method_of_moments: rename Bounds to BoundsInfo
Willi Mutschler
parent
d7850a2bbe
commit
dedbb3be57
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@ -1,5 +1,5 @@
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function oo_ = Jtest(xparam, objective_function, Woptflag, oo_, options_mom_, bayestopt_, Bounds, estim_params_, M_, nobs)
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% function oo_ = Jtest(xparam, objective_function, Woptflag, oo_, options_mom_, bayestopt_, Bounds, estim_params_, M_, nobs)
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function oo_ = Jtest(xparam, objective_function, Woptflag, oo_, options_mom_, bayestopt_, BoundsInfo, estim_params_, M_, nobs)
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% function oo_ = Jtest(xparam, objective_function, Woptflag, oo_, options_mom_, bayestopt_, BoundsInfo, estim_params_, M_, nobs)
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% -------------------------------------------------------------------------
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% Computes the J-test statistic and p-value for a GMM/SMM estimation
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% =========================================================================
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@ -10,7 +10,7 @@ function oo_ = Jtest(xparam, objective_function, Woptflag, oo_, options_mom_, ba
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% oo_: [struct] results
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% options_mom_: [struct] options
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% bayestopt_: [struct] information on priors
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% Bounds: [struct] bounds on parameters
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% BoundsInfo: [struct] bounds on parameters
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% estim_params_: [struct] information on estimated parameters
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% M_: [struct] information on the model
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% nobs: [scalar] number of observations
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@ -49,7 +49,7 @@ if options_mom_.mom.mom_nbr > length(xparam)
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W_opt = mom.optimal_weighting_matrix(oo_.mom.m_data, oo_.mom.model_moments, options_mom_.mom.bartlett_kernel_lag);
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oo_J = oo_;
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oo_J.mom.Sw = chol(W_opt);
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fval = feval(objective_function, xparam, Bounds, oo_J, estim_params_, M_, options_mom_);
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fval = feval(objective_function, xparam, BoundsInfo, oo_J, estim_params_, M_, options_mom_);
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else
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fval = oo_.mom.Q;
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end
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@ -1,5 +1,5 @@
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function [xparam1, oo_, Woptflag] = mode_compute_gmm_smm(xparam0, objective_function, oo_, M_, options_mom_, estim_params_, bayestopt_, Bounds)
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% function [xparam1, oo_, Woptflag] = mode_compute_gmm_smm(xparam0, objective_function, oo_, M_, options_mom_, estim_params_, bayestopt_, Bounds)
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function [xparam1, oo_, Woptflag] = mode_compute_gmm_smm(xparam0, objective_function, oo_, M_, options_mom_, estim_params_, bayestopt_, BoundsInfo)
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% function [xparam1, oo_, Woptflag] = mode_compute_gmm_smm(xparam0, objective_function, oo_, M_, options_mom_, estim_params_, bayestopt_, BoundsInfo)
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% -------------------------------------------------------------------------
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% Iterated method of moments for GMM and SMM, computes the minimum of the
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% objective function (distance between data moments and model moments)
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@ -14,7 +14,7 @@ function [xparam1, oo_, Woptflag] = mode_compute_gmm_smm(xparam0, objective_func
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% options_mom_: [structure] options
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% estim_params_: [structure] information on estimated parameters
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% bayestopt_: [structure] information on priors
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% Bounds: [structure] bounds for optimization
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% BoundsInfo: [structure] bounds for optimization
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% -------------------------------------------------------------------------
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% OUTPUT
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% xparam1: [vector] mode of objective function
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@ -100,7 +100,7 @@ for stage_iter = 1:size(options_mom_.mom.weighting_matrix,1)
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options_mom_.current_optimizer = options_mom_.optimizer_vec{optim_iter};
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if options_mom_.optimizer_vec{optim_iter} == 0
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xparam1 = xparam0; % no minimization, evaluate objective at current values
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fval = feval(objective_function, xparam1, Bounds, oo_, estim_params_, M_, options_mom_);
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fval = feval(objective_function, xparam1, BoundsInfo, oo_, estim_params_, M_, options_mom_);
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else
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if options_mom_.optimizer_vec{optim_iter} == 13
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options_mom_.mom.vector_output = true;
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@ -112,8 +112,8 @@ for stage_iter = 1:size(options_mom_.mom.weighting_matrix,1)
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else
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options_mom_.mom.compute_derivs = false;
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end
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[xparam1, fval] = dynare_minimize_objective(objective_function, xparam0, options_mom_.optimizer_vec{optim_iter}, options_mom_, [Bounds.lb Bounds.ub], bayestopt_.name, bayestopt_, [],...
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Bounds, oo_, estim_params_, M_, options_mom_);
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[xparam1, fval] = dynare_minimize_objective(objective_function, xparam0, options_mom_.optimizer_vec{optim_iter}, options_mom_, [BoundsInfo.lb BoundsInfo.ub], bayestopt_.name, bayestopt_, [],...
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BoundsInfo, oo_, estim_params_, M_, options_mom_);
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if options_mom_.mom.vector_output
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fval = fval'*fval;
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end
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@ -125,5 +125,5 @@ for stage_iter = 1:size(options_mom_.mom.weighting_matrix,1)
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xparam0 = xparam1;
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end
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options_mom_.vector_output = false;
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[~, ~, ~,~,~, oo_] = feval(objective_function, xparam1, Bounds, oo_, estim_params_, M_, options_mom_); % get oo_.mom.model_moments for iterated GMM/SMM to compute optimal weighting matrix
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[~, ~, ~,~,~, oo_] = feval(objective_function, xparam1, BoundsInfo, oo_, estim_params_, M_, options_mom_); % get oo_.mom.model_moments for iterated GMM/SMM to compute optimal weighting matrix
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end
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@ -1,11 +1,11 @@
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function [fval, info, exit_flag, df, junkHessian, oo_, M_] = objective_function(xparam, Bounds, oo_, estim_params_, M_, options_mom_)
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% [fval, info, exit_flag, df, junk1, oo_, M_] = objective_function(xparam, Bounds, oo_, estim_params_, M_, options_mom_)
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function [fval, info, exit_flag, df, junkHessian, oo_, M_] = objective_function(xparam, BoundsInfo, oo_, estim_params_, M_, options_mom_)
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% [fval, info, exit_flag, df, junk1, oo_, M_] = objective_function(xparam, BoundsInfo, oo_, estim_params_, M_, options_mom_)
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% -------------------------------------------------------------------------
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% This function evaluates the objective function for method of moments estimation
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% =========================================================================
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% INPUTS
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% o xparam: [vector] current value of estimated parameters as returned by set_prior()
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% o Bounds: [structure] containing parameter bounds
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% o BoundsInfo: [structure] containing parameter bounds
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% o oo_: [structure] for results
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% o estim_params_: [structure] describing the estimated_parameters
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% o M_ [structure] describing the model
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@ -86,7 +86,7 @@ end
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% Ensure that xparam1 is a column vector; particleswarm.m requires this.
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xparam = xparam(:);
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M_ = set_all_parameters(xparam, estim_params_, M_);
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[fval,info,exit_flag] = check_bounds_and_definiteness_estimation(xparam, M_, estim_params_, Bounds);
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[fval,info,exit_flag] = check_bounds_and_definiteness_estimation(xparam, M_, estim_params_, BoundsInfo);
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if info(1)
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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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@ -333,14 +333,14 @@ if ~isempty(bayestopt_) && doBayesianEstimation
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end
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end
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% set prior bounds
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Bounds = prior_bounds(bayestopt_, options_mom_.prior_trunc);
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Bounds.lb = max(Bounds.lb,lb);
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Bounds.ub = min(Bounds.ub,ub);
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BoundsInfo = prior_bounds(bayestopt_, options_mom_.prior_trunc);
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BoundsInfo.lb = max(BoundsInfo.lb,lb);
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BoundsInfo.ub = min(BoundsInfo.ub,ub);
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else
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% no priors are declared so Dynare will estimate the parameters with
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% Frequentist methods using inequality constraints for the parameters
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Bounds.lb = lb;
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Bounds.ub = ub;
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BoundsInfo.lb = lb;
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BoundsInfo.ub = ub;
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if options_mom_.mom.penalized_estimator
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fprintf('Penalized estimation turned off as you did not declare priors\n')
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options_mom_.mom.penalized_estimator = 0;
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@ -348,18 +348,18 @@ else
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end
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% set correct bounds for standard deviations and correlations
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Bounds = mom.set_correct_bounds_for_stderr_corr(estim_params_,Bounds);
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BoundsInfo = mom.set_correct_bounds_for_stderr_corr(estim_params_,BoundsInfo);
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% test if initial values of the estimated parameters are all between the prior lower and upper bounds
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if options_mom_.use_calibration_initialization
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try
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check_prior_bounds(xparam0,Bounds,M_,estim_params_,options_mom_,bayestopt_);
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check_prior_bounds(xparam0,BoundsInfo,M_,estim_params_,options_mom_,bayestopt_);
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catch last_error
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fprintf('Cannot use parameter values from calibration as they violate the prior bounds.')
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rethrow(last_error);
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end
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else
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check_prior_bounds(xparam0,Bounds,M_,estim_params_,options_mom_,bayestopt_);
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check_prior_bounds(xparam0,BoundsInfo,M_,estim_params_,options_mom_,bayestopt_);
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end
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% check for positive definiteness
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@ -493,7 +493,7 @@ try
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oo_.mom.Sw = eye(options_mom_.mom.mom_nbr); % initialize with identity weighting matrix
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end
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tic_id = tic;
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[fval, info, ~, ~, ~, oo_, M_] = feval(objective_function, xparam0, Bounds, oo_, estim_params_, M_, options_mom_);
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[fval, info, ~, ~, ~, oo_, M_] = feval(objective_function, xparam0, BoundsInfo, oo_, estim_params_, M_, options_mom_);
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elapsed_time = toc(tic_id);
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if isnan(fval)
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error('method_of_moments: The initial value of the objective function with identity weighting matrix is NaN!')
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@ -535,16 +535,16 @@ mom.print_info_on_estimation_settings(options_mom_, number_of_estimated_paramete
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% -------------------------------------------------------------------------
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if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_method,'SMM')
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% compute mode
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[xparam1, oo_, Woptflag] = mom.mode_compute_gmm_smm(xparam0, objective_function, oo_, M_, options_mom_, estim_params_, bayestopt_, Bounds);
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[xparam1, oo_, Woptflag] = mom.mode_compute_gmm_smm(xparam0, objective_function, oo_, M_, options_mom_, estim_params_, bayestopt_, BoundsInfo);
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% compute standard errors at mode
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options_mom_.mom.vector_output = false; % make sure flag is reset
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M_ = set_all_parameters(xparam1,estim_params_,M_); % update M_ and oo_ (in particular to get oo_.mom.model_moments)
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if strcmp(options_mom_.mom.mom_method,'GMM') && options_mom_.mom.analytic_standard_errors
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options_mom_.mom.compute_derivs = true; % for GMM we compute derivatives analytically in the objective function with this flag
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end
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[~, ~, ~,~,~, oo_] = feval(objective_function, xparam1, Bounds, oo_, estim_params_, M_, options_mom_); % compute model moments and oo_.mom.model_moments_params_derivs
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[~, ~, ~,~,~, oo_] = feval(objective_function, xparam1, BoundsInfo, oo_, estim_params_, M_, options_mom_); % compute model moments and oo_.mom.model_moments_params_derivs
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options_mom_.mom.compute_derivs = false; % reset to not compute derivatives in objective function during optimization
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[stdh,hessian_xparam1] = mom.standard_errors(xparam1, objective_function, Bounds, oo_, estim_params_, M_, options_mom_, Woptflag);
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[stdh,hessian_xparam1] = mom.standard_errors(xparam1, objective_function, BoundsInfo, oo_, estim_params_, M_, options_mom_, Woptflag);
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end
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@ -552,8 +552,8 @@ end
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% checks for mode and hessian at the mode
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% -------------------------------------------------------------------------
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if options_mom_.mode_check.status
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mode_check(objective_function, xparam1, hessian_xparam1, options_mom_, M_, estim_params_, bayestopt_, Bounds, true,...
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Bounds, oo_, estim_params_, M_, options_mom_);
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mode_check(objective_function, xparam1, hessian_xparam1, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, true,...
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BoundsInfo, oo_, estim_params_, M_, options_mom_);
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end
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@ -564,7 +564,7 @@ if strcmp(options_mom_.mom.mom_method,'SMM') || strcmp(options_mom_.mom.mom_meth
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% Store results in output structure
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oo_.mom = display_estimation_results_table(xparam1,stdh,M_,options_mom_,estim_params_,bayestopt_,oo_.mom,prior_dist_names,options_mom_.mom.mom_method,lower(options_mom_.mom.mom_method));
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% J test
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oo_ = mom.Jtest(xparam1, objective_function, Woptflag, oo_, options_mom_, bayestopt_, Bounds, estim_params_, M_, dataset_.nobs);
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oo_ = mom.Jtest(xparam1, objective_function, Woptflag, oo_, options_mom_, bayestopt_, BoundsInfo, estim_params_, M_, dataset_.nobs);
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% display comparison of model moments and data moments
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mom.display_comparison_moments(M_, options_mom_, oo_.mom.data_moments, oo_.mom.model_moments);
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end
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@ -1,14 +1,14 @@
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function Bounds = set_correct_bounds_for_stderr_corr(estim_params_,Bounds)
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% function Bounds = set_correct_bounds_for_stderr_corr(estim_params_,Bounds)
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function BoundsInfo = set_correct_bounds_for_stderr_corr(estim_params_,BoundsInfo)
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% function BoundsInfo = set_correct_bounds_for_stderr_corr(estim_params_,BoundsInfo)
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% -------------------------------------------------------------------------
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% Set correct bounds for standard deviation and corrrelation parameters
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% =========================================================================
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% INPUTS
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% o estim_params_ [struct] information on estimated parameters
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% o Bounds [struct] information on bounds
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% o BoundsInfo [struct] information on bounds
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% -------------------------------------------------------------------------
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% OUTPUT
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% o Bounds [struct] updated bounds
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% o BoundsInfo [struct] updated bounds
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% -------------------------------------------------------------------------
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% This function is called by
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% o mom.run
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@ -34,10 +34,10 @@ function Bounds = set_correct_bounds_for_stderr_corr(estim_params_,Bounds)
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number_of_estimated_parameters = estim_params_.nvx+estim_params_.nvn+estim_params_.ncx+estim_params_.ncn+estim_params_.np;
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% set correct bounds for standard deviations and corrrelations
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param_of_interest = (1:number_of_estimated_parameters)'<=estim_params_.nvx+estim_params_.nvn;
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LB_below_0 = (Bounds.lb<0 & param_of_interest);
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Bounds.lb(LB_below_0) = 0;
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LB_below_0 = (BoundsInfo.lb<0 & param_of_interest);
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BoundsInfo.lb(LB_below_0) = 0;
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param_of_interest = (1:number_of_estimated_parameters)'> estim_params_.nvx+estim_params_.nvn & (1:number_of_estimated_parameters)'<estim_params_.nvx+estim_params_.nvn +estim_params_.ncx + estim_params_.ncn;
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LB_below_minus_1 = (Bounds.lb<-1 & param_of_interest);
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UB_above_1 = (Bounds.ub>1 & param_of_interest);
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Bounds.lb(LB_below_minus_1) = -1;
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Bounds.ub(UB_above_1) = 1;
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LB_below_minus_1 = (BoundsInfo.lb<-1 & param_of_interest);
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UB_above_1 = (BoundsInfo.ub>1 & param_of_interest);
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BoundsInfo.lb(LB_below_minus_1) = -1;
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BoundsInfo.ub(UB_above_1) = 1;
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@ -1,5 +1,5 @@
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function [SE_values, Asympt_Var] = standard_errors(xparam, objective_function, Bounds, oo_, estim_params_, M_, options_mom_, Wopt_flag)
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% [SE_values, Asympt_Var] = standard_errors(xparam, objective_function, Bounds, oo_, estim_params_, M_, options_mom_, Wopt_flag)
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function [SE_values, Asympt_Var] = standard_errors(xparam, objective_function, BoundsInfo, oo_, estim_params_, M_, options_mom_, Wopt_flag)
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% [SE_values, Asympt_Var] = standard_errors(xparam, objective_function, BoundsInfo, oo_, estim_params_, M_, options_mom_, Wopt_flag)
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% -------------------------------------------------------------------------
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% This function computes standard errors to the method of moments estimates
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% Adapted from replication codes of
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@ -8,7 +8,7 @@ function [SE_values, Asympt_Var] = standard_errors(xparam, objective_function, B
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% INPUTS
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% o xparam: value of estimated parameters as returned by set_prior()
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% o objective_function string of objective function
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% o Bounds: structure containing parameter bounds
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% o BoundsInfo: structure containing parameter bounds
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% o oo_: structure for results
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% o estim_params_: structure describing the estimated_parameters
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% o M_ structure describing the model
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@ -75,12 +75,12 @@ else
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%Positive step
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xparam_eps_p = xparam;
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xparam_eps_p(i,1) = xparam_eps_p(i) + eps_value;
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[~, info_p, ~, ~,~, oo__p] = feval(objective_function, xparam_eps_p, Bounds, oo_, estim_params_, M_, options_mom_);
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[~, info_p, ~, ~,~, oo__p] = feval(objective_function, xparam_eps_p, BoundsInfo, oo_, estim_params_, M_, options_mom_);
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% Negative step
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xparam_eps_m = xparam;
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xparam_eps_m(i,1) = xparam_eps_m(i) - eps_value;
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[~, info_m, ~, ~,~, oo__m] = feval(objective_function, xparam_eps_m, Bounds, oo_, estim_params_, M_, options_mom_);
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[~, info_m, ~, ~,~, oo__m] = feval(objective_function, xparam_eps_m, BoundsInfo, oo_, estim_params_, M_, options_mom_);
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% The Jacobian:
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if nnz(info_p)==0 && nnz(info_m)==0
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