
+ else
+ options_mom_ = set_default_option(options_mom_,'replic',50);
+ end
+end
options_mom_ = set_default_option(options_mom_,'pruning',false); % use pruned state space system at order>1
options_mom_ = set_default_option(options_mom_,'aim_solver',false); % use AIM algorithm to compute perturbation approximation instead of mjdgges
options_mom_ = set_default_option(options_mom_,'k_order_solver',false); % use k_order_perturbation instead of mjdgges
@@ -161,12 +192,121 @@ options_mom_ = set_default_option(options_mom_,'lyapunov_complex_threshold',1e-1
options_mom_ = set_default_option(options_mom_,'lyapunov_fixed_point_tol',1e-10); % convergence criterion used in the fixed point Lyapunov solver
options_mom_ = set_default_option(options_mom_,'lyapunov_doubling_tol',1e-16); % convergence criterion used in the doubling algorithm
-% mode check plot
+% Bayesian MCMC related
+if doBayesianEstimation
+ options_mom_ = set_default_option(options_mom_,'mh_replic',0); % number of draws in Metropolis-Hastings and slice samplers
+ 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
+ options_mom_ = set_default_option(options_mom_,'load_mh_file',false); % add to previous Metropolis-Hastings or slice simulations instead of starting from scratch
+ 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
+
+ if options_mom_.mh_replic > 0 || options_mom_.load_mh_file
+ options_mom_ = set_default_option(options_mom_,'sub_draws',[]);
+ options_mom_ = set_default_option(options_mom_,'posterior_max_subsample_draws',1200);
+ options_mom_ = set_default_option(options_mom_,'mh_nblck',2); % number of parallel chains for Metropolis-Hastings or slice algorithm
+ 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
+ options_mom_ = set_default_option(options_mom_,'mh_conf_sig',0.9); % confidence/HPD interval used for the computation of prior and posterior statistics
+ options_mom_ = set_default_option(options_mom_,'mh_recover',false); % attempts to recover a Metropolis-Hastings simulation that crashed prematurely
+ options_mom_ = set_default_option(options_mom_,'MCMC_jumping_covariance','hessian'); % which covariance to use for the proposal density of the MCMC sampler
+ if ~isfield(options_mom_,'mh_initialize_from_previous_mcmc')
+ options_mom_.mh_initialize_from_previous_mcmc.status = false; % pick initial values for new MCMC from a previous one
+ end
+ 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
+ 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
+ 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
+ if ~isfield(options_mom_,'posterior_sampler_options')
+ options_mom_.posterior_sampler_options = [];
+ end
+ 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
+ options_mom_.posterior_sampler_options = set_default_option(options_mom_.posterior_sampler_options,'sampling_opt',[]); % used to set options for the posterior sampling methods
+ switch options_mom_.posterior_sampler_options.posterior_sampling_method
+ case 'random_walk_metropolis_hastings'
+ if ~isfield(options_mom_.posterior_sampler_options,'rwmh')
+ options_mom_.posterior_sampler_options.rwmh = [];
+ end
+ options_mom_.posterior_sampler_options.rwmh = set_default_option(options_mom_.posterior_sampler_options.rwmh,'proposal_distribution','rand_multivariate_normal');
+ options_mom_.posterior_sampler_options.rwmh = set_default_option(options_mom_.posterior_sampler_options.rwmh,'student_degrees_of_freedom',3);
+ options_mom_.posterior_sampler_options.rwmh = set_default_option(options_mom_.posterior_sampler_options.rwmh,'use_mh_covariance_matrix',false);
+ options_mom_.posterior_sampler_options.rwmh = set_default_option(options_mom_.posterior_sampler_options.rwmh,'save_tmp_file',false);
+ case 'tailored_random_block_metropolis_hastings'
+ if ~isfield(options_mom_.posterior_sampler_options,'tarb')
+ options_mom_.posterior_sampler_options.tarb = [];
+ end
+ options_mom_.posterior_sampler_options.tarb = set_default_option(options_mom_.posterior_sampler_options.tarb,'proposal_distribution','rand_multivariate_normal');
+ options_mom_.posterior_sampler_options.tarb = set_default_option(options_mom_.posterior_sampler_options.tarb,'student_degrees_of_freedom',3);
+ options_mom_.posterior_sampler_options.tarb = set_default_option(options_mom_.posterior_sampler_options.tarb,'mode_compute',4);
+ options_mom_.posterior_sampler_options.tarb = set_default_option(options_mom_.posterior_sampler_options.tarb,'new_block_probability',0.25);
+ options_mom_.posterior_sampler_options.tarb = set_default_option(options_mom_.posterior_sampler_options.tarb,'optim_opt','');
+ options_mom_.posterior_sampler_options.tarb = set_default_option(options_mom_.posterior_sampler_options.tarb,'save_tmp_file',true);
+ case 'slice'
+ if ~isfield(options_mom_.posterior_sampler_options,'slice')
+ options_mom_.posterior_sampler_options.slice = [];
+ end
+ options_mom_.posterior_sampler_options.slice = set_default_option(options_mom_.posterior_sampler_options.slice,'proposal_distribution','');
+ options_mom_.posterior_sampler_options.slice = set_default_option(options_mom_.posterior_sampler_options.slice,'rotated',0);
+ 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
+ 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
+ options_mom_.posterior_sampler_options.slice = set_default_option(options_mom_.posterior_sampler_options.slice,'WR',[]);
+ options_mom_.posterior_sampler_options.slice = set_default_option(options_mom_.posterior_sampler_options.slice,'mode_files',[]);
+ options_mom_.posterior_sampler_options.slice = set_default_option(options_mom_.posterior_sampler_options.slice,'mode',[]);
+ options_mom_.posterior_sampler_options.slice = set_default_option(options_mom_.posterior_sampler_options.slice,'initial_step_size',0.8);
+ options_mom_.posterior_sampler_options.slice = set_default_option(options_mom_.posterior_sampler_options.slice,'save_tmp_file',true);
+ case 'independent_metropolis_hastings'
+ if ~isfield(options_mom_.posterior_sampler_options,'imh')
+ options_mom_.posterior_sampler_options.imh = [];
+ end
+ options_mom_.posterior_sampler_options.imh = set_default_option(options_mom_.posterior_sampler_options.imh,'proposal_distribution','rand_multivariate_normal');
+ options_mom_.posterior_sampler_options.imh = set_default_option(options_mom_.posterior_sampler_options.imh,'use_mh_covariance_matrix',false);
+ options_mom_.posterior_sampler_options.imh = set_default_option(options_mom_.posterior_sampler_options.imh,'save_tmp_file',false);
+ end
+ if ~strcmp(options_mom_.posterior_sampler_options.posterior_sampling_method,'slice')
+ options_mom_ = set_default_option(options_mom_,'mh_init_scale_factor',2);
+ options_mom_ = set_default_option(options_mom_,'mh_jscale',[]);
+ end
+
+ % mh_tune_jscale options
+ if strcmp(options_mom_.posterior_sampler_options.posterior_sampling_method,'random_walk_metropolis_hastings')
+ if ~isfield(options_mom_,'mh_tune_jscale')
+ options_mom_.mh_tune_jscale = [];
+ end
+ options_mom_.mh_tune_jscale = set_default_option(options_mom_.mh_tune_jscale,'status',false);
+ options_mom_.mh_tune_jscale = set_default_option(options_mom_.mh_tune_jscale,'target',0.33);
+ options_mom_.mh_tune_jscale = set_default_option(options_mom_.mh_tune_jscale,'guess',[]);
+ options_mom_.mh_tune_jscale.maxiter = options_.mh_tune_jscale.maxiter;
+ options_mom_.mh_tune_jscale.rho = options_.mh_tune_jscale.rho;
+ options_mom_.mh_tune_jscale.stepsize = options_.mh_tune_jscale.stepsize;
+ options_mom_.mh_tune_jscale.c1 = options_.mh_tune_jscale.c1;
+ options_mom_.mh_tune_jscale.c2 = options_.mh_tune_jscale.c2;
+ options_mom_.mh_tune_jscale.c3 = options_.mh_tune_jscale.c3;
+ end
+
+ % convergence diagnostics
+ options_mom_ = set_default_option(options_mom_,'nodiagnostic',false);
+ if ~isfield(options_mom_,'convergence')
+ options_mom_.convergence = [];
+ end
+ if ~isfield(options_mom_.convergence,'geweke')
+ options_mom_.convergence.geweke = [];
+ end
+ if ~isfield(options_mom_.convergence,'rafterylewis')
+ options_mom_.convergence.rafterylewis = [];
+ end
+ if ~isfield(options_mom_.convergence,'brooksgelman')
+ options_mom_.convergence.brooksgelman = [];
+ 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
\ No newline at end of file
+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
\ No newline at end of file
diff --git a/matlab/+mom/run.m b/matlab/+mom/run.m
index 182011c0b4af9c7dc787f5a69f285f5c4fbce6a6..edcc52d1b5eac606a56b394721be281a414de16f 100644
--- a/matlab/+mom/run.m
+++ b/matlab/+mom/run.m
@@ -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