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Merge branch 'mom_namespace' of git.dynare.org:JohannesPfeifer/dynare 

Ref. !1903
pac-components
Sébastien Villemot 2021-08-30 16:13:29 +02:00
parent 85c37636dd e944b783b9
commit d6483ef3f8
No known key found for this signature in database
GPG Key ID: 2CECE9350ECEBE4A
7 changed files with 39 additions and 42 deletions
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7
matlab/method_of_moments/method_of_moments_check_plot.m → matlab/+mom/check_plot.m
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@ -1,4 +1,5 @@
function method_of_moments_check_plot(fun,xparam,SE_vec,options_,M_,estim_params_,Bounds,bayestopt_,varargin)
function check_plot(fun,xparam,SE_vec,options_,M_,estim_params_,Bounds,bayestopt_,varargin)
%function check_plot(fun,xparam,SE_vec,options_,M_,estim_params_,Bounds,bayestopt_,varargin)
% Checks the estimated local minimum of the moment's distance objective
@ -44,7 +45,7 @@ if ~exist([M_.dname filesep 'graphs'],'dir')
end
if TeX && any(strcmp('eps',cellstr(options_.graph_format)))
fidTeX = fopen([M_.dname, '/graphs/', M_.fname '_MoMCheckPlots.tex'],'w');
fprintf(fidTeX,'%% TeX eps-loader file generated by method_of_moments_check_plot.m (Dynare).\n');
fprintf(fidTeX,'%% TeX eps-loader file generated by mom.check_plot.m (Dynare).\n');
fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
fprintf(fidTeX,' \n');
end
@ -119,7 +120,7 @@ for plt = 1:nbplt
else
y(i,1) = NaN;
if options_.debug
fprintf('method_of_moments_check_plot:: could not solve model for parameter %s at value %4.3f, error code: %u\n',name,z(i),info(1))
fprintf('mom.check_plot:: could not solve model for parameter %s at value %4.3f, error code: %u\n',name,z(i),info(1))
end
end
if options_.mom.penalized_estimator

13
matlab/method_of_moments/method_of_moments_data_moments.m → matlab/+mom/data_moments.m
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@ -1,5 +1,5 @@
function [dataMoments, m_data] = method_of_moments_data_moments(data, oo_, matched_moments_, options_mom_)
% [dataMoments, m_data] = method_of_moments_data_moments(data, oo_, matched_moments_, options_mom_)
function [dataMoments, m_data] = data_moments(data, oo_, matched_moments_, options_mom_)
% [dataMoments, m_data] = data_moments(data, oo_, matched_moments_, options_mom_)
% This function computes the user-selected empirical moments from data
% =========================================================================
% INPUTS
@ -13,8 +13,8 @@ function [dataMoments, m_data] = method_of_moments_data_moments(data, oo_, match
% o m_data [T x numMom] selected empirical moments at each point in time
% -------------------------------------------------------------------------
% This function is called by
% o method_of_moments.m
% o method_of_moments_objective_function.m
% o mom.run.m
% o mom.objective_function.m
% =========================================================================
% Copyright (C) 2020-2021 Dynare Team
%
@ -69,7 +69,4 @@ for jm = 1:options_mom_.mom.mom_nbr
end
end %function end
end %function end

8
matlab/method_of_moments/method_of_moments_objective_function.m → matlab/+mom/objective_function.m
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@ -1,5 +1,5 @@
function [fval, info, exit_flag, df, junk1, oo_, M_, options_mom_] = method_of_moments_objective_function(xparam1, Bounds, oo_, estim_params_, M_, options_mom_)
% [fval, info, exit_flag, df, junk1, oo_, M_, options_mom_] = method_of_moments_objective_function(xparam1, Bounds, oo_, estim_params_, M_, options_mom_)
function [fval, info, exit_flag, df, junk1, oo_, M_, options_mom_] = objective_function(xparam1, Bounds, oo_, estim_params_, M_, options_mom_)
% [fval, info, exit_flag, df, junk1, oo_, M_, options_mom_] = objective_function(xparam1, Bounds, oo_, estim_params_, M_, options_mom_)
% -------------------------------------------------------------------------
% This function evaluates the objective function for GMM/SMM estimation
% =========================================================================
@ -27,7 +27,7 @@ function [fval, info, exit_flag, df, junk1, oo_, M_, options_mom_] = method_of_m
% o options_mom_: structure information about all settings (specified by the user, preprocessor, and taken from global options_)
% -------------------------------------------------------------------------
% This function is called by
% o method_of_moments.m
% o mom.run.m
% o dynare_minimize_objective.m
% -------------------------------------------------------------------------
% This function calls
@ -250,7 +250,7 @@ elseif strcmp(options_mom_.mom.mom_method,'SMM')
if options_mom_.prefilter
y_sim = bsxfun(@minus, y_sim, mean(y_sim,1));
end
oo_.mom.model_moments = method_of_moments_data_moments(y_sim, oo_, M_.matched_moments, options_mom_);
oo_.mom.model_moments = mom.data_moments(y_sim, oo_, M_.matched_moments, options_mom_);
end

6
matlab/method_of_moments/method_of_moments_optimal_weighting_matrix.m → matlab/+mom/optimal_weighting_matrix.m
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@ -1,5 +1,5 @@
function W_opt = method_of_moments_optimal_weighting_matrix(m_data, moments, q_lag)
% W_opt = method_of_moments_optimal_weighting_matrix(m_data, moments, q_lag)
function W_opt = optimal_weighting_matrix(m_data, moments, q_lag)
% W_opt = optimal_weighting_matrix(m_data, moments, q_lag)
% -------------------------------------------------------------------------
% This function computes the optimal weigthing matrix by a Bartlett kernel with maximum lag q_lag
% Adapted from replication codes of
@ -14,7 +14,7 @@ function W_opt = method_of_moments_optimal_weighting_matrix(m_data, moments, q_l
% o W_opt [numMom x numMom] optimal weighting matrix
% -------------------------------------------------------------------------
% This function is called by
% o method_of_moments.m
% o mom.run.m
% -------------------------------------------------------------------------
% This function calls:
% o CorrMatrix (embedded)

32
matlab/method_of_moments/method_of_moments.m → matlab/+mom/run.m
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@ -1,5 +1,5 @@
function [oo_, options_mom_, M_] = method_of_moments(bayestopt_, options_, oo_, estim_params_, M_, options_mom_)
%function [oo_, options_mom_, M_] = method_of_moments(bayestopt_, options_, oo_, estim_params_, M_, options_mom_)
function [oo_, options_mom_, M_] = run(bayestopt_, options_, oo_, estim_params_, M_, options_mom_)
%function [oo_, options_mom_, M_] = run(bayestopt_, options_, oo_, estim_params_, M_, options_mom_)
% -------------------------------------------------------------------------
% This function performs a method of moments estimation with the following steps:
% Step 0: Check if required structures and options exist
@ -49,11 +49,11 @@ function [oo_, options_mom_, M_] = method_of_moments(bayestopt_, options_, oo_,
% o get_all_parameters.m
% o get_matrix_entries_for_psd_check.m
% o makedataset.m
% o method_of_moments_check_plot.m
% o method_of_moments_data_moments.m
% o method_of_moments_objective_function.m
% o method_of_moments_optimal_weighting_matrix
% o method_of_moments_standard_errors
% o mom.check_plot.m
% o mom.data_moments.m
% o mom.objective_function.m
% o mom.optimal_weighting_matrix
% o mom-standard_errors
% o plot_priors.m
% o print_info.m
% o prior_bounds.m
@ -628,7 +628,7 @@ end
fprintf('Computing data moments. Note that NaN values in the moments (due to leads and lags or missing data) are replaced by the mean of the corresponding moment\n');
% Get data moments for the method of moments
[oo_.mom.data_moments, oo_.mom.m_data] = method_of_moments_data_moments(dataset_.data, oo_, M_.matched_moments, options_mom_);
[oo_.mom.data_moments, oo_.mom.m_data] = mom.data_moments(dataset_.data, oo_, M_.matched_moments, options_mom_);
% Get shock series for SMM and set variance correction factor
if strcmp(options_mom_.mom.mom_method,'SMM')
@ -715,7 +715,7 @@ end
% -------------------------------------------------------------------------
% Step 6: checks for objective function at initial parameters
% -------------------------------------------------------------------------
objective_function = str2func('method_of_moments_objective_function');
objective_function = str2func('mom.objective_function');
try
% Check for NaN or complex values of moment-distance-funtion evaluated
@ -851,19 +851,19 @@ for stage_iter=1:size(options_mom_.mom.weighting_matrix,1)
fprintf(' - diagonal of optimal weighting matrix (Bartlett kernel with %d lags)\n', options_mom_.mom.bartlett_kernel_lag);
if stage_iter == 1
fprintf(' and using data-moments as initial estimate of model-moments\n');
weighting_matrix = diag(diag( method_of_moments_optimal_weighting_matrix(oo_.mom.m_data, oo_.mom.data_moments, options_mom_.mom.bartlett_kernel_lag) ));
weighting_matrix = diag(diag( mom.optimal_weighting_matrix(oo_.mom.m_data, oo_.mom.data_moments, options_mom_.mom.bartlett_kernel_lag) ));
else
fprintf(' and using previous stage estimate of model-moments\n');
weighting_matrix = diag(diag( method_of_moments_optimal_weighting_matrix(oo_.mom.m_data, oo_.mom.model_moments, options_mom_.mom.bartlett_kernel_lag) ));
weighting_matrix = diag(diag( mom.optimal_weighting_matrix(oo_.mom.m_data, oo_.mom.model_moments, options_mom_.mom.bartlett_kernel_lag) ));
end
case 'optimal'
fprintf(' - optimal weighting matrix (Bartlett kernel with %d lags)\n', options_mom_.mom.bartlett_kernel_lag);
if stage_iter == 1
fprintf(' and using data-moments as initial estimate of model-moments\n');
weighting_matrix = method_of_moments_optimal_weighting_matrix(oo_.mom.m_data, oo_.mom.data_moments, options_mom_.mom.bartlett_kernel_lag);
weighting_matrix = mom.optimal_weighting_matrix(oo_.mom.m_data, oo_.mom.data_moments, options_mom_.mom.bartlett_kernel_lag);
else
fprintf(' and using previous stage estimate of model-moments\n');
weighting_matrix = method_of_moments_optimal_weighting_matrix(oo_.mom.m_data, oo_.mom.model_moments, options_mom_.mom.bartlett_kernel_lag);
weighting_matrix = mom.optimal_weighting_matrix(oo_.mom.m_data, oo_.mom.model_moments, options_mom_.mom.bartlett_kernel_lag);
Woptflag = true;
end
otherwise %user specified matrix in file
@ -922,7 +922,7 @@ for stage_iter=1:size(options_mom_.mom.weighting_matrix,1)
[fval, ~, ~,~,~, oo_] = feval(objective_function, xparam1, Bounds, oo_, estim_params_, M_, options_mom_);
options_mom_.mom.compute_derivs = false; % reset to not compute derivatives in objective function during optimization
SE = method_of_moments_standard_errors(xparam1, objective_function, Bounds, oo_, estim_params_, M_, options_mom_, Woptflag);
SE = mom.standard_errors(xparam1, objective_function, Bounds, oo_, estim_params_, M_, options_mom_, Woptflag);
% Store results in output structure
oo_.mom = display_estimation_results_table(xparam1,SE,M_,options_mom_,estim_params_,bayestopt_laplace,oo_.mom,prior_dist_names,sprintf('%s (STAGE %u)',options_mom_.mom.mom_method,stage_iter),sprintf('%s_stage_%u',lower(options_mom_.mom.mom_method),stage_iter));
@ -934,7 +934,7 @@ end
if options_mom_.mom.mom_nbr > length(xparam1)
%get optimal weighting matrix for J test, if necessary
if ~Woptflag
W_opt = method_of_moments_optimal_weighting_matrix(oo_.mom.m_data, oo_.mom.model_moments, options_mom_.mom.bartlett_kernel_lag);
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, xparam1, Bounds, oo_j, estim_params_, M_, options_mom_);
@ -992,7 +992,7 @@ if options_mom_.TeX
end
if options_mom_.mode_check.status
method_of_moments_check_plot(objective_function,xparam1,SE,options_mom_,M_,estim_params_,Bounds,bayestopt_laplace,...
mom.check_plot(objective_function,xparam1,SE,options_mom_,M_,estim_params_,Bounds,bayestopt_laplace,...
Bounds, oo_, estim_params_, M_, options_mom_)
end

14
matlab/method_of_moments/method_of_moments_standard_errors.m → matlab/+mom/standard_errors.m
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@ -1,5 +1,5 @@
function [SE_values, Asympt_Var] = method_of_moments_standard_errors(xparam, objective_function, Bounds, oo_, estim_params_, M_, options_mom_, Wopt_flag)
% [SE_values, Asympt_Var] = method_of_moments_standard_errors(xparam, objective_function, Bounds, oo_, estim_params_, M_, options_mom_, Wopt_flag)
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)
% -------------------------------------------------------------------------
% This function computes standard errors to the method of moments estimates
% Adapted from replication codes of
@ -7,7 +7,7 @@ function [SE_values, Asympt_Var] = method_of_moments_standard_errors(xparam, obj
% =========================================================================
% INPUTS
% o xparam: value of estimated parameters as returned by set_prior()
% o objective_function string of objective function, either method_of_moments_GMM.m or method_of_moments_SMM.m
% o objective_function string of objective function
% o Bounds: structure containing parameter bounds
% o oo_: structure for results
% o estim_params_: structure describing the estimated_parameters
@ -20,13 +20,13 @@ function [SE_values, Asympt_Var] = method_of_moments_standard_errors(xparam, obj
% o Asympt_Var [nparam x nparam] asymptotic covariance matrix
% -------------------------------------------------------------------------
% This function is called by
% o method_of_moments.m
% o mom.run.m
% -------------------------------------------------------------------------
% This function calls:
% o get_the_name
% o get_error_message
% o method_of_moments_objective_function
% o method_of_moments_optimal_weighting_matrix
% o mom.objective_function
% o mom.optimal_weighting_matrix
% =========================================================================
% Copyright (C) 2020-2021 Dynare Team
%
@ -109,7 +109,7 @@ if Wopt_flag
Asympt_Var = 1/T*((D'*WW*D)\eye(dim_params));
else
% We do not have the optimal weighting matrix yet
WWopt = method_of_moments_optimal_weighting_matrix(oo_.mom.m_data, oo_.mom.model_moments, options_mom_.mom.bartlett_kernel_lag);
WWopt = mom.optimal_weighting_matrix(oo_.mom.m_data, oo_.mom.model_moments, options_mom_.mom.bartlett_kernel_lag);
S = WWopt\eye(size(WWopt,1));
AA = (D'*WW*D)\eye(dim_params);
Asympt_Var = 1/T*AA*D'*WW*S*WW*D*AA;

1
matlab/dynare_config.m
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@ -63,7 +63,6 @@ p = {'/distributions/' ; ...
'/optimization/' ; ...
'/ols/' ; ...
'/pac-tools/' ; ...
'/method_of_moments/' ; ...
'/discretionary_policy/' ; ...
'/accessors/' ; ...
'/modules/dseries/src/' ; ...