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Merge branch 'MoM_optim_gradient' into 'master' 

Method of Moments: use analytical Jacobian in gradient-based optimizers (GMM only)

See merge request Dynare/dynare!1801
time-shift
Sébastien Villemot 2021-01-14 16:04:14 +00:00
parent 6639cd8a41 52a57eaf60
commit b4eb68d71c
10 changed files with 281 additions and 18 deletions
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43
matlab/method_of_moments/method_of_moments.m
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@ -93,6 +93,7 @@ function [oo_, options_mom_, M_] = method_of_moments(bayestopt_, options_, oo_,
% - [ ] deal with measurement errors (once @wmutschl has implemented this in identification toolbox)
% - [ ] improve check for duplicate moments by using the cellfun and unique functions
% - [ ] dirname option to save output to different directory not yet implemented
% - [ ] add analytic_jacobian option for mode_compute 4 and 101
% -------------------------------------------------------------------------
% Step 0: Check if required structures and options exist
% -------------------------------------------------------------------------
@ -165,9 +166,10 @@ if strcmp(options_mom_.mom.mom_method,'GMM')
error('method_of_moments: perturbation orders higher than 3 are not implemented for GMM estimation, try using SMM.\n');
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
end
options_mom_.mom.compute_derivs = false;% flag to compute derivs in objective function (needed for analytic standard errors with GMM)
% 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]
@ -330,6 +332,10 @@ options_mom_.analytic_derivation_mode = 0; % needed by get_perturbation_params_d
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, 13]; %these are currently supported, see to-do list
% -------------------------------------------------------------------------
% Step 1d: Other options that need to be initialized
% -------------------------------------------------------------------------
@ -708,6 +714,7 @@ end
% Step 6: checks for objective function at initial parameters
% -------------------------------------------------------------------------
objective_function = str2func('method_of_moments_objective_function');
try
% Check for NaN or complex values of moment-distance-funtion evaluated
% at initial parameters and identity weighting matrix
@ -757,7 +764,7 @@ end
if options_mom_.mom.penalized_estimator
fprintf('\n - penalized estimation using deviation from prior mean and weighted with prior precision');
end
optimizer_vec=[options_mom_.mode_compute;num2cell(options_mom_.additional_optimizer_steps)]; % at each stage one can possibly use different optimizers sequentially
for i = 1:length(optimizer_vec)
if i == 1
str = '- optimizer (mode_compute';
@ -807,6 +814,9 @@ for i = 1:length(optimizer_vec)
if options_mom_.silent_optimizer
fprintf(' (silent)');
end
if strcmp(options_mom_.mom.mom_method,'GMM') && options_mom_.mom.analytic_jacobian && ismember(optimizer_vec{i},analytic_jacobian_optimizers)
fprintf(' (using analytical Jacobian)');
end
end
fprintf('\n - perturbation order: %d', options_mom_.order)
if options_mom_.order > 1 && options_mom_.pruning
@ -828,6 +838,7 @@ if size(options_mom_.mom.weighting_matrix,1)>1 && ~(any(strcmpi('diagonal',optio
fprintf('\nYou did not specify the use of an optimal or diagonal weighting matrix. There is no point in running an iterated method of moments.\n')
end
optim_opt0 = options_mom_.optim_opt; % store original options set by user
for stage_iter=1:size(options_mom_.mom.weighting_matrix,1)
fprintf('Estimation stage %u\n',stage_iter);
Woptflag = false;
@ -873,16 +884,36 @@ for stage_iter=1:size(options_mom_.mom.weighting_matrix,1)
end
for optim_iter= 1:length(optimizer_vec)
options_mom_.current_optimizer = optimizer_vec{optim_iter};
if 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_);
else
if optimizer_vec{optim_iter}==13
options_mom_.vector_output = true;
options_mom_.vector_output = true;
else
options_mom_.vector_output = false;
options_mom_.vector_output = false;
end
[xparam1, fval, exitflag] = dynare_minimize_objective(objective_function, xparam0, optimizer_vec{optim_iter}, options_mom_, [Bounds.lb Bounds.ub], bayestopt_laplace.name, bayestopt_laplace, [],...
if strcmp(options_mom_.mom.mom_method,'GMM') && options_mom_.mom.analytic_jacobian && ismember(optimizer_vec{optim_iter},analytic_jacobian_optimizers) %do this only for gradient-based optimizers
options_mom_.mom.compute_derivs = true;
objective_function1 = str2func('method_of_moments_objective_function_gradient_helper');
switch optimizer_vec{optim_iter}
case 1
options_mom_.optim_opt = [optim_opt0, ',''GradObj'',''on''']; % make sure GradObj option is on for fmincon
case 3
options_mom_.optim_opt = [optim_opt0, ',''GradObj'',''on''']; % make sure GradObj option is on for fmincon
case 13
options_mom_.optim_opt = [optim_opt0, ',''Jacobian'',''on''']; % make sure Jacobian option is on for lsqnonlin
end
if strcmp(options_mom_.optim_opt(1),',')
options_mom_.optim_opt(1) = []; %remove the comma if optim_opt was empty
end
else
options_mom_.mom.compute_derivs = false;
objective_function1 = objective_function;
end
[xparam1, fval, exitflag] = dynare_minimize_objective(objective_function1, xparam0, optimizer_vec{optim_iter}, options_mom_, [Bounds.lb Bounds.ub], bayestopt_laplace.name, bayestopt_laplace, [],...
Bounds, oo_, estim_params_, M_, options_mom_);
if options_mom_.vector_output
fval = fval'*fval;

55
matlab/method_of_moments/method_of_moments_objective_function.m
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@ -1,4 +1,4 @@
function [fval, info, exit_flag, junk1, junk2, oo_, M_, options_mom_] = method_of_moments_objective_function(xparam1, Bounds, oo_, estim_params_, M_, options_mom_)
function [fval, info, exit_flag, junk1, junk2, oo_, M_, options_mom_, df] = method_of_moments_objective_function(xparam1, Bounds, oo_, estim_params_, M_, options_mom_)
% [fval, info, exit_flag, junk1, junk2, oo_, M_, options_mom_] = method_of_moments_objective_function(xparam1, Bounds, oo_, estim_params_, M_, options_mom_)
% -------------------------------------------------------------------------
% This function evaluates the objective function for GMM/SMM estimation
@ -21,9 +21,12 @@ function [fval, info, exit_flag, junk1, junk2, oo_, M_, options_mom_] = method_o
% - mom.model_moments [numMom x 1] vector with model moments
% - mom.Q value of the quadratic form of the moment difference
% o M_: Matlab's structure describing the model
% o options_mom_: structure information about all settings (specified by the user, preprocessor, and taken from global options_)
% o df: analytical parameter Jacobian of the quadratic form of the moment difference (for GMM only)
% -------------------------------------------------------------------------
% This function is called by
% o method_of_moments.m
% o dynare_minimize_objective.m
% -------------------------------------------------------------------------
% This function calls
% o check_bounds_and_definiteness_estimation
@ -56,7 +59,15 @@ function [fval, info, exit_flag, junk1, junk2, oo_, M_, options_mom_] = method_o
%------------------------------------------------------------------------------
% 0. Initialization of the returned variables and others...
%------------------------------------------------------------------------------
if options_mom_.vector_output == 1
if options_mom_.mom.penalized_estimator
df = nan(size(oo_.mom.data_moments,1)+length(xparam1),length(xparam1));
else
df = nan(size(oo_.mom.data_moments,1),length(xparam1));
end
else
df = nan(1,length(xparam1));
end
junk1 = [];
junk2 = [];
@ -112,7 +123,7 @@ if strcmp(options_mom_.mom.mom_method,'GMM')
%--------------------------------------------------------------------------
% 3. Set up pruned state-space system and compute model moments
%--------------------------------------------------------------------------
if options_mom_.mom.compute_derivs && options_mom_.mom.analytic_standard_errors
if options_mom_.mom.compute_derivs && ( options_mom_.mom.analytic_standard_errors || options_mom_.mom.analytic_jacobian )
indpmodel = []; %initialize index for model parameters
if ~isempty(estim_params_.param_vals)
indpmodel = estim_params_.param_vals(:,1); %values correspond to parameters declaration order, row number corresponds to order in estimated_params
@ -146,7 +157,7 @@ if strcmp(options_mom_.mom.mom_method,'GMM')
E_y = pruned_state_space.E_y;
E_y_nbr = nnz(options_mom_.mom.index.E_y);
oo_.mom.model_moments(offset+1:E_y_nbr,1) = E_y(options_mom_.mom.index.E_y);
if options_mom_.mom.compute_derivs && options_mom_.mom.analytic_standard_errors
if options_mom_.mom.compute_derivs && ( options_mom_.mom.analytic_standard_errors || options_mom_.mom.analytic_jacobian )
oo_.mom.model_moments_params_derivs(offset+1:E_y_nbr,:) = pruned_state_space.dE_y(options_mom_.mom.index.E_y,:);
end
offset = offset + E_y_nbr;
@ -156,12 +167,12 @@ if strcmp(options_mom_.mom.mom_method,'GMM')
if isfield(options_mom_.mom.index,'E_yy') && nnz(options_mom_.mom.index.E_yy) > 0
if options_mom_.prefilter
E_yy = pruned_state_space.Var_y;
if options_mom_.mom.compute_derivs && options_mom_.mom.analytic_standard_errors
if options_mom_.mom.compute_derivs && ( options_mom_.mom.analytic_standard_errors || options_mom_.mom.analytic_jacobian )
dE_yy = pruned_state_space.dVar_y;
end
else
E_yy = pruned_state_space.Var_y + pruned_state_space.E_y*pruned_state_space.E_y';
if options_mom_.mom.compute_derivs && options_mom_.mom.analytic_standard_errors
if options_mom_.mom.compute_derivs && ( options_mom_.mom.analytic_standard_errors || options_mom_.mom.analytic_jacobian )
dE_yy = pruned_state_space.dVar_y;
for jp=1:totparam_nbr
dE_yy(:,:,jp) = dE_yy(:,:,jp) + pruned_state_space.dE_y(:,jp)*pruned_state_space.E_y' + pruned_state_space.E_y*pruned_state_space.dE_y(:,jp)';
@ -170,7 +181,7 @@ if strcmp(options_mom_.mom.mom_method,'GMM')
end
E_yy_nbr = nnz(tril(options_mom_.mom.index.E_yy));
oo_.mom.model_moments(offset+(1:E_yy_nbr),1) = E_yy(tril(options_mom_.mom.index.E_yy));
if options_mom_.mom.compute_derivs && options_mom_.mom.analytic_standard_errors
if options_mom_.mom.compute_derivs && ( options_mom_.mom.analytic_standard_errors || options_mom_.mom.analytic_jacobian )
oo_.mom.model_moments_params_derivs(offset+(1:E_yy_nbr),:) = reshape(dE_yy(repmat(tril(options_mom_.mom.index.E_yy),[1 1 totparam_nbr])),E_yy_nbr,totparam_nbr);
end
offset = offset + E_yy_nbr;
@ -179,12 +190,12 @@ if strcmp(options_mom_.mom.mom_method,'GMM')
if isfield(options_mom_.mom.index,'E_yyt') && nnz(options_mom_.mom.index.E_yyt) > 0
if options_mom_.prefilter
E_yyt = pruned_state_space.Var_yi;
if options_mom_.mom.compute_derivs && options_mom_.mom.analytic_standard_errors
if options_mom_.mom.compute_derivs && ( options_mom_.mom.analytic_standard_errors || options_mom_.mom.analytic_jacobian )
dE_yyt = pruned_state_space.dVar_yi;
end
else
E_yyt = pruned_state_space.Var_yi + repmat(pruned_state_space.E_y*pruned_state_space.E_y',[1 1 size(pruned_state_space.Var_yi,3)]);
if options_mom_.mom.compute_derivs && options_mom_.mom.analytic_standard_errors
if options_mom_.mom.compute_derivs && ( options_mom_.mom.analytic_standard_errors || options_mom_.mom.analytic_jacobian )
dE_yyt = pruned_state_space.dVar_yi;
for jp=1:totparam_nbr
dE_yyt(:,:,:,jp) = dE_yyt(:,:,:,jp) + repmat(pruned_state_space.dE_y(:,jp)*pruned_state_space.E_y',[1 1 size(pruned_state_space.Var_yi,3)])...
@ -194,7 +205,7 @@ if strcmp(options_mom_.mom.mom_method,'GMM')
end
E_yyt_nbr = nnz(options_mom_.mom.index.E_yyt);
oo_.mom.model_moments(offset+(1:E_yyt_nbr),1) = E_yyt(options_mom_.mom.index.E_yyt);
if options_mom_.mom.compute_derivs && options_mom_.mom.analytic_standard_errors
if options_mom_.mom.compute_derivs && ( options_mom_.mom.analytic_standard_errors || options_mom_.mom.analytic_jacobian )
oo_.mom.model_moments_params_derivs(offset+(1:E_yyt_nbr),:) = reshape(dE_yyt(repmat(options_mom_.mom.index.E_yyt,[1 1 1 totparam_nbr])),E_yyt_nbr,totparam_nbr);
end
end
@ -265,6 +276,30 @@ else
end
end
if options_mom_.mom.compute_derivs && options_mom_.mom.analytic_jacobian
if options_mom_.mom.penalized_estimator
dxparam1 = eye(length(xparam1));
end
for jp=1:length(xparam1)
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.penalized_estimator
df(:,jp)=[dresiduals;dxparam1(:,jp)./sqrt(diag(oo_.prior.variance))];
else
df(:,jp) = dresiduals;
end
else
df(:,jp) = dresiduals'*residuals + residuals'*dresiduals;
if options_mom_.mom.penalized_estimator
df(:,jp)=df(:,jp)+(dxparam1(:,jp))'/oo_.prior.variance*(xparam1-oo_.prior.mean)+(xparam1-oo_.prior.mean)'/oo_.prior.variance*(dxparam1(:,jp));
end
end
end
end
end%main function end

55
matlab/method_of_moments/method_of_moments_objective_function_gradient_helper.m Normal file
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@ -0,0 +1,55 @@
function [out1, out2] = method_of_moments_objective_function_gradient_helper(xparam1, Bounds, oo_, estim_params_, M_, options_mom_)
% [out1, out2] = method_of_moments_objective_function_gradient_helper(xparam1, Bounds, oo_, estim_params_, M_, options_mom_)
% -------------------------------------------------------------------------
% This helper function evaluates the objective function for GMM/SMM estimation and
% outputs the function value fval at first and the gradient df at second place,
% needed for gradient-based optimizers if analytic_jacobian option is set
% =========================================================================
% INPUTS
% o xparam1: current value of estimated parameters as returned by set_prior()
% o Bounds: structure containing parameter bounds
% o oo_: structure for results
% o estim_params_: structure describing the estimated_parameters
% o M_ structure describing the model
% o options_mom_: structure information about all settings (specified by the user, preprocessor, and taken from global options_)
% -------------------------------------------------------------------------
% OUTPUTS: dependent on the optimizer calling this function, see below
% -------------------------------------------------------------------------
% This function calls
% o method_of_moments_objective_function.m
% =========================================================================
% Copyright (C) 2020-2021 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
% -------------------------------------------------------------------------
% This function is called by
% o method_of_moments.m
% o dynare_minimize_objective.m
% -------------------------------------------------------------------------
% This function calls
% o method_of_moments_objective_function
% -------------------------------------------------------------------------
% Author(s):
% o Willi Mutschler (willi@mutschler.eu)
% =========================================================================
[fval, info, exit_flag, junk1, junk2, oo_, M_, options_mom_, df] = method_of_moments_objective_function(xparam1, Bounds, oo_, estim_params_, M_, options_mom_);
% mode_compute=1|3|13 require the gradient as second output argument
out1=fval;
out2=df;
end%main function end

3
matlab/method_of_moments/method_of_moments_standard_errors.m
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@ -25,8 +25,7 @@ function [SE_values, Asympt_Var] = method_of_moments_standard_errors(xparam, obj
% This function calls:
% o get_the_name
% o get_error_message
% o GMM_objective_function
% o SMM_objective_function.m
% o method_of_moments_objective_function
% o method_of_moments_optimal_weighting_matrix
% =========================================================================
% Copyright (C) 2020-2021 Dynare Team

1
tests/Makefile.am
View File

@ -56,6 +56,7 @@ MODFILES = \
estimation/method_of_moments/RBC/RBC_MoM_SMM_ME.mod \
estimation/method_of_moments/RBC/RBC_MoM_prefilter.mod \
estimation/method_of_moments/RBC/RBC_MoM_optimizer.mod \
estimation/method_of_moments/RBC/RBC_MoM_GMM_gradient_optim.mod \
estimation/method_of_moments/AFVRR/AFVRR_M0.mod \
estimation/method_of_moments/AFVRR/AFVRR_MFB.mod \
estimation/method_of_moments/AFVRR/AFVRR_MFB_RRA.mod \

2
tests/estimation/method_of_moments/AFVRR/AFVRR_M0.mod
View File

@ -256,11 +256,13 @@ dev_Q = AndreasenEtAl.Q@{orderApp} - oo_.mom.Q;
dev_datamoments = str2double(AndreasenEtAl.moments@{orderApp}(:,5)) - oo_.mom.data_moments;
dev_modelmoments = str2double(AndreasenEtAl.moments@{orderApp}(:,6)) - oo_.mom.model_moments;
if ~isoctave %there is no table command in Octave
table([AndreasenEtAl.Q@{orderApp} ; str2double(AndreasenEtAl.moments@{orderApp}(:,5)) ; str2double(AndreasenEtAl.moments@{orderApp}(:,6))],...
[oo_.mom.Q ; oo_.mom.data_moments ; oo_.mom.model_moments ],...
[dev_Q ; dev_datamoments ; dev_modelmoments ],...
'VariableNames', {'Andreasen et al', 'Dynare', 'dev'},...
'RowNames', ['Q'; strcat('Data_', M_.matched_moments(:,4)); strcat('Model_', M_.matched_moments(:,4))])
end
if norm(dev_modelmoments)> 1e-4
error('Something wrong in the computation of moments at order @{orderApp}')

2
tests/estimation/method_of_moments/AFVRR/AFVRR_MFB.mod
View File

@ -257,11 +257,13 @@ dev_Q = AndreasenEtAl.Q@{orderApp} - oo_.mom.Q;
dev_datamoments = str2double(AndreasenEtAl.moments@{orderApp}(:,5)) - oo_.mom.data_moments;
dev_modelmoments = str2double(AndreasenEtAl.moments@{orderApp}(:,6)) - oo_.mom.model_moments;
if ~isoctave %there is no table command in Octave
table([AndreasenEtAl.Q@{orderApp} ; str2double(AndreasenEtAl.moments@{orderApp}(:,5)) ; str2double(AndreasenEtAl.moments@{orderApp}(:,6))],...
[oo_.mom.Q ; oo_.mom.data_moments ; oo_.mom.model_moments ],...
[dev_Q ; dev_datamoments ; dev_modelmoments ],...
'VariableNames', {'Andreasen et al', 'Dynare', 'dev'},...
'RowNames', ['Q'; strcat('Data_', M_.matched_moments(:,4)); strcat('Model_', M_.matched_moments(:,4))])
end
if norm(dev_modelmoments)> 1e-4
warning('Something wrong in the computation of moments at order @{orderApp}')

2
tests/estimation/method_of_moments/AFVRR/AFVRR_MFB_RRA.mod
View File

@ -256,11 +256,13 @@ dev_Q = AndreasenEtAl.Q@{orderApp} - oo_.mom.Q;
dev_datamoments = str2double(AndreasenEtAl.moments@{orderApp}(:,5)) - oo_.mom.data_moments;
dev_modelmoments = str2double(AndreasenEtAl.moments@{orderApp}(:,6)) - oo_.mom.model_moments;
if ~isoctave %there is no table command in Octave
table([AndreasenEtAl.Q@{orderApp} ; str2double(AndreasenEtAl.moments@{orderApp}(:,5)) ; str2double(AndreasenEtAl.moments@{orderApp}(:,6))],...
[oo_.mom.Q ; oo_.mom.data_moments ; oo_.mom.model_moments ],...
[dev_Q ; dev_datamoments ; dev_modelmoments ],...
'VariableNames', {'Andreasen et al', 'Dynare', 'dev'},...
'RowNames', ['Q'; strcat('Data_', M_.matched_moments(:,4)); strcat('Model_', M_.matched_moments(:,4))])
end
if norm(dev_modelmoments)> 1e-4
warning('Something wrong in the computation of moments at order @{orderApp}')

125
tests/estimation/method_of_moments/RBC/RBC_MoM_GMM_gradient_optim.mod Normal file
View File

@ -0,0 +1,125 @@
% Test whether gradient-based optimizers are able to use analytical
% Jacobian of moments in GMM estimation
%
% Copyright (C) 2021 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
% =========================================================================
@#include "RBC_MoM_common.inc"
shocks;
var u_a; stderr 0.0072;
end;
varobs c iv n;
%--------------------------------------------------------------------------
% Method of Moments Estimation
%--------------------------------------------------------------------------
matched_moments;
c;
n;
iv;
c*c;
c*iv;
iv*n;
iv*iv;
n*c;
n*n;
c*c(-1);
n*n(-1);
iv*iv(-1);
end;
@#for estimParams in [0, 1, 2]
clear estim_params_;
@#if estimParams == 0
estimated_params;
%DELTA, 0.025;
%BETTA, 0.984;
%B, 0.5;
%ETAc, 2;
ALFA, 0.667;
RHOA, 0.979;
stderr u_a, 0.0072;
end;
@#endif
@#if estimParams == 1
estimated_params;
%DELTA, , 0, 1;
%BETTA, , 0, 1;
%B, , 0, 1;
%ETAc, , 0, 10;
ALFA, , 0, 1;
RHOA, , 0, 1;
stderr u_a, , 0, 1;
end;
@#endif
@#if estimParams == 2
estimated_params;
%DELTA, 0.025, 0, 1, normal_pdf, 0.02, 0.5;
%BETTA, 0.98, 0, 1, beta_pdf, 0.90, 0.25;
%B, 0.45, 0, 1, normal_pdf, 0.40, 0.5;
%ETAl, 1, 0, 10, normal_pdf, 0.25, 0.0.1;
%ETAc, 1.8, 0, 10, normal_pdf, 1.80, 0.5;
ALFA, 0.65, 0, 1, normal_pdf, 0.60, 0.5;
RHOA, 0.95, 0, 1, normal_pdf, 0.90, 0.5;
stderr u_a, 0.01, 0, 1, normal_pdf, 0.01, 0.5;
%THETA, 3.48, 0, 10, normal_pdf, 0.25, 0.0.1;
end;
@#endif
estimated_params_init(use_calibration);
end;
@#for optimizer in [1, 3, 13]
@#if estimParams == 2 && optimizer == 13
%skip due to buggy behavior in Octave
if ~isoctave
@#endif
method_of_moments(
mom_method = GMM % method of moments method; possible values: GMM|SMM
, datafile = 'RBC_Andreasen_Data_2.mat' % name of filename with data
, order = 2 % order of Taylor approximation in perturbation
, weighting_matrix = ['OPTIMAL'] % weighting matrix in moments distance objective function; possible values: OPTIMAL|IDENTITY_MATRIX|DIAGONAL|filename. Size of cell determines stages in iterated estimation, e.g. two state with ['DIAGONAL','OPTIMAL']
, nodisplay
, nograph
, mode_compute = @{optimizer} % specifies the optimizer for minimization of moments distance
%, additional_optimizer_steps = [1 3 13]
% , optim = ('TolFun', 1e-6
% ,'TolX', 1e-6
% ,'MaxIter', 3000
% ,'MaxFunEvals', 1D6
% ,'UseParallel' , 1
% ,'Jacobian' , 'on'
% ,'GradObj','on'
% ) % a list of NAME and VALUE pairs to set options for the optimization routines. Available options depend on mode_compute
, silent_optimizer % run minimization of moments distance silently without displaying results or saving files in between
, analytic_jacobian
);
@#if estimParams == 2 && optimizer == 13
%skip due to buggy behavior in Octave
end
@#endif
@#endfor
@#endfor

11
tests/estimation/method_of_moments/RBC/RBC_MoM_optimizer.mod
View File

@ -118,6 +118,12 @@ options_.solveopt.TolXConstraint=1e-3;
end;
@#for optimizer in OPTIMIZERS
@#if estimParams == 2 && optimizer == 13
%skip due to buggy behavior in Octave
if ~isoctave
@#endif
method_of_moments(
mom_method = GMM % method of moments method; possible values: GMM|SMM
, datafile = 'RBC_Andreasen_Data_2.mat' % name of filename with data
@ -139,6 +145,11 @@ options_.solveopt.TolXConstraint=1e-3;
@#endif
%, silent_optimizer % run minimization of moments distance silently without displaying results or saving files in between
);
@#if estimParams == 2 && optimizer == 13
%skip due to buggy behavior in Octave
end
@#endif
@#endfor
@#endfor