diff --git a/matlab/+mom/Jtest.m b/matlab/+mom/Jtest.m
index 254d55ceceb5b0e2f69a13e0676c0da82970199c..5c5001a3dbb0604c7f20508f7e25f476fab1a740 100644
--- a/matlab/+mom/Jtest.m
+++ b/matlab/+mom/Jtest.m
@@ -1,5 +1,5 @@
-function oo_ = Jtest(xparam, objective_function, Woptflag, oo_, options_mom_, bayestopt_, Bounds, estim_params_, M_, nobs)
-% function oo_ = Jtest(xparam, objective_function, Woptflag, oo_, options_mom_, bayestopt_, Bounds, estim_params_, M_, nobs)
+function oo_ = Jtest(xparam, objective_function, Woptflag, oo_, options_mom_, bayestopt_, BoundsInfo, estim_params_, M_, nobs)
+% function oo_ = Jtest(xparam, objective_function, Woptflag, oo_, options_mom_, bayestopt_, BoundsInfo, estim_params_, M_, nobs)
% -------------------------------------------------------------------------
% Computes the J-test statistic and p-value for a GMM/SMM estimation
% =========================================================================
@@ -10,7 +10,7 @@ function oo_ = Jtest(xparam, objective_function, Woptflag, oo_, options_mom_, ba
% oo_: [struct] results
% options_mom_: [struct] options
% bayestopt_: [struct] information on priors
-% Bounds: [struct] bounds on parameters
+% BoundsInfo: [struct] bounds on parameters
% estim_params_: [struct] information on estimated parameters
% M_: [struct] information on the model
% nobs: [scalar] number of observations
@@ -49,7 +49,7 @@ if options_mom_.mom.mom_nbr > length(xparam)
W_opt = mom.optimal_weighting_matrix(oo_.mom.m_data, oo_.mom.model_moments, options_mom_.mom.bartlett_kernel_lag);
oo_J = oo_;
oo_J.mom.Sw = chol(W_opt);
- fval = feval(objective_function, xparam, Bounds, oo_J, estim_params_, M_, options_mom_);
+ fval = feval(objective_function, xparam, BoundsInfo, oo_J, estim_params_, M_, options_mom_);
else
fval = oo_.mom.Q;
end
diff --git a/matlab/+mom/mode_compute_gmm_smm.m b/matlab/+mom/mode_compute_gmm_smm.m
index 2c1fc5ef6772b646a175701162e0190a3aefaebf..5514b0bd6a25bca52a8f1d824e3c3c2efa6173a8 100644
--- a/matlab/+mom/mode_compute_gmm_smm.m
+++ b/matlab/+mom/mode_compute_gmm_smm.m
@@ -1,5 +1,5 @@
-function [xparam1, oo_, Woptflag] = mode_compute_gmm_smm(xparam0, objective_function, oo_, M_, options_mom_, estim_params_, bayestopt_, Bounds)
-% function [xparam1, oo_, Woptflag] = mode_compute_gmm_smm(xparam0, objective_function, oo_, M_, options_mom_, estim_params_, bayestopt_, Bounds)
+function [xparam1, oo_, Woptflag] = mode_compute_gmm_smm(xparam0, objective_function, oo_, M_, options_mom_, estim_params_, bayestopt_, BoundsInfo)
+% function [xparam1, oo_, Woptflag] = mode_compute_gmm_smm(xparam0, objective_function, oo_, M_, options_mom_, estim_params_, bayestopt_, BoundsInfo)
% -------------------------------------------------------------------------
% Iterated method of moments for GMM and SMM, computes the minimum of the
% objective function (distance between data moments and model moments)
@@ -14,7 +14,7 @@ function [xparam1, oo_, Woptflag] = mode_compute_gmm_smm(xparam0, objective_func
% options_mom_: [structure] options
% estim_params_: [structure] information on estimated parameters
% bayestopt_: [structure] information on priors
-% Bounds: [structure] bounds for optimization
+% BoundsInfo: [structure] bounds for optimization
% -------------------------------------------------------------------------
% OUTPUT
% xparam1: [vector] mode of objective function
@@ -100,7 +100,7 @@ for stage_iter = 1:size(options_mom_.mom.weighting_matrix,1)
options_mom_.current_optimizer = options_mom_.optimizer_vec{optim_iter};
if options_mom_.optimizer_vec{optim_iter} == 0
xparam1 = xparam0; % no minimization, evaluate objective at current values
- fval = feval(objective_function, xparam1, Bounds, oo_, estim_params_, M_, options_mom_);
+ fval = feval(objective_function, xparam1, BoundsInfo, oo_, estim_params_, M_, options_mom_);
else
if options_mom_.optimizer_vec{optim_iter} == 13
options_mom_.mom.vector_output = true;
@@ -112,8 +112,8 @@ for stage_iter = 1:size(options_mom_.mom.weighting_matrix,1)
else
options_mom_.mom.compute_derivs = false;
end
- [xparam1, fval] = dynare_minimize_objective(objective_function, xparam0, options_mom_.optimizer_vec{optim_iter}, options_mom_, [Bounds.lb Bounds.ub], bayestopt_.name, bayestopt_, [],...
- Bounds, oo_, estim_params_, M_, options_mom_);
+ [xparam1, fval] = dynare_minimize_objective(objective_function, xparam0, options_mom_.optimizer_vec{optim_iter}, options_mom_, [BoundsInfo.lb BoundsInfo.ub], bayestopt_.name, bayestopt_, [],...
+ BoundsInfo, oo_, estim_params_, M_, options_mom_);
if options_mom_.mom.vector_output
fval = fval'*fval;
end
@@ -125,5 +125,5 @@ for stage_iter = 1:size(options_mom_.mom.weighting_matrix,1)
xparam0 = xparam1;
end
options_mom_.vector_output = false;
- [~, ~, ~,~,~, 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
+ [~, ~, ~,~,~, 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
end
diff --git a/matlab/+mom/objective_function.m b/matlab/+mom/objective_function.m
index 4144673539dddd4212e8deb6b42b4e608bad7dce..c6b06948ebe7cfb4336bbbcd656091af0390f88f 100644
--- a/matlab/+mom/objective_function.m
+++ b/matlab/+mom/objective_function.m
@@ -1,11 +1,11 @@
-function [fval, info, exit_flag, df, junkHessian, oo_, M_] = objective_function(xparam, Bounds, oo_, estim_params_, M_, options_mom_)
-% [fval, info, exit_flag, df, junk1, oo_, M_] = objective_function(xparam, Bounds, oo_, estim_params_, M_, options_mom_)
+function [fval, info, exit_flag, df, junkHessian, oo_, M_] = objective_function(xparam, BoundsInfo, oo_, estim_params_, M_, options_mom_)
+% [fval, info, exit_flag, df, junk1, oo_, M_] = objective_function(xparam, BoundsInfo, oo_, estim_params_, M_, options_mom_)
% -------------------------------------------------------------------------
% This function evaluates the objective function for method of moments estimation
% =========================================================================
% INPUTS
% o xparam: [vector] current value of estimated parameters as returned by set_prior()
-% o Bounds: [structure] containing parameter bounds
+% o BoundsInfo: [structure] containing parameter bounds
% o oo_: [structure] for results
% o estim_params_: [structure] describing the estimated_parameters
% o M_ [structure] describing the model
@@ -86,7 +86,7 @@ end
% Ensure that xparam1 is a column vector; particleswarm.m requires this.
xparam = xparam(:);
M_ = set_all_parameters(xparam, estim_params_, M_);
-[fval,info,exit_flag] = check_bounds_and_definiteness_estimation(xparam, M_, estim_params_, Bounds);
+[fval,info,exit_flag] = check_bounds_and_definiteness_estimation(xparam, M_, estim_params_, BoundsInfo);
if info(1)
if options_mom_.mom.vector_output == 1 % lsqnonlin requires vector output
fval = ones(size(oo_.mom.data_moments,1),1)*options_mom_.huge_number;
diff --git a/matlab/+mom/run.m b/matlab/+mom/run.m
index b5579aeb49ce6bb483a50334113b0c7e86ff9a5b..57464756a10c40cae337482a123fe978c3faf01e 100644
--- a/matlab/+mom/run.m
+++ b/matlab/+mom/run.m
@@ -333,14 +333,14 @@ if ~isempty(bayestopt_) && doBayesianEstimation
end
end
% set prior bounds
- Bounds = prior_bounds(bayestopt_, options_mom_.prior_trunc);
- Bounds.lb = max(Bounds.lb,lb);
- Bounds.ub = min(Bounds.ub,ub);
+ BoundsInfo = prior_bounds(bayestopt_, options_mom_.prior_trunc);
+ BoundsInfo.lb = max(BoundsInfo.lb,lb);
+ BoundsInfo.ub = min(BoundsInfo.ub,ub);
else
% no priors are declared so Dynare will estimate the parameters with
% Frequentist methods using inequality constraints for the parameters
- Bounds.lb = lb;
- Bounds.ub = ub;
+ BoundsInfo.lb = lb;
+ BoundsInfo.ub = ub;
if options_mom_.mom.penalized_estimator
fprintf('Penalized estimation turned off as you did not declare priors\n')
options_mom_.mom.penalized_estimator = 0;
@@ -348,18 +348,18 @@ else
end
% set correct bounds for standard deviations and correlations
-Bounds = mom.set_correct_bounds_for_stderr_corr(estim_params_,Bounds);
+BoundsInfo = mom.set_correct_bounds_for_stderr_corr(estim_params_,BoundsInfo);
% test if initial values of the estimated parameters are all between the prior lower and upper bounds
if options_mom_.use_calibration_initialization
try
- check_prior_bounds(xparam0,Bounds,M_,estim_params_,options_mom_,bayestopt_);
+ check_prior_bounds(xparam0,BoundsInfo,M_,estim_params_,options_mom_,bayestopt_);
catch last_error
fprintf('Cannot use parameter values from calibration as they violate the prior bounds.')
rethrow(last_error);
end
else
- check_prior_bounds(xparam0,Bounds,M_,estim_params_,options_mom_,bayestopt_);
+ check_prior_bounds(xparam0,BoundsInfo,M_,estim_params_,options_mom_,bayestopt_);
end
% check for positive definiteness
@@ -493,7 +493,7 @@ try
oo_.mom.Sw = eye(options_mom_.mom.mom_nbr); % initialize with identity weighting matrix
end
tic_id = tic;
- [fval, info, ~, ~, ~, oo_, M_] = feval(objective_function, xparam0, Bounds, oo_, estim_params_, M_, options_mom_);
+ [fval, info, ~, ~, ~, oo_, M_] = feval(objective_function, xparam0, BoundsInfo, oo_, estim_params_, M_, options_mom_);
elapsed_time = toc(tic_id);
if isnan(fval)
error('method_of_moments: The initial value of the objective function with identity weighting matrix is NaN!')
@@ -535,16 +535,16 @@ mom.print_info_on_estimation_settings(options_mom_, number_of_estimated_paramete
% -------------------------------------------------------------------------
if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_method,'SMM')
% compute mode
- [xparam1, oo_, Woptflag] = mom.mode_compute_gmm_smm(xparam0, objective_function, oo_, M_, options_mom_, estim_params_, bayestopt_, Bounds);
+ [xparam1, oo_, Woptflag] = mom.mode_compute_gmm_smm(xparam0, objective_function, oo_, M_, options_mom_, estim_params_, bayestopt_, BoundsInfo);
% compute standard errors at mode
options_mom_.mom.vector_output = false; % make sure flag is reset
M_ = set_all_parameters(xparam1,estim_params_,M_); % update M_ and oo_ (in particular to get oo_.mom.model_moments)
if strcmp(options_mom_.mom.mom_method,'GMM') && options_mom_.mom.analytic_standard_errors
options_mom_.mom.compute_derivs = true; % for GMM we compute derivatives analytically in the objective function with this flag
end
- [~, ~, ~,~,~, oo_] = feval(objective_function, xparam1, Bounds, oo_, estim_params_, M_, options_mom_); % compute model moments and oo_.mom.model_moments_params_derivs
+ [~, ~, ~,~,~, oo_] = feval(objective_function, xparam1, BoundsInfo, oo_, estim_params_, M_, options_mom_); % compute model moments and oo_.mom.model_moments_params_derivs
options_mom_.mom.compute_derivs = false; % reset to not compute derivatives in objective function during optimization
- [stdh,hessian_xparam1] = mom.standard_errors(xparam1, objective_function, Bounds, oo_, estim_params_, M_, options_mom_, Woptflag);
+ [stdh,hessian_xparam1] = mom.standard_errors(xparam1, objective_function, BoundsInfo, oo_, estim_params_, M_, options_mom_, Woptflag);
end
@@ -552,8 +552,8 @@ end
% checks for mode and hessian at the mode
% -------------------------------------------------------------------------
if options_mom_.mode_check.status
- mode_check(objective_function, xparam1, hessian_xparam1, options_mom_, M_, estim_params_, bayestopt_, Bounds, true,...
- Bounds, oo_, estim_params_, M_, options_mom_);
+ mode_check(objective_function, xparam1, hessian_xparam1, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, true,...
+ BoundsInfo, oo_, estim_params_, M_, options_mom_);
end
@@ -564,7 +564,7 @@ if strcmp(options_mom_.mom.mom_method,'SMM') || strcmp(options_mom_.mom.mom_meth
% Store results in output structure
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));
% J test
- oo_ = mom.Jtest(xparam1, objective_function, Woptflag, oo_, options_mom_, bayestopt_, Bounds, estim_params_, M_, dataset_.nobs);
+ oo_ = mom.Jtest(xparam1, objective_function, Woptflag, oo_, options_mom_, bayestopt_, BoundsInfo, estim_params_, M_, dataset_.nobs);
% display comparison of model moments and data moments
mom.display_comparison_moments(M_, options_mom_, oo_.mom.data_moments, oo_.mom.model_moments);
end
diff --git a/matlab/+mom/set_correct_bounds_for_stderr_corr.m b/matlab/+mom/set_correct_bounds_for_stderr_corr.m
index d65e9473be22b91c06559b512531a195c7add5bc..1bfac50a8bfa3c95a4e215c97ed7ac09b84ef7ef 100644
--- a/matlab/+mom/set_correct_bounds_for_stderr_corr.m
+++ b/matlab/+mom/set_correct_bounds_for_stderr_corr.m
@@ -1,14 +1,14 @@
-function Bounds = set_correct_bounds_for_stderr_corr(estim_params_,Bounds)
-% function Bounds = set_correct_bounds_for_stderr_corr(estim_params_,Bounds)
+function BoundsInfo = set_correct_bounds_for_stderr_corr(estim_params_,BoundsInfo)
+% function BoundsInfo = set_correct_bounds_for_stderr_corr(estim_params_,BoundsInfo)
% -------------------------------------------------------------------------
% Set correct bounds for standard deviation and corrrelation parameters
% =========================================================================
% INPUTS
% o estim_params_ [struct] information on estimated parameters
-% o Bounds [struct] information on bounds
+% o BoundsInfo [struct] information on bounds
% -------------------------------------------------------------------------
% OUTPUT
-% o Bounds [struct] updated bounds
+% o BoundsInfo [struct] updated bounds
% -------------------------------------------------------------------------
% This function is called by
% o mom.run
@@ -34,10 +34,10 @@ function Bounds = set_correct_bounds_for_stderr_corr(estim_params_,Bounds)
number_of_estimated_parameters = estim_params_.nvx+estim_params_.nvn+estim_params_.ncx+estim_params_.ncn+estim_params_.np;
% set correct bounds for standard deviations and corrrelations
param_of_interest = (1:number_of_estimated_parameters)'<=estim_params_.nvx+estim_params_.nvn;
-LB_below_0 = (Bounds.lb<0 & param_of_interest);
-Bounds.lb(LB_below_0) = 0;
+LB_below_0 = (BoundsInfo.lb<0 & param_of_interest);
+BoundsInfo.lb(LB_below_0) = 0;
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;
-LB_below_minus_1 = (Bounds.lb<-1 & param_of_interest);
-UB_above_1 = (Bounds.ub>1 & param_of_interest);
-Bounds.lb(LB_below_minus_1) = -1;
-Bounds.ub(UB_above_1) = 1;
\ No newline at end of file
+LB_below_minus_1 = (BoundsInfo.lb<-1 & param_of_interest);
+UB_above_1 = (BoundsInfo.ub>1 & param_of_interest);
+BoundsInfo.lb(LB_below_minus_1) = -1;
+BoundsInfo.ub(UB_above_1) = 1;
\ No newline at end of file
diff --git a/matlab/+mom/standard_errors.m b/matlab/+mom/standard_errors.m
index 6bfec1e72dadbdc117c1fec9767b27f0577324a2..0fac91d94133cd3f3937a4df0bfc68fbf907c253 100644
--- a/matlab/+mom/standard_errors.m
+++ b/matlab/+mom/standard_errors.m
@@ -1,5 +1,5 @@
-function [SE_values, Asympt_Var] = standard_errors(xparam, objective_function, Bounds, oo_, estim_params_, M_, options_mom_, Wopt_flag)
-% [SE_values, Asympt_Var] = standard_errors(xparam, objective_function, Bounds, oo_, estim_params_, M_, options_mom_, Wopt_flag)
+function [SE_values, Asympt_Var] = standard_errors(xparam, objective_function, BoundsInfo, oo_, estim_params_, M_, options_mom_, Wopt_flag)
+% [SE_values, Asympt_Var] = standard_errors(xparam, objective_function, BoundsInfo, oo_, estim_params_, M_, options_mom_, Wopt_flag)
% -------------------------------------------------------------------------
% This function computes standard errors to the method of moments estimates
% Adapted from replication codes of
@@ -8,7 +8,7 @@ function [SE_values, Asympt_Var] = standard_errors(xparam, objective_function, B
% INPUTS
% o xparam: value of estimated parameters as returned by set_prior()
% o objective_function string of objective function
-% o Bounds: structure containing parameter bounds
+% o BoundsInfo: structure containing parameter bounds
% o oo_: structure for results
% o estim_params_: structure describing the estimated_parameters
% o M_ structure describing the model
@@ -75,12 +75,12 @@ else
%Positive step
xparam_eps_p = xparam;
xparam_eps_p(i,1) = xparam_eps_p(i) + eps_value;
- [~, info_p, ~, ~,~, oo__p] = feval(objective_function, xparam_eps_p, Bounds, oo_, estim_params_, M_, options_mom_);
+ [~, info_p, ~, ~,~, oo__p] = feval(objective_function, xparam_eps_p, BoundsInfo, oo_, estim_params_, M_, options_mom_);
% Negative step
xparam_eps_m = xparam;
xparam_eps_m(i,1) = xparam_eps_m(i) - eps_value;
- [~, info_m, ~, ~,~, oo__m] = feval(objective_function, xparam_eps_m, Bounds, oo_, estim_params_, M_, options_mom_);
+ [~, info_m, ~, ~,~, oo__m] = feval(objective_function, xparam_eps_m, BoundsInfo, oo_, estim_params_, M_, options_mom_);
% The Jacobian:
if nnz(info_p)==0 && nnz(info_m)==0