method_of_moments: refactor and function for default options & initializations
Also changed: - options_mom_.vector_output to options_mom_.mom.vector_output - optimizer_vec to options_mom_.optimizer_vec - oo_.dr.obs_var to oo_.mom.obs_varkalman-mex
Willi Mutschler
parent
f25188cf2f
commit
0487dd4a77
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@ -49,7 +49,7 @@ for jm = 1:options_mom_.mom.mom_nbr
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leadlags = matched_moments_{jm,2}; % lags are negative numbers and leads are positive numbers
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powers = matched_moments_{jm,3};
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for jv = 1:length(vars)
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jvar = (oo_.dr.obs_var == vars(jv));
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jvar = (oo_.mom.obs_var == vars(jv));
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y = NaN(T,1); %Take care of T_eff instead of T for lags and NaN via mean with 'omitnan' option below
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y( (1-min(leadlags(jv),0)) : (T-max(leadlags(jv),0)), 1) = data( (1+max(leadlags(jv),0)) : (T+min(leadlags(jv),0)), jvar).^powers(jv);
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if jv==1
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@ -0,0 +1,269 @@
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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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% INPUTS
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% o M_: [structure] information about model with
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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] information about all (user-specified and updated) settings used in estimation (options_mom_)
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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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% -------------------------------------------------------------------------
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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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% -------------------------------------------------------------------------
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% Copyright © 2023 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 <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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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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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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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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% -------------------------------------------------------------------------
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% OPTIONS POSSIBLY SET BY THE USER
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% -------------------------------------------------------------------------
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% common settings
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options_mom_ = set_default_option(options_mom_,'dirname',dname); % specify directory in which to store estimation output
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options_mom_ = set_default_option(options_mom_,'graph_format','eps'); % specify the file format(s) for graphs saved to disk
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options_mom_ = set_default_option(options_mom_,'nodisplay',false); % do not display the graphs, but still save them to disk
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options_mom_ = set_default_option(options_mom_,'nograph',false); % do not create graphs (which implies that they are not saved to the disk nor displayed)
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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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end
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% specific method_of_moments settings
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if strcmp(mom_method,'GMM') || strcmp(mom_method,'SMM')
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options_mom_.mom = set_default_option(options_mom_.mom,'bartlett_kernel_lag',20); % bandwith in optimal weighting matrix
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options_mom_.mom = set_default_option(options_mom_.mom,'penalized_estimator',false); % include deviation from prior mean as additional moment restriction and use prior precision as weights
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options_mom_.mom = set_default_option(options_mom_.mom,'se_tolx',1e-5); % step size for numerical computation of standard errors
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options_mom_.mom = set_default_option(options_mom_.mom,'weighting_matrix_scaling_factor',1); % scaling of weighting matrix in objective function
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options_mom_.mom = set_default_option(options_mom_.mom,'weighting_matrix',{'DIAGONAL'; 'OPTIMAL'}); % weighting matrix in moments distance objective function at each iteration of estimation;
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% possible values are 'OPTIMAL', 'IDENTITY_MATRIX' ,'DIAGONAL' or a filename. Size of cell determines stages in iterated estimation.
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end
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if strcmp(mom_method,'SMM')
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options_mom_.mom = set_default_option(options_mom_.mom,'burnin',500); % number of periods dropped at beginning of simulation
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options_mom_.mom = set_default_option(options_mom_.mom,'bounded_shock_support',false); % trim shocks in simulation to +- 2 stdev
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options_mom_.mom = set_default_option(options_mom_.mom,'seed',24051986); % seed used in simulations
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options_mom_.mom = set_default_option(options_mom_.mom,'simulation_multiple',7); % multiple of the data length used for simulation
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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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% data related options
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if strcmp(mom_method,'GMM') || strcmp(mom_method,'SMM')
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options_mom_ = set_default_option(options_mom_,'first_obs',1); % number of first observation
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options_mom_ = set_default_option(options_mom_,'logdata',false); % if data is already in logs
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options_mom_ = set_default_option(options_mom_,'nobs',NaN); % number of observations
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options_mom_ = set_default_option(options_mom_,'prefilter',false); % demean each data series by its empirical mean and use centered moments
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options_mom_ = set_default_option(options_mom_,'xls_sheet',1); % name of sheet with data in Excel, Octave does not support the empty string, rather use first sheet
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options_mom_ = set_default_option(options_mom_,'xls_range',''); % range of data in Excel sheet
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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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options_mom_ = set_default_option(options_mom_,'mode_compute',13); % specifies lsqnonlin as default optimizer 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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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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options_mom_ = set_default_option(options_mom_,'silent_optimizer',false); % run minimization of moments distance silently without displaying results or saving files in between
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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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% 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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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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options_mom_ = set_default_option(options_mom_,'dr_cycle_reduction',false); % use cycle reduction algorithm to solve the polynomial equation for retrieving the coefficients associated to the endogenous variables in the decision rule
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options_mom_ = set_default_option(options_mom_,'dr_cycle_reduction_tol',1e-7); % convergence criterion used in the cycle reduction algorithm
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options_mom_ = set_default_option(options_mom_,'dr_logarithmic_reduction',false); % use logarithmic reduction algorithm to solve the polynomial equation for retrieving the coefficients associated to the endogenous variables in the decision rule
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options_mom_ = set_default_option(options_mom_,'dr_logarithmic_reduction_maxiter',100); % maximum number of iterations used in the logarithmic reduction algorithm
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options_mom_ = set_default_option(options_mom_,'dr_logarithmic_reduction_tol',1e-12); % convergence criterion used in the cycle reduction algorithm
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options_mom_ = set_default_option(options_mom_,'qz_criterium',1-1e-6); % value used to split stable from unstable eigenvalues in reordering the Generalized Schur decomposition used for solving first order problems
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% if there are no unit roots one can use 1.0 (or slightly below) which we set as default; if they are possible, you may have have multiple unit roots and the accuracy decreases when computing the eigenvalues in lyapunov_symm
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% Note that unit roots are only possible at first-order, at higher order we set it to 1 in pruned_state_space_system and focus only on stationary observables.
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options_mom_ = set_default_option(options_mom_,'qz_zero_threshold',1e-6); % value used to test if a generalized eigenvalue is 0/0 in the generalized Schur decomposition
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options_mom_ = set_default_option(options_mom_,'schur_vec_tol',1e-11); % tolerance level used to find nonstationary variables in Schur decomposition of the transition matrix.
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% numerical algorithms
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options_mom_ = set_default_option(options_mom_,'lyapunov_db',false); % doubling algorithm (disclyap_fast) to solve Lyapunov equation to compute variance-covariance matrix of state variables
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options_mom_ = set_default_option(options_mom_,'lyapunov_fp',false); % fixed-point algorithm to solve Lyapunov equation to compute variance-covariance matrix of state variables
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options_mom_ = set_default_option(options_mom_,'lyapunov_srs',false); % square-root-solver (dlyapchol) algorithm to solve Lyapunov equation to compute variance-covariance matrix of state variables
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options_mom_ = set_default_option(options_mom_,'lyapunov_complex_threshold',1e-15); % complex block threshold for the upper triangular matrix in symmetric Lyapunov equation solver
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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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options_mom_ = set_default_option(options_mom_,'sylvester_fp',false); % determines whether to use fixed point algorihtm to solve Sylvester equation (gensylv_fp), faster for large scale models
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options_mom_ = set_default_option(options_mom_,'sylvester_fixed_point_tol',1e-12); % convergence criterion used in the fixed point Sylvester solver
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% mode check plot
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options_mom_.mode_check.nolik = false; % we don't do likelihood (also this initializes mode_check substructure)
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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.
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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
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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
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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)
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% -------------------------------------------------------------------------
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% OPTIONS THAT NEED TO BE CARRIED OVER (E.G. SET BY THE PREPROCESSOR)
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% -------------------------------------------------------------------------
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% related to VAROBS block
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options_mom_.varobs = options_.varobs; % observable variables in order they are declared in varobs
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options_mom_.varobs_id = options_.varobs_id; % index for observable variables in M_.endo_names
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options_mom_.obs_nbr = length(options_mom_.varobs); % number of observed variables
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% related to call of dynare
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options_mom_.console_mode = options_.console_mode;
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options_mom_.parallel = options_.parallel;
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options_mom_.parallel_info = options_.parallel_info;
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% related to estimated_params and estimated_params_init blocks
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options_mom_.use_calibration_initialization = options_.use_calibration_initialization;
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% related to model block
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options_mom_.linear = options_.linear;
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options_mom_.use_dll = options_.use_dll;
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options_mom_.block = options_.block;
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options_mom_.bytecode = options_.bytecode;
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% related to steady-state computations
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options_mom_.homotopy_force_continue = options_.homotopy_force_continue;
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options_mom_.homotopy_mode = options_.homotopy_mode;
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options_mom_.homotopy_steps = options_.homotopy_steps;
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options_mom_.markowitz = options_.markowitz;
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options_mom_.solve_algo = options_.solve_algo;
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options_mom_.solve_tolf = options_.solve_tolf;
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options_mom_.solve_tolx = options_.solve_tolx;
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options_mom_.steady = options_.steady;
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options_mom_.steadystate = options_.steadystate;
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options_mom_.steadystate_flag = options_.steadystate_flag;
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%options_mom_.steadystate_partial
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options_mom_.threads = options_.threads; % needed by resol
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options_mom_.debug = options_.debug; % debug option needed by some functions, e.g. check_plot
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% random numbers
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options_mom_.DynareRandomStreams.seed = options_.DynareRandomStreams.seed;
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options_mom_.DynareRandomStreams.algo = options_.DynareRandomStreams.algo;
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% dataset_ related
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options_mom_.dataset = options_.dataset;
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options_mom_.initial_period = options_.initial_period;
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% optimization related
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if any(cellfun(@(x) isnumeric(x) && any(x == 2), options_mom_.optimizer_vec)) % simulated annealing (mode_compute=2)
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options_mom_.saopt = options_.saopt;
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end
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if any(cellfun(@(x) isnumeric(x) && any(x == 4), options_mom_.optimizer_vec)) % csminwel (mode_compute=4)
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options_mom_.csminwel = options_.csminwel;
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end
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if any(cellfun(@(x) isnumeric(x) && any(x == 5), options_mom_.optimizer_vec)) % newrat (mode_compute=5)
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options_mom_.newrat = options_.newrat;
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end
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if any(cellfun(@(x) isnumeric(x) && any(x == 6), options_mom_.optimizer_vec)) % gmhmaxlik (mode_compute=6)
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options_mom_.gmhmaxlik = options_.gmhmaxlik;
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options_mom_.mh_jscale = options_.mh_jscale;
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end
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if any(cellfun(@(x) isnumeric(x) && any(x == 8), options_mom_.optimizer_vec)) % simplex variation on Nelder Mead algorithm (mode_compute=8)
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options_mom_.simplex = options_.simplex;
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end
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if any(cellfun(@(x) isnumeric(x) && any(x == 9), options_mom_.optimizer_vec)) % cmaes (mode_compute=9)
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options_mom_.cmaes = options_.cmaes;
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end
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if any(cellfun(@(x) isnumeric(x) && any(x == 10), options_mom_.optimizer_vec)) % simpsa (mode_compute=10)
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options_mom_.simpsa = options_.simpsa;
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end
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if any(cellfun(@(x) isnumeric(x) && any(x == 12), options_mom_.optimizer_vec)) % particleswarm (mode_compute=12)
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options_mom_.particleswarm = options_.particleswarm;
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end
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if any(cellfun(@(x) isnumeric(x) && any(x == 101), options_mom_.optimizer_vec)) % solveopt (mode_compute=101)
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options_mom_.solveopt = options_.solveopt;
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end
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if any(cellfun(@(x) isnumeric(x) && (any(x == 4) || any(x == 5)), options_mom_.optimizer_vec)) % used by csminwel and newrat
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options_mom_.gradient_method = options_.gradient_method;
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options_mom_.gradient_epsilon = options_.gradient_epsilon;
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end
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options_mom_.gstep = options_.gstep; % needed by hessian.m
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options_mom_.trust_region_initial_step_bound_factor = options_.trust_region_initial_step_bound_factor; % used in dynare_solve for trust_region
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% other
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options_mom_.MaxNumberOfBytes = options_.MaxNumberOfBytes;
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%options_mom_.MaximumNumberOfMegaBytes = options_.MaximumNumberOfMegaBytes;
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% -------------------------------------------------------------------------
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% DEFAULT VALUES
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% -------------------------------------------------------------------------
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options_mom_.analytic_derivation = 0;
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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)
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options_mom_.initialize_estimated_parameters_with_the_prior_mode = 0; % needed by set_prior.m
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options_mom_.figures = options_.figures; % needed by plot_priors.m
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options_mom_.ramsey_policy = false; % needed by evaluate_steady_state
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options_mom_.risky_steadystate = false; % needed by resol
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options_mom_.jacobian_flag = true; % needed by dynare_solve
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@ -62,7 +62,7 @@ function [fval, info, exit_flag, df, junk1, oo_, M_, options_mom_] = objective_f
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% 0. Initialization of the returned variables and others...
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%------------------------------------------------------------------------------
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if options_mom_.mom.compute_derivs && options_mom_.mom.analytic_jacobian
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if options_mom_.vector_output == 1
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if options_mom_.mom.vector_output == 1
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if options_mom_.mom.penalized_estimator
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df = nan(size(oo_.mom.data_moments,1)+length(xparam1),length(xparam1));
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else
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@ -88,7 +88,7 @@ M_ = set_all_parameters(xparam1, estim_params_, M_);
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[fval,info,exit_flag]=check_bounds_and_definiteness_estimation(xparam1, M_, estim_params_, Bounds);
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if info(1)
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if options_mom_.vector_output == 1 % lsqnonlin requires vector output
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if options_mom_.mom.vector_output == 1 % lsqnonlin requires vector output
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fval = ones(size(oo_.mom.data_moments,1),1)*options_mom_.huge_number;
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end
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return
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|
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@ -110,7 +110,7 @@ if info(1)
|
|||
fval = Inf;
|
||||
info(4) = info(2);
|
||||
exit_flag = 0;
|
||||
if options_mom_.vector_output == 1 % lsqnonlin requires vector output
|
||||
if options_mom_.mom.vector_output == 1 % lsqnonlin requires vector output
|
||||
fval = ones(size(oo_.mom.data_moments,1),1)*options_mom_.huge_number;
|
||||
end
|
||||
return
|
||||
|
|
@ -118,7 +118,7 @@ if info(1)
|
|||
fval = Inf;
|
||||
info(4) = 0.1;
|
||||
exit_flag = 0;
|
||||
if options_mom_.vector_output == 1 % lsqnonlin requires vector output
|
||||
if options_mom_.mom.vector_output == 1 % lsqnonlin requires vector output
|
||||
fval = ones(size(oo_.mom.data_moments,1),1)*options_mom_.huge_number;
|
||||
end
|
||||
return
|
||||
|
|
@ -151,16 +151,16 @@ if strcmp(options_mom_.mom.mom_method,'GMM')
|
|||
totparam_nbr = stderrparam_nbr+corrparam_nbr+modparam_nbr;
|
||||
dr.derivs = get_perturbation_params_derivs(M_, options_mom_, estim_params_, oo_, indpmodel, indpstderr, indpcorr, 0); %analytic derivatives of perturbation matrices
|
||||
oo_.mom.model_moments_params_derivs = NaN(options_mom_.mom.mom_nbr,totparam_nbr);
|
||||
pruned_state_space = pruned_state_space_system(M_, options_mom_, dr, oo_.dr.obs_var, options_mom_.ar, 0, 1);
|
||||
pruned_state_space = pruned_state_space_system(M_, options_mom_, dr, oo_.mom.obs_var, options_mom_.ar, 0, 1);
|
||||
else
|
||||
pruned_state_space = pruned_state_space_system(M_, options_mom_, dr, oo_.dr.obs_var, options_mom_.ar, 0, 0);
|
||||
pruned_state_space = pruned_state_space_system(M_, options_mom_, dr, oo_.mom.obs_var, options_mom_.ar, 0, 0);
|
||||
end
|
||||
|
||||
oo_.mom.model_moments = NaN(options_mom_.mom.mom_nbr,1);
|
||||
for jm = 1:size(M_.matched_moments,1)
|
||||
% First moments
|
||||
if ~options_mom_.prefilter && (sum(M_.matched_moments{jm,3}) == 1)
|
||||
idx1 = (oo_.dr.obs_var == find(oo_.dr.order_var==M_.matched_moments{jm,1}) );
|
||||
idx1 = (oo_.mom.obs_var == find(oo_.dr.order_var==M_.matched_moments{jm,1}) );
|
||||
oo_.mom.model_moments(jm,1) = pruned_state_space.E_y(idx1);
|
||||
if options_mom_.mom.compute_derivs && ( options_mom_.mom.analytic_standard_errors || options_mom_.mom.analytic_jacobian )
|
||||
oo_.mom.model_moments_params_derivs(jm,:) = pruned_state_space.dE_y(idx1,:);
|
||||
|
|
@ -168,8 +168,8 @@ if strcmp(options_mom_.mom.mom_method,'GMM')
|
|||
end
|
||||
% Second moments
|
||||
if (sum(M_.matched_moments{jm,3}) == 2)
|
||||
idx1 = (oo_.dr.obs_var == find(oo_.dr.order_var==M_.matched_moments{jm,1}(1)) );
|
||||
idx2 = (oo_.dr.obs_var == find(oo_.dr.order_var==M_.matched_moments{jm,1}(2)) );
|
||||
idx1 = (oo_.mom.obs_var == find(oo_.dr.order_var==M_.matched_moments{jm,1}(1)) );
|
||||
idx2 = (oo_.mom.obs_var == find(oo_.dr.order_var==M_.matched_moments{jm,1}(2)) );
|
||||
if nnz(M_.matched_moments{jm,2}) == 0
|
||||
% Covariance
|
||||
if options_mom_.prefilter
|
||||
|
|
@ -221,7 +221,7 @@ elseif strcmp(options_mom_.mom.mom_method,'SMM')
|
|||
y_sim = simult_(M_, options_mom_, dr.ys, dr, scaled_shock_series, options_mom_.order);
|
||||
% provide meaningful penalty if data is nan or inf
|
||||
if any(any(isnan(y_sim))) || any(any(isinf(y_sim)))
|
||||
if options_mom_.vector_output == 1 % lsqnonlin requires vector output
|
||||
if options_mom_.mom.vector_output == 1 % lsqnonlin requires vector output
|
||||
fval = Inf(size(oo_.mom.Sw,1),1);
|
||||
else
|
||||
fval = Inf;
|
||||
|
|
@ -229,14 +229,14 @@ elseif strcmp(options_mom_.mom.mom_method,'SMM')
|
|||
info(1)=180;
|
||||
info(4) = 0.1;
|
||||
exit_flag = 0;
|
||||
if options_mom_.vector_output == 1 % lsqnonlin requires vector output
|
||||
if options_mom_.mom.vector_output == 1 % lsqnonlin requires vector output
|
||||
fval = ones(size(oo_.mom.data_moments,1),1)*options_mom_.huge_number;
|
||||
end
|
||||
return
|
||||
end
|
||||
|
||||
% Remove burn-in and focus on observables (note that y_sim is in declaration order)
|
||||
y_sim = y_sim(oo_.dr.order_var(oo_.dr.obs_var) , end-options_mom_.mom.long+1:end)';
|
||||
y_sim = y_sim(oo_.dr.order_var(oo_.mom.obs_var) , end-options_mom_.mom.long+1:end)';
|
||||
|
||||
if ~all(diag(M_.H)==0)
|
||||
i_ME = setdiff([1:size(M_.H,1)],find(diag(M_.H) == 0)); % find ME with 0 variance
|
||||
|
|
@ -260,7 +260,7 @@ end
|
|||
moments_difference = oo_.mom.data_moments - oo_.mom.model_moments;
|
||||
residuals = sqrt(options_mom_.mom.weighting_matrix_scaling_factor)*oo_.mom.Sw*moments_difference;
|
||||
oo_.mom.Q = residuals'*residuals;
|
||||
if options_mom_.vector_output == 1 % lsqnonlin requires vector output
|
||||
if options_mom_.mom.vector_output == 1 % lsqnonlin requires vector output
|
||||
fval = residuals;
|
||||
if options_mom_.mom.penalized_estimator
|
||||
fval=[fval;(xparam1-oo_.prior.mean)./sqrt(diag(oo_.prior.variance))];
|
||||
|
|
@ -281,7 +281,7 @@ if options_mom_.mom.compute_derivs && options_mom_.mom.analytic_jacobian
|
|||
dmoments_difference = - oo_.mom.model_moments_params_derivs(:,jp);
|
||||
dresiduals = sqrt(options_mom_.mom.weighting_matrix_scaling_factor)*oo_.mom.Sw*dmoments_difference;
|
||||
|
||||
if options_mom_.vector_output == 1 % lsqnonlin requires vector output
|
||||
if options_mom_.mom.vector_output == 1 % lsqnonlin requires vector output
|
||||
if options_mom_.mom.penalized_estimator
|
||||
df(:,jp)=[dresiduals;dxparam1(:,jp)./sqrt(diag(oo_.prior.variance))];
|
||||
else
|
||||
|
|
|
|||
|
|
@ -122,6 +122,56 @@ function [oo_, options_mom_, M_] = run(bayestopt_, options_, oo_, estim_params_,
|
|||
% - enable first moments despite prefilter
|
||||
% - do "true" Bayesian GMM and SMM not only penalized
|
||||
|
||||
fprintf('\n==== Method of Moments Estimation (%s) ====\n\n',options_mom_.mom.mom_method)
|
||||
|
||||
|
||||
% -------------------------------------------------------------------------
|
||||
% checks if required structures exist
|
||||
% -------------------------------------------------------------------------
|
||||
if isempty(estim_params_) % structure storing the info about estimated parameters in the estimated_params block
|
||||
if ~(isfield(estim_params_,'nvx') && (size(estim_params_.var_exo,1)+size(estim_params_.var_endo,1)+size(estim_params_.corrx,1)+size(estim_params_.corrn,1)+size(estim_params_.param_vals,1))==0)
|
||||
error('method_of_moments: You need to provide an ''estimated_params'' block!')
|
||||
else
|
||||
error('method_of_moments: The ''estimated_params'' block must not be empty!')
|
||||
end
|
||||
end
|
||||
if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_method,'SMM')
|
||||
if ~isfield(M_,'matched_moments') || isempty(M_.matched_moments) % structure storing the moments used for GMM and SMM estimation
|
||||
error('method_of_moments: You need to provide a ''matched_moments'' block for ''mom_method=%s''!',options_mom_.mom.mom_method)
|
||||
end
|
||||
end
|
||||
if (~isempty(estim_params_.var_endo) || ~isempty(estim_params_.corrn)) && strcmp(options_mom_.mom.mom_method, 'GMM')
|
||||
error('method_of_moments: GMM estimation does not support measurement error(s) yet. Please specifiy them as a structural shock!')
|
||||
end
|
||||
doBayesianEstimation = [estim_params_.var_exo(:,5); estim_params_.var_endo(:,5); estim_params_.corrx(:,6); estim_params_.corrn(:,6); estim_params_.param_vals(:,5)];
|
||||
if all(doBayesianEstimation~=0)
|
||||
doBayesianEstimation = true;
|
||||
elseif all(doBayesianEstimation==0)
|
||||
doBayesianEstimation = false;
|
||||
else
|
||||
error('method_of_moments: Estimation must be either fully Frequentist or fully Bayesian. Maybe you forgot to specify a prior distribution!')
|
||||
end
|
||||
if ~isfield(options_,'varobs')
|
||||
error('method_of_moments: VAROBS statement is missing!')
|
||||
end
|
||||
check_varobs_are_endo_and_declared_once(options_.varobs,M_.endo_names);
|
||||
|
||||
|
||||
% -------------------------------------------------------------------------
|
||||
% options_mom_ structure
|
||||
% -------------------------------------------------------------------------
|
||||
% 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_ = mom.default_option_mom_values(options_mom_, options_, M_.dname, doBayesianEstimation);
|
||||
|
||||
|
||||
% -------------------------------------------------------------------------
|
||||
% workarounds
|
||||
% -------------------------------------------------------------------------
|
||||
% The TeX option crashes MATLAB R2014a run with "-nodisplay" option
|
||||
% (as is done from the testsuite).
|
||||
% Since we can’t directly test whether "-nodisplay" has been passed,
|
||||
|
|
@ -138,291 +188,71 @@ if isfield(options_mom_, 'TeX') && options_mom_.TeX && ~isoctave && matlab_ver_l
|
|||
warning('Disabling TeX option due to a bug in MATLAB R2014a with -nodisplay')
|
||||
options_mom_.TeX = false;
|
||||
end
|
||||
|
||||
% -------------------------------------------------------------------------
|
||||
% Step 0: Check if required structures and options exist
|
||||
% -------------------------------------------------------------------------
|
||||
if isempty(estim_params_) % structure storing the info about estimated parameters in the estimated_params block
|
||||
if ~(isfield(estim_params_,'nvx') && (size(estim_params_.var_exo,1)+size(estim_params_.var_endo,1)+size(estim_params_.corrx,1)+size(estim_params_.corrn,1)+size(estim_params_.param_vals,1))==0)
|
||||
error('method_of_moments: You need to provide an ''estimated_params'' block')
|
||||
else
|
||||
error('method_of_moments: The ''estimated_params'' block must not be empty')
|
||||
end
|
||||
end
|
||||
if ~isfield(M_,'matched_moments') || isempty(M_.matched_moments) % structure storing the moments used for the method of moments estimation
|
||||
error('method_of_moments: You need to provide a ''matched_moments'' block')
|
||||
end
|
||||
if ~isempty(bayestopt_) && any(bayestopt_.pshape==0) && any(bayestopt_.pshape~=0)
|
||||
error('method_of_moments: Estimation must be either fully classical or fully Bayesian. Maybe you forgot to specify a prior distribution.')
|
||||
end
|
||||
|
||||
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.\n')
|
||||
else
|
||||
options_mom_.logged_steady_state = 0;
|
||||
options_mom_.loglinear = false;
|
||||
end
|
||||
|
||||
fprintf('\n==== Method of Moments Estimation (%s) ====\n\n',options_mom_.mom.mom_method)
|
||||
|
||||
% -------------------------------------------------------------------------
|
||||
% Step 1a: Prepare options_mom_ structure
|
||||
% -------------------------------------------------------------------------
|
||||
% 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.
|
||||
|
||||
% Method of Moments estimation options that can be set by the user in the mod file, otherwise default values are provided
|
||||
if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_method,'SMM')
|
||||
options_mom_.mom = set_default_option(options_mom_.mom,'bartlett_kernel_lag',20); % bandwith in optimal weighting matrix
|
||||
options_mom_.mom = set_default_option(options_mom_.mom,'penalized_estimator',false); % include deviation from prior mean as additional moment restriction and use prior precision as weight
|
||||
options_mom_.mom = set_default_option(options_mom_.mom,'verbose',false); % display and store intermediate estimation results
|
||||
options_mom_.mom = set_default_option(options_mom_.mom,'weighting_matrix',{'DIAGONAL'; 'OPTIMAL'}); % weighting matrix in moments distance objective function at each iteration of estimation;
|
||||
% possible values are 'OPTIMAL', 'IDENTITY_MATRIX' ,'DIAGONAL' or a filename. Size of cell determines stages in iterated estimation.
|
||||
options_mom_.mom = set_default_option(options_mom_.mom,'weighting_matrix_scaling_factor',1); % scaling of weighting matrix in objective function
|
||||
options_mom_.mom = set_default_option(options_mom_.mom,'se_tolx',1e-5); % step size for numerical computation of standard errors
|
||||
options_mom_ = set_default_option(options_mom_,'order',1); % order of Taylor approximation in perturbation
|
||||
options_mom_ = set_default_option(options_mom_,'pruning',false); % use pruned state space system at higher-order
|
||||
% Checks for perturbation order
|
||||
if options_mom_.order < 1
|
||||
error('method_of_moments: The order of the Taylor approximation cannot be 0!')
|
||||
% temporary workaround for https://git.dynare.org/Dynare/dseries/-/issues/51
|
||||
if options_mom_.xls_sheet~=1
|
||||
evalin('base','options_.xls_sheet=options_mom_.xls_sheet');
|
||||
end
|
||||
if ~isempty(options_mom_.xls_range)
|
||||
evalin('base','options_.xls_range=options_mom_.xls_range');
|
||||
end
|
||||
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')
|
||||
options_mom_.mom = set_default_option(options_mom_.mom,'burnin',500); % number of periods dropped at beginning of simulation
|
||||
options_mom_.mom = set_default_option(options_mom_.mom,'bounded_shock_support',false); % trim shocks in simulation to +- 2 stdev
|
||||
options_mom_.mom = set_default_option(options_mom_.mom,'seed',24051986); % seed used in simulations
|
||||
options_mom_.mom = set_default_option(options_mom_.mom,'simulation_multiple',7); % multiple of the data length used for simulation
|
||||
if options_mom_.mom.simulation_multiple < 1
|
||||
fprintf('The simulation horizon is shorter than the data. Dynare resets the simulation_multiple to 5.\n')
|
||||
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')
|
||||
% Check for pruning with GMM at higher order
|
||||
% 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.\n');
|
||||
error('method_of_moments: Perturbation orders higher than 3 are not implemented for GMM estimation, try using SMM!');
|
||||
end
|
||||
end
|
||||
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.
|
||||
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
|
||||
% initialize flag to compute derivs in objective function (needed for GMM with either analytic_standard_errors or analytic_jacobian )
|
||||
options_mom_.mom.compute_derivs = false;
|
||||
|
||||
% General options that can be set by the user in the mod file, otherwise default values are provided
|
||||
options_mom_ = set_default_option(options_mom_,'dirname',M_.dname); % specify directory in which to store estimation output [not yet working]
|
||||
options_mom_ = set_default_option(options_mom_,'graph_format','eps'); % specify the file format(s) for graphs saved to disk
|
||||
options_mom_ = set_default_option(options_mom_,'nodisplay',false); % do not display the graphs, but still save them to disk
|
||||
options_mom_ = set_default_option(options_mom_,'nograph',false); % do not create graphs (which implies that they are not saved to the disk nor displayed)
|
||||
options_mom_ = set_default_option(options_mom_,'noprint',false); % do not print output to console
|
||||
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
|
||||
options_mom_ = set_default_option(options_mom_,'TeX',false); % print TeX tables and graphics
|
||||
options_mom_ = set_default_option(options_mom_,'verbosity',false); % print TeX tables and graphics
|
||||
|
||||
% Data and model options that can be set by the user in the mod file, otherwise default values are provided
|
||||
options_mom_ = set_default_option(options_mom_,'first_obs',1); % number of first observation
|
||||
options_mom_ = set_default_option(options_mom_,'logdata',false); % if data is already in logs
|
||||
options_mom_ = set_default_option(options_mom_,'nobs',NaN); % number of observations
|
||||
options_mom_ = set_default_option(options_mom_,'prefilter',false); % demean each data series by its empirical mean and use centered moments
|
||||
options_mom_ = set_default_option(options_mom_,'xls_sheet',1); % name of sheet with data in Excel
|
||||
options_mom_ = set_default_option(options_mom_,'xls_range',''); % range of data in Excel sheet
|
||||
% temporary workaround for https://git.dynare.org/Dynare/dseries/-/issues/51
|
||||
if options_mom_.xls_sheet~=1
|
||||
evalin('base','options_.xls_sheet=options_mom_.xls_sheet');
|
||||
end
|
||||
if ~isempty(options_mom_.xls_range)
|
||||
evalin('base','options_.xls_range=options_mom_.xls_range');
|
||||
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
|
||||
|
||||
% Recursive estimation and forecast are not supported
|
||||
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
|
||||
|
||||
% Optimization options that can be set by the user in the mod file, otherwise default values are provided
|
||||
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
|
||||
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 of moments distance
|
||||
else
|
||||
options_mom_ = set_default_option(options_mom_,'mode_compute',4); % specifies csminwel as fallback default option for minimization of moments distance
|
||||
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
|
||||
options_mom_ = set_default_option(options_mom_,'silent_optimizer',false); % run minimization of moments distance silently without displaying results or saving files in between
|
||||
% Check plot options that can be set by the user in the mod file, otherwise default values are provided
|
||||
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)
|
||||
|
||||
% Numerical algorithms options that can be set by the user in the mod file, otherwise default values are provided
|
||||
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
|
||||
options_mom_ = set_default_option(options_mom_,'dr_cycle_reduction',false); % use cycle reduction algorithm to solve the polynomial equation for retrieving the coefficients associated to the endogenous variables in the decision rule
|
||||
options_mom_ = set_default_option(options_mom_,'dr_cycle_reduction_tol',1e-7); % convergence criterion used in the cycle reduction algorithm
|
||||
options_mom_ = set_default_option(options_mom_,'dr_logarithmic_reduction',false); % use logarithmic reduction algorithm to solve the polynomial equation for retrieving the coefficients associated to the endogenous variables in the decision rule
|
||||
options_mom_ = set_default_option(options_mom_,'dr_logarithmic_reduction_maxiter',100); % maximum number of iterations used in the logarithmic reduction algorithm
|
||||
options_mom_ = set_default_option(options_mom_,'dr_logarithmic_reduction_tol',1e-12); % convergence criterion used in the cycle reduction algorithm
|
||||
options_mom_ = set_default_option(options_mom_,'lyapunov_db',false); % doubling algorithm (disclyap_fast) to solve Lyapunov equation to compute variance-covariance matrix of state variables
|
||||
options_mom_ = set_default_option(options_mom_,'lyapunov_fp',false); % fixed-point algorithm to solve Lyapunov equation to compute variance-covariance matrix of state variables
|
||||
options_mom_ = set_default_option(options_mom_,'lyapunov_srs',false); % square-root-solver (dlyapchol) algorithm to solve Lyapunov equation to compute variance-covariance matrix of state variables
|
||||
options_mom_ = set_default_option(options_mom_,'lyapunov_complex_threshold',1e-15); % complex block threshold for the upper triangular matrix in symmetric Lyapunov equation solver
|
||||
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
|
||||
options_mom_ = set_default_option(options_mom_,'sylvester_fp',false); % determines whether to use fixed point algorihtm to solve Sylvester equation (gensylv_fp), faster for large scale models
|
||||
options_mom_ = set_default_option(options_mom_,'sylvester_fixed_point_tol',1e-12); % convergence criterion used in the fixed point Sylvester solver
|
||||
options_mom_ = set_default_option(options_mom_,'qz_criterium',1-1e-6); % value used to split stable from unstable eigenvalues in reordering the Generalized Schur decomposition used for solving first order problems
|
||||
% if there are no unit roots one can use 1.0 (or slightly below) which we set as default; if they are possible, you may have have multiple unit roots and the accuracy decreases when computing the eigenvalues in lyapunov_symm
|
||||
% Note that unit roots are only possible at first-order, at higher order we set it to 1 in pruned_state_space_system and focus only on stationary observables.
|
||||
options_mom_ = set_default_option(options_mom_,'qz_zero_threshold',1e-6); % value used to test if a generalized eigenvalue is 0/0 in the generalized Schur decomposition
|
||||
options_mom_ = set_default_option(options_mom_,'schur_vec_tol',1e-11); % tolerance level used to find nonstationary variables in Schur decomposition of the transition matrix.
|
||||
options_mom_ = set_default_option(options_mom_,'trust_region_initial_step_bound_factor',1); % used in dynare_solve for trust_region
|
||||
if options_mom_.order > 2
|
||||
fprintf('Dynare will use ''k_order_solver'' as the order>2\n');
|
||||
options_mom_.k_order_solver = true;
|
||||
end
|
||||
|
||||
% -------------------------------------------------------------------------
|
||||
% Step 1b: Options that are set by the preprocessor and need to be carried over
|
||||
% initializations
|
||||
% -------------------------------------------------------------------------
|
||||
|
||||
% options related to VAROBS
|
||||
if ~isfield(options_,'varobs')
|
||||
error('method_of_moments: VAROBS statement is missing!')
|
||||
else
|
||||
options_mom_.varobs = options_.varobs; % observable variables in declaration order
|
||||
options_mom_.obs_nbr = length(options_mom_.varobs); % number of observed variables
|
||||
% Check that each declared observed variable is also an endogenous variable
|
||||
for i = 1:options_mom_.obs_nbr
|
||||
if ~any(strcmp(options_mom_.varobs{i},M_.endo_names))
|
||||
error(['method_of_moments: Unknown variable (' options_mom_.varobs{i} ')!'])
|
||||
end
|
||||
end
|
||||
|
||||
% Check that a variable is not declared as observed more than once
|
||||
if length(unique(options_mom_.varobs))<length(options_mom_.varobs)
|
||||
for i = 1:options_mom_.obs_nbr
|
||||
if sum(strcmp(options_mom_.varobs{i},options_mom_.varobs))>1
|
||||
error(['method_of_moments: A variable cannot be declared as observed more than once (' options_mom_.varobs{i} ')!'])
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
% options related to variable declarations
|
||||
if isfield(options_,'trend_coeffs')
|
||||
error('method_of_moments: %s does not allow for trend in data',options_mom_.mom.mom_method)
|
||||
end
|
||||
|
||||
% options related to estimated_params and estimated_params_init
|
||||
options_mom_.use_calibration_initialization = options_.use_calibration_initialization;
|
||||
|
||||
% options related to model block
|
||||
options_mom_.linear = options_.linear;
|
||||
options_mom_.use_dll = options_.use_dll;
|
||||
options_mom_.block = options_.block;
|
||||
options_mom_.bytecode = options_.bytecode;
|
||||
|
||||
% options related to steady command
|
||||
options_mom_.homotopy_force_continue = options_.homotopy_force_continue;
|
||||
options_mom_.homotopy_mode = options_.homotopy_mode;
|
||||
options_mom_.homotopy_steps = options_.homotopy_steps;
|
||||
options_mom_.markowitz = options_.markowitz;
|
||||
options_mom_.solve_algo = options_.solve_algo;
|
||||
options_mom_.solve_tolf = options_.solve_tolf;
|
||||
options_mom_.solve_tolx = options_.solve_tolx;
|
||||
options_mom_.steady = options_.steady;
|
||||
options_mom_.steadystate = options_.steadystate;
|
||||
options_mom_.steadystate_flag = options_.steadystate_flag;
|
||||
|
||||
% options related to dataset
|
||||
options_mom_.dataset = options_.dataset;
|
||||
options_mom_.initial_period = options_.initial_period;
|
||||
|
||||
% options related to endogenous prior restrictions are not supported
|
||||
options_mom_.endogenous_prior_restrictions.irf = {};
|
||||
options_mom_.endogenous_prior_restrictions.moment = {};
|
||||
if ~isempty(options_.endogenous_prior_restrictions.irf) && ~isempty(options_.endogenous_prior_restrictions.moment)
|
||||
fprintf('Endogenous prior restrictions are not supported yet and will be skipped.\n')
|
||||
end
|
||||
|
||||
% -------------------------------------------------------------------------
|
||||
% Step 1c: Options related to optimizers
|
||||
% -------------------------------------------------------------------------
|
||||
% mode_compute = 1, 3, 7, 11, 102, 11, 13
|
||||
% nothing to be done
|
||||
% mode_compute = 2
|
||||
options_mom_.saopt = options_.saopt;
|
||||
% mode_compute = 4
|
||||
options_mom_.csminwel = options_.csminwel;
|
||||
% mode_compute = 5
|
||||
options_mom_.newrat = options_.newrat;
|
||||
options_mom_.gstep = options_.gstep;
|
||||
% mode_compute = 6
|
||||
options_mom_.gmhmaxlik = options_.gmhmaxlik;
|
||||
options_mom_.mh_jscale = options_.mh_jscale;
|
||||
% mode_compute = 8
|
||||
options_mom_.simplex = options_.simplex;
|
||||
% mode_compute = 9
|
||||
options_mom_.cmaes = options_.cmaes;
|
||||
% mode_compute = 10
|
||||
options_mom_.simpsa = options_.simpsa;
|
||||
% mode_compute = 12
|
||||
options_mom_.particleswarm = options_.particleswarm;
|
||||
% mode_compute = 101
|
||||
options_mom_.solveopt = options_.solveopt;
|
||||
|
||||
options_mom_.gradient_method = options_.gradient_method;
|
||||
options_mom_.gradient_epsilon = options_.gradient_epsilon;
|
||||
options_mom_.analytic_derivation = 0;
|
||||
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_.vector_output= false; % specifies whether the objective function returns a vector
|
||||
|
||||
optimizer_vec=[options_mom_.mode_compute;num2cell(options_mom_.additional_optimizer_steps)]; % at each stage one can possibly use different optimizers sequentially
|
||||
|
||||
analytic_jacobian_optimizers = [1, 3, 4, 13, 101]; %these are currently supported, see to-do list
|
||||
|
||||
% -------------------------------------------------------------------------
|
||||
% Step 1d: Other options that need to be initialized
|
||||
% -------------------------------------------------------------------------
|
||||
options_mom_.initialize_estimated_parameters_with_the_prior_mode = 0; % needed by set_prior.m
|
||||
options_mom_.figures.textwidth = 0.8; %needed by plot_priors.m
|
||||
options_mom_.ramsey_policy = 0; % needed by evaluate_steady_state
|
||||
options_mom_ = set_default_option(options_mom_,'debug',false); %neeeded by e.g. check_plot
|
||||
options_mom_.risky_steadystate = false; %needed by resol
|
||||
options_mom_.threads = options_.threads; %needed by resol
|
||||
options_mom_.jacobian_flag = true;
|
||||
options_mom_.gstep = options_.gstep;
|
||||
|
||||
% options_mom.dsge_var = false; %needed by check_list_of_variables
|
||||
% options_mom.bayesian_irf = false; %needed by check_list_of_variables
|
||||
% options_mom.moments_varendo = false; %needed by check_list_of_variables
|
||||
% options_mom.smoother = false; %needed by check_list_of_variables
|
||||
% options_mom.filter_step_ahead = []; %needed by check_list_of_variables
|
||||
% options_mom.forecast = 0;
|
||||
%options_mom_ = set_default_option(options_mom_,'endo_vars_for_moment_computations_in_estimation',[]);
|
||||
|
||||
% -------------------------------------------------------------------------
|
||||
% Step 1e: Get variable orderings and state space representation
|
||||
% -------------------------------------------------------------------------
|
||||
oo_.dr = set_state_space(oo_.dr,M_,options_mom_);
|
||||
% Get index of observed variables in DR order
|
||||
oo_.dr.obs_var = [];
|
||||
for i=1:options_mom_.obs_nbr
|
||||
oo_.dr.obs_var = [oo_.dr.obs_var; find(strcmp(options_mom_.varobs{i}, M_.endo_names(oo_.dr.order_var)))];
|
||||
% create output directories to store results
|
||||
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
|
||||
% decision rule
|
||||
oo_.dr = set_state_space(oo_.dr,M_,options_mom_); % get state-space representation
|
||||
oo_.mom.obs_var = []; % create index of observed variables in DR order
|
||||
for i = 1:options_mom_.obs_nbr
|
||||
oo_.mom.obs_var = [oo_.mom.obs_var; find(strcmp(options_mom_.varobs{i}, M_.endo_names(oo_.dr.order_var)))];
|
||||
end
|
||||
|
||||
% -------------------------------------------------------------------------
|
||||
|
|
@ -493,23 +323,18 @@ options_mom_.mom.mom_nbr = size(M_.matched_moments,1);
|
|||
options_mom_.ar = max(cellfun(@max,M_.matched_moments(:,2))) - min(cellfun(@min,M_.matched_moments(:,2)));
|
||||
|
||||
%check that only observed variables are involved in moments
|
||||
not_observed_variables=setdiff(oo_.dr.inv_order_var([M_.matched_moments{:,1}]),oo_.dr.obs_var);
|
||||
not_observed_variables=setdiff(oo_.dr.inv_order_var([M_.matched_moments{:,1}]),oo_.mom.obs_var);
|
||||
if ~isempty(not_observed_variables)
|
||||
error('\nmethod_of_moments: You specified moments involving %s, but it is not a varobs.',M_.endo_names{oo_.dr.order_var(not_observed_variables)})
|
||||
end
|
||||
|
||||
% -------------------------------------------------------------------------
|
||||
% Step 3: Checks and transformations for estimated parameters, priors, and bounds
|
||||
% -------------------------------------------------------------------------
|
||||
|
||||
% -------------------------------------------------------------------------
|
||||
% estimated parameters: checks and transformations on values, priors, bounds
|
||||
% -------------------------------------------------------------------------
|
||||
% Set priors and bounds over the estimated parameters
|
||||
[xparam0, estim_params_, bayestopt_, lb, ub, M_] = set_prior(estim_params_, M_, options_mom_);
|
||||
|
||||
% Check measurement errors
|
||||
if (estim_params_.nvn || estim_params_.ncn) && strcmp(options_mom_.mom.mom_method, 'GMM')
|
||||
error('method_of_moments: GMM estimation does not support measurement error(s) yet. Please specifiy them as a structural shock.')
|
||||
end
|
||||
|
||||
% Check if enough moments for estimation
|
||||
if options_mom_.mom.mom_nbr < length(xparam0)
|
||||
fprintf('\n');
|
||||
|
|
@ -750,7 +575,7 @@ end
|
|||
test_for_deep_parameters_calibration(M_);
|
||||
|
||||
% If steady state of observed variables is non zero, set noconstant equal 0
|
||||
if all(abs(oo_.steady_state(oo_.dr.order_var(oo_.dr.obs_var)))<1e-9)
|
||||
if all(abs(oo_.steady_state(oo_.dr.order_var(oo_.mom.obs_var)))<1e-9)
|
||||
options_mom_.noconstant = 0; %identifying the constant based on just the initial parameter value is not feasible
|
||||
else
|
||||
options_mom_.noconstant = 0;
|
||||
|
|
@ -811,13 +636,13 @@ if options_mom_.mom.penalized_estimator
|
|||
fprintf('\n - penalized estimation using deviation from prior mean and weighted with prior precision');
|
||||
end
|
||||
|
||||
for i = 1:length(optimizer_vec)
|
||||
for i = 1:length(options_mom_.optimizer_vec)
|
||||
if i == 1
|
||||
str = '- optimizer (mode_compute';
|
||||
else
|
||||
str = ' (additional_optimizer_steps';
|
||||
end
|
||||
switch optimizer_vec{i}
|
||||
switch options_mom_.optimizer_vec{i}
|
||||
case 0
|
||||
fprintf('\n %s=0): no minimization',str);
|
||||
case 1
|
||||
|
|
|
|||
Loading…
Reference in New Issue