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Merge branch 'MoM_testsuite' into 'master' 

Method of Moments: Updates to testsuite

See merge request Dynare/dynare!1799
time-shift
Sébastien Villemot 2021-01-07 19:52:13 +00:00
parent be16f29254 b3e3501a6d
commit 4434edae0b
25 changed files with 2199 additions and 498 deletions
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109
matlab/method_of_moments/method_of_moments.m
View File

@ -63,7 +63,7 @@ function [oo_, options_mom_, M_] = method_of_moments(bayestopt_, options_, oo_,
% o set_all_parameters.m
% o test_for_deep_parameters_calibration.m
% =========================================================================
% Copyright (C) 2020 Dynare Team
% Copyright (C) 2020-2021 Dynare Team
%
% This file is part of Dynare.
%
@ -92,6 +92,7 @@ function [oo_, options_mom_, M_] = method_of_moments(bayestopt_, options_, oo_,
% - [ ] SMM with extended path
% - [ ] 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
% -------------------------------------------------------------------------
% Step 0: Check if required structures and options exist
% -------------------------------------------------------------------------
@ -133,9 +134,9 @@ if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_meth
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'; 'DIAGONAL'}); % weighting matrix in moments distance objective function at each iteration of estimation; cell of strings with
options_mom_.mom = set_default_option(options_mom_.mom,'weighting_matrix',{'DIAGONAL'; 'DIAGONAL'}); % 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
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
@ -169,6 +170,7 @@ options_mom_.mom.compute_derivs = false;% flag to compute derivs in objective fu
% 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)
@ -200,7 +202,7 @@ options_mom_ = set_default_option(options_mom_,'optim_opt',[]);
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,'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)
@ -738,11 +740,6 @@ catch last_error% if check fails, provide info on using calibration if present
rethrow(last_error);
end
if options_mom_.mode_compute == 0 %We only report value of moments distance at initial value of the parameters
fprintf('No minimization of moments distance due to ''mode_compute=0''\n')
return
end
% -------------------------------------------------------------------------
% Step 7a: Method of moments estimation: print some info
% -------------------------------------------------------------------------
@ -760,27 +757,56 @@ end
if options_mom_.mom.penalized_estimator
fprintf('\n - penalized estimation using deviation from prior mean and weighted with prior precision');
end
if options_mom_.mode_compute == 1; fprintf('\n - optimizer (mode_compute=1): fmincon');
elseif options_mom_.mode_compute == 2; fprintf('\n - optimizer (mode_compute=2): continuous simulated annealing');
elseif options_mom_.mode_compute == 3; fprintf('\n - optimizer (mode_compute=3): fminunc');
elseif options_mom_.mode_compute == 4; fprintf('\n - optimizer (mode_compute=4): csminwel');
elseif options_mom_.mode_compute == 5; fprintf('\n - optimizer (mode_compute=5): newrat');
elseif options_mom_.mode_compute == 6; fprintf('\n - optimizer (mode_compute=6): gmhmaxlik');
elseif options_mom_.mode_compute == 7; fprintf('\n - optimizer (mode_compute=7): fminsearch');
elseif options_mom_.mode_compute == 8; fprintf('\n - optimizer (mode_compute=8): Dynare Nelder-Mead simplex');
elseif options_mom_.mode_compute == 9; fprintf('\n - optimizer (mode_compute=9): CMA-ES');
elseif options_mom_.mode_compute == 10; fprintf('\n - optimizer (mode_compute=10): simpsa');
elseif options_mom_.mode_compute == 11; fprintf('\n - optimizer (mode_compute=11): online_auxiliary_filter');
elseif options_mom_.mode_compute == 12; fprintf('\n - optimizer (mode_compute=12): particleswarm');
elseif options_mom_.mode_compute == 101; fprintf('\n - optimizer (mode_compute=101): SolveOpt');
elseif options_mom_.mode_compute == 102; fprintf('\n - optimizer (mode_compute=102): simulannealbnd');
elseif options_mom_.mode_compute == 13; fprintf('\n - optimizer (mode_compute=13): lsqnonlin');
elseif ischar(minimizer_algorithm); fprintf(['\n - user-defined optimizer: ' minimizer_algorithm]);
else
error('method_of_moments: Unknown optimizer, please contact the developers ')
end
if options_mom_.silent_optimizer
fprintf(' (silent)');
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';
else
str = ' (additional_optimizer_steps';
end
switch optimizer_vec{i}
case 0
fprintf('\n %s=0): no minimization',str);
case 1
fprintf('\n %s=1): fmincon',str);
case 2
fprintf('\n %s=2): continuous simulated annealing',str);
case 3
fprintf('\n %s=3): fminunc',str);
case 4
fprintf('\n %s=4): csminwel',str);
case 5
fprintf('\n %s=5): newrat',str);
case 6
fprintf('\n %s=6): gmhmaxlik',str);
case 7
fprintf('\n %s=7): fminsearch',str);
case 8
fprintf('\n %s=8): Dynare Nelder-Mead simplex',str);
case 9
fprintf('\n %s=9): CMA-ES',str);
case 10
fprintf('\n %s=10): simpsa',str);
case 11
fprintf('\n %s=11): online_auxiliary_filter',str);
case 12
fprintf('\n %s=12): particleswarm',str);
case 101
fprintf('\n %s=101): SolveOpt',str);
case 102
fprintf('\n %s=102): simulannealbnd',str);
case 13
fprintf('\n %s=13): lsqnonlin',str);
otherwise
if ischar(optimizer_vec{i})
fprintf('\n %s=%s): user-defined',str,optimizer_vec{i});
else
error('method_of_moments: Unknown optimizer, please contact the developers ')
end
end
if options_mom_.silent_optimizer
fprintf(' (silent)');
end
end
fprintf('\n - perturbation order: %d', options_mom_.order)
if options_mom_.order > 1 && options_mom_.pruning
@ -802,8 +828,6 @@ 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
optimizer_vec=[options_mom_.mode_compute,options_mom_.additional_optimizer_steps]; % at each stage one can possibly use different optimizers sequentially
for stage_iter=1:size(options_mom_.mom.weighting_matrix,1)
fprintf('Estimation stage %u\n',stage_iter);
Woptflag = false;
@ -849,15 +873,20 @@ for stage_iter=1:size(options_mom_.mom.weighting_matrix,1)
end
for optim_iter= 1:length(optimizer_vec)
if optimizer_vec(optim_iter)==13
options_mom_.vector_output = true;
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
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, [],...
Bounds, oo_, estim_params_, M_, options_mom_);
if options_mom_.vector_output
fval = fval'*fval;
if optimizer_vec{optim_iter}==13
options_mom_.vector_output = true;
else
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, [],...
Bounds, oo_, estim_params_, M_, options_mom_);
if options_mom_.vector_output
fval = fval'*fval;
end
end
fprintf('\nStage %d Iteration %d: value of minimized moment distance objective function: %12.10f.\n',stage_iter,optim_iter,fval)
if options_mom_.mom.verbose

2
matlab/method_of_moments/method_of_moments_check_plot.m
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@ -2,7 +2,7 @@ function method_of_moments_check_plot(fun,xparam,SE_vec,options_,M_,estim_params
% Checks the estimated local minimum of the moment's distance objective
% Copyright (C) 2020 Dynare Team
% Copyright (C) 2020-2021 Dynare Team
%
% This file is part of Dynare.
%

2
matlab/method_of_moments/method_of_moments_data_moments.m
View File

@ -16,7 +16,7 @@ function [dataMoments, m_data] = method_of_moments_data_moments(data, oo_, match
% o method_of_moments.m
% o method_of_moments_objective_function.m
% =========================================================================
% Copyright (C) 2020 Dynare Team
% Copyright (C) 2020-2021 Dynare Team
%
% This file is part of Dynare.
%

4
matlab/method_of_moments/method_of_moments_objective_function.m
View File

@ -31,7 +31,7 @@ function [fval, info, exit_flag, junk1, junk2, oo_, M_, options_mom_] = method_o
% o resol
% o set_all_parameters
% =========================================================================
% Copyright (C) 2020 Dynare Team
% Copyright (C) 2020-2021 Dynare Team
%
% This file is part of Dynare.
%
@ -232,7 +232,7 @@ elseif strcmp(options_mom_.mom.mom_method,'SMM')
i_ME = setdiff([1:size(M_.H,1)],find(diag(M_.H) == 0)); % find ME with 0 variance
chol_S = chol(M_.H(i_ME,i_ME)); %decompose rest
shock_mat=zeros(size(options_mom_.mom.ME_shock_series)); %initialize
shock_mat(:,i_ME)=options_mom_.mom.ME_shock_series(:,i_exo_var)*chol_S;
shock_mat(:,i_ME)=options_mom_.mom.ME_shock_series(:,i_ME)*chol_S;
y_sim = y_sim+shock_mat;
end

2
matlab/method_of_moments/method_of_moments_optimal_weighting_matrix.m
View File

@ -19,7 +19,7 @@ function W_opt = method_of_moments_optimal_weighting_matrix(m_data, moments, q_l
% This function calls:
% o CorrMatrix (embedded)
% =========================================================================
% Copyright (C) 2020 Dynare Team
% Copyright (C) 2020-2021 Dynare Team
%
% This file is part of Dynare.
%

2
matlab/method_of_moments/method_of_moments_standard_errors.m
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@ -29,7 +29,7 @@ function [SE_values, Asympt_Var] = method_of_moments_standard_errors(xparam, obj
% o SMM_objective_function.m
% o method_of_moments_optimal_weighting_matrix
% =========================================================================
% Copyright (C) 2020 Dynare Team
% Copyright (C) 2020-2021 Dynare Team
%
% This file is part of Dynare.
%

6
tests/.gitignore vendored
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@ -50,8 +50,10 @@ wsOct
!/ep/mean_preserving_spread.m
!/ep/rbcii_steady_state.m
!/estimation/fsdat_simul.m
!/estimation/method_of_moments/RBC_MoM_steady_helper.m
!/estimation/method_of_moments/RBC_Andreasen_Data_2.mat
!/estimation/method_of_moments/RBC/RBC_MoM_steady_helper.m
!/estimation/method_of_moments/RBC/RBC_Andreasen_Data_2.mat
!/estimation/method_of_moments/AFVRR/AFVRR_data.mat
!/estimation/method_of_moments/AFVRR/AFVRR_steady_helper.m
!/expectations/expectation_ss_old_steadystate.m
!/external_function/extFunDeriv.m
!/external_function/extFunNoDerivs.m

22
tests/Makefile.am
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@ -50,10 +50,14 @@ MODFILES = \
estimation/MH_recover/fs2000_recover_3.mod \
estimation/t_proposal/fs2000_student.mod \
estimation/tune_mh_jscale/fs2000.mod \
estimation/method_of_moments/AnScho_MoM.mod \
estimation/method_of_moments/RBC_MoM_Andreasen.mod \
estimation/method_of_moments/RBC_MoM_SMM_ME.mod \
estimation/method_of_moments/RBC_MoM_prefilter.mod \
estimation/method_of_moments/AnScho/AnScho_MoM.mod \
estimation/method_of_moments/RBC/RBC_MoM_Andreasen.mod \
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/AFVRR/AFVRR_M0.mod \
estimation/method_of_moments/AFVRR/AFVRR_MFB.mod \
estimation/method_of_moments/AFVRR/AFVRR_MFB_RRA.mod \
moments/example1_var_decomp.mod \
moments/example1_bp_test.mod \
moments/test_AR1_spectral_density.mod \
@ -835,6 +839,10 @@ particle: m/particle o/particle
m/particle: $(patsubst %.mod, %.m.trs, $(PARTICLEFILES))
o/particle: $(patsubst %.mod, %.o.trs, $(PARTICLEFILES))
method_of_moments: m/method_of_moments o/method_of_moments
m/method_of_moments: $(patsubst %.mod, %.m.trs, $(filter estimation/method_of_moments/%.mod, $(MODFILES)))
o/method_of_moments: $(patsubst %.mod, %.o.trs, $(filter estimation/method_of_moments/%.mod, $(MODFILES)))
# Matlab TRS Files
M_TRS_FILES = $(patsubst %.mod, %.m.trs, $(MODFILES))
M_TRS_FILES += run_block_byte_tests_matlab.m.trs \
@ -984,8 +992,10 @@ EXTRA_DIST = \
lmmcp/sw-common-header.inc \
lmmcp/sw-common-footer.inc \
estimation/tune_mh_jscale/fs2000.inc \
estimation/method_of_moments/RBC_MoM_common.inc \
estimation/method_of_moments/RBC_MoM_steady_helper.m \
estimation/method_of_moments/RBC/RBC_MoM_common.inc \
estimation/method_of_moments/RBC/RBC_MoM_steady_helper.m \
estimation/method_of_moments/AFVRR/AFVRR_common.inc \
estimation/method_of_moments/AFVRR/AFVRR_steady_helper.m \
histval_initval_file_unit_tests.m \
histval_initval_file/my_assert.m \
histval_initval_file/ramst_data.xls \

299
tests/estimation/method_of_moments/AFVRR/AFVRR_M0.mod Normal file
View File

@ -0,0 +1,299 @@
% DSGE model based on replication files of
% Andreasen, Fernandez-Villaverde, Rubio-Ramirez (2018), The Pruned State-Space System for Non-Linear DSGE Models: Theory and Empirical Applications, Review of Economic Studies, 85, p. 1-49
% Adapted for Dynare by Willi Mutschler (@wmutschl, willi@mutschler.eu), Jan 2021
% =========================================================================
% 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/>.
% =========================================================================
% This is the benchmark model with no feedback M_0
% Original code RunGMM_standardModel_RRA.m by Martin M. Andreasen, Jan 2016
@#include "AFVRR_common.inc"
%--------------------------------------------------------------------------
% Parameter calibration taken from RunGMM_standardModel_RRA.m
%--------------------------------------------------------------------------
% fixed parameters
INHABIT = 1;
PHI1 = 4;
PHI4 = 1;
KAPAone = 0;
DELTA = 0.025;
THETA = 0.36;
ETA = 6;
CHI = 0;
CONSxhr40 = 0;
BETTAxhr = 0;
BETTAxhr40= 0;
RHOD = 0;
GAMA = 0.9999;
CONSxhr20 = 0;
% estimated parameters
BETTA = 0.999544966118000;
B = 0.668859504661000;
H = 0.342483445196000;
PHI2 = 0.997924964981000;
RRA = 662.7953149595370;
KAPAtwo = 5.516226495551000;
ALFA = 0.809462321180000;
RHOR = 0.643873352513000;
BETTAPAI = 1.270087844103000;
BETTAY = 0.031812764291000;
MYYPS = 1.001189151180000;
MYZ = 1.005286347928000;
RHOA = 0.743239127127000;
RHOG = 0.793929380230000;
PAI = 1.012163659169000;
GoY = 0.206594858866000;
STDA = 0.016586292524000;
STDG = 0.041220613851000;
STDD = 0.013534473123000;
% endogenous parameters set via steady state, no need to initialize
%PHIzero = ;
%AA = ;
%PHI3 = ;
%negVf = ;
model_diagnostics;
% Model diagnostics show that some parameters are endogenously determined
% via the steady state, so we run steady to calibrate all parameters
steady;
model_diagnostics;
% Now all parameters are determined
resid;
check;
%--------------------------------------------------------------------------
% Shock distribution
%--------------------------------------------------------------------------
shocks;
var eps_a = STDA^2;
var eps_d = STDD^2;
var eps_g = STDG^2;
end;
%--------------------------------------------------------------------------
% Estimated Params block - these parameters will be estimated, we
% initialize at calibrated values
%--------------------------------------------------------------------------
estimated_params;
BETTA;
B;
H;
PHI2;
RRA;
KAPAtwo;
ALFA;
RHOR;
BETTAPAI;
BETTAY;
MYYPS;
MYZ;
RHOA;
RHOG;
PAI;
GoY;
stderr eps_a;
stderr eps_g;
stderr eps_d;
end;
estimated_params_init(use_calibration);
end;
%--------------------------------------------------------------------------
% Compare whether toolbox yields equivalent moments at second order
%--------------------------------------------------------------------------
% Note that we compare results for orderApp=1|2 and not for orderApp=3, because
% there is a small error in the replication files of the original article in the
% computation of the covariance matrix of the extended innovations vector.
% The authors have been contacted, fixed it, and report that the results
% change only slightly at orderApp=3 to what they report in the paper. At
% orderApp=2 all is correct and so the following part tests whether we get
% the same model moments at the calibrated parameters (we do not optimize).
% We compare it to the replication file RunGMM_standardModel_RRA.m with the
% following settings: orderApp=1|2, seOn=0, q_lag=10, weighting=1;
% scaled=0; optimizer=0; estimator=1; momentSet=2;
%
% Output of the replication files for orderApp=1
AndreasenEtAl.Q1 = 23893.072;
AndreasenEtAl.moments1 =[ % note that we reshuffeled to be compatible with our matched moments block
{[ 1]} {'Ex' } {'Gr_C '} {' ' } {'0.024388' } {'0.023764' }
{[ 2]} {'Ex' } {'Gr_I '} {' ' } {'0.031046' } {'0.028517' }
{[ 3]} {'Ex' } {'Infl ' } {' ' } {'0.03757' } {'0.048361' }
{[ 4]} {'Ex' } {'r1 ' } {' ' } {'0.056048' } {'0.073945' }
{[ 5]} {'Ex' } {'r40 ' } {' ' } {'0.069929' } {'0.073945' }
{[ 6]} {'Ex' } {'xhr40 '} {' ' } {'0.017237' } {'0' }
{[ 7]} {'Ex' } {'GoY '} {' ' } {'-1.5745' } {'-1.577' }
{[ 8]} {'Ex' } {'hours '} {' ' } {'-0.043353' } {'-0.042861' }
{[ 9]} {'Exx' } {'Gr_C '} {'Gr_C '} {'0.0013159' } {'0.0011816' }
{[17]} {'Exx' } {'Gr_C '} {'Gr_I '} {'0.0021789' } {'0.0016052' }
{[18]} {'Exx' } {'Gr_C '} {'Infl ' } {'0.00067495' } {'0.00090947' }
{[19]} {'Exx' } {'Gr_C '} {'r1 ' } {'0.0011655' } {'0.0016016' }
{[20]} {'Exx' } {'Gr_C '} {'r40 ' } {'0.0015906' } {'0.0017076' }
{[21]} {'Exx' } {'Gr_C '} {'xhr40 '} {'0.0020911' } {'0.0013997' }
{[10]} {'Exx' } {'Gr_I '} {'Gr_I '} {'0.0089104' } {'0.0055317' }
{[22]} {'Exx' } {'Gr_I '} {'Infl ' } {'0.00063139' } {'0.00050106' }
{[23]} {'Exx' } {'Gr_I '} {'r1 ' } {'0.0011031' } {'0.0018178' }
{[24]} {'Exx' } {'Gr_I '} {'r40 ' } {'0.0018445' } {'0.0020186' }
{[25]} {'Exx' } {'Gr_I '} {'xhr40 '} {'0.00095556' } {'0.0064471' }
{[11]} {'Exx' } {'Infl ' } {'Infl ' } {'0.0020268' } {'0.0030519' }
{[26]} {'Exx' } {'Infl ' } {'r1 ' } {'0.0025263' } {'0.0042181' }
{[27]} {'Exx' } {'Infl ' } {'r40 ' } {'0.0029126' } {'0.0039217' }
{[28]} {'Exx' } {'Infl ' } {'xhr40 '} {'-0.00077101'} {'-0.0019975' }
{[12]} {'Exx' } {'r1 ' } {'r1 ' } {'0.0038708' } {'0.0061403' }
{[29]} {'Exx' } {'r1 ' } {'r40 ' } {'0.0044773' } {'0.0058343' }
{[30]} {'Exx' } {'r1 ' } {'xhr40 '} {'-0.00048202'} {'-0.00089501'}
{[13]} {'Exx' } {'r40 ' } {'r40 ' } {'0.0054664' } {'0.0056883' }
{[31]} {'Exx' } {'r40 ' } {'xhr40 '} {'0.00053864' } {'-0.00041184'}
{[14]} {'Exx' } {'xhr40 '} {'xhr40 '} {'0.053097' } {'0.016255' }
{[15]} {'Exx' } {'GoY '} {'GoY '} {'2.4863' } {'2.4919' }
{[16]} {'Exx' } {'hours '} {'hours '} {'0.0018799' } {'0.0018384' }
{[32]} {'Exx1'} {'Gr_C '} {'Gr_C '} {'0.00077917' } {'0.00065543' }
{[33]} {'Exx1'} {'Gr_I '} {'Gr_I '} {'0.0050104' } {'0.0033626' }
{[34]} {'Exx1'} {'Infl ' } {'Infl ' } {'0.0019503' } {'0.0029033' }
{[35]} {'Exx1'} {'r1 ' } {'r1 ' } {'0.0038509' } {'0.006112' }
{[36]} {'Exx1'} {'r40 ' } {'r40 ' } {'0.0054699' } {'0.005683' }
{[37]} {'Exx1'} {'xhr40 '} {'xhr40 '} {'-0.00098295'} {'3.3307e-16' }
{[38]} {'Exx1'} {'GoY '} {'GoY '} {'2.4868' } {'2.4912' }
{[39]} {'Exx1'} {'hours '} {'hours '} {'0.0018799' } {'0.0018378' }
];
% Output of the replication files for orderApp=2
AndreasenEtAl.Q2 = 65.8269;
AndreasenEtAl.moments2 =[ % note that we reshuffeled to be compatible with our matched moments block
{[ 1]} {'Ex' } {'Gr_C '} {' ' } {'0.024388' } {'0.023764' }
{[ 2]} {'Ex' } {'Gr_I '} {' ' } {'0.031046' } {'0.028517' }
{[ 3]} {'Ex' } {'Infl ' } {' ' } {'0.03757' } {'0.034882' }
{[ 4]} {'Ex' } {'r1 ' } {' ' } {'0.056048' } {'0.056542' }
{[ 5]} {'Ex' } {'r40 ' } {' ' } {'0.069929' } {'0.070145' }
{[ 6]} {'Ex' } {'xhr40 '} {' ' } {'0.017237' } {'0.020825' }
{[ 7]} {'Ex' } {'GoY '} {' ' } {'-1.5745' } {'-1.5748' }
{[ 8]} {'Ex' } {'hours '} {' ' } {'-0.043353' } {'-0.04335' }
{[ 9]} {'Exx' } {'Gr_C '} {'Gr_C '} {'0.0013159' } {'0.001205' }
{[17]} {'Exx' } {'Gr_C '} {'Gr_I '} {'0.0021789' } {'0.0016067' }
{[18]} {'Exx' } {'Gr_C '} {'Infl ' } {'0.00067495' } {'0.00059406'}
{[19]} {'Exx' } {'Gr_C '} {'r1 ' } {'0.0011655' } {'0.0011949' }
{[20]} {'Exx' } {'Gr_C '} {'r40 ' } {'0.0015906' } {'0.0016104' }
{[21]} {'Exx' } {'Gr_C '} {'xhr40 '} {'0.0020911' } {'0.0020245' }
{[10]} {'Exx' } {'Gr_I '} {'Gr_I '} {'0.0089104' } {'0.0060254' }
{[22]} {'Exx' } {'Gr_I '} {'Infl ' } {'0.00063139' } {'8.3563e-05'}
{[23]} {'Exx' } {'Gr_I '} {'r1 ' } {'0.0011031' } {'0.0013176' }
{[24]} {'Exx' } {'Gr_I '} {'r40 ' } {'0.0018445' } {'0.0019042' }
{[25]} {'Exx' } {'Gr_I '} {'xhr40 '} {'0.00095556' } {'0.0064261' }
{[11]} {'Exx' } {'Infl ' } {'Infl ' } {'0.0020268' } {'0.0020735' }
{[26]} {'Exx' } {'Infl ' } {'r1 ' } {'0.0025263' } {'0.0027621' }
{[27]} {'Exx' } {'Infl ' } {'r40 ' } {'0.0029126' } {'0.0029257' }
{[28]} {'Exx' } {'Infl ' } {'xhr40 '} {'-0.00077101'} {'-0.0012165'}
{[12]} {'Exx' } {'r1 ' } {'r1 ' } {'0.0038708' } {'0.0040235' }
{[29]} {'Exx' } {'r1 ' } {'r40 ' } {'0.0044773' } {'0.0044702' }
{[30]} {'Exx' } {'r1 ' } {'xhr40 '} {'-0.00048202'} {'0.00030542'}
{[13]} {'Exx' } {'r40 ' } {'r40 ' } {'0.0054664' } {'0.0052718' }
{[31]} {'Exx' } {'r40 ' } {'xhr40 '} {'0.00053864' } {'0.0010045' }
{[14]} {'Exx' } {'xhr40 '} {'xhr40 '} {'0.053097' } {'0.018416' }
{[15]} {'Exx' } {'GoY '} {'GoY '} {'2.4863' } {'2.4853' }
{[16]} {'Exx' } {'hours '} {'hours '} {'0.0018799' } {'0.0018806' }
{[32]} {'Exx1'} {'Gr_C '} {'Gr_C '} {'0.00077917' } {'0.00067309'}
{[33]} {'Exx1'} {'Gr_I '} {'Gr_I '} {'0.0050104' } {'0.0033293' }
{[34]} {'Exx1'} {'Infl ' } {'Infl ' } {'0.0019503' } {'0.0019223' }
{[35]} {'Exx1'} {'r1 ' } {'r1 ' } {'0.0038509' } {'0.0039949' }
{[36]} {'Exx1'} {'r40 ' } {'r40 ' } {'0.0054699' } {'0.0052659' }
{[37]} {'Exx1'} {'xhr40 '} {'xhr40 '} {'-0.00098295'} {'0.0004337' }
{[38]} {'Exx1'} {'GoY '} {'GoY '} {'2.4868' } {'2.4846' }
{[39]} {'Exx1'} {'hours '} {'hours '} {'0.0018799' } {'0.00188' }
];
@#for orderApp in 1:2
method_of_moments(
mom_method = GMM % method of moments method; possible values: GMM|SMM
, datafile = 'AFVRR_data.mat' % name of filename with data
, bartlett_kernel_lag = 10 % bandwith in optimal weighting matrix
, order = @{orderApp} % order of Taylor approximation in perturbation
, pruning % use pruned state space system at higher-order
% , verbose % display and store intermediate estimation results
, weighting_matrix = ['DIAGONAL'] % weighting matrix in moments distance objective function; possible values: OPTIMAL|IDENTITY_MATRIX|DIAGONAL|filename
% , TeX % print TeX tables and graphics
% Optimization options that can be set by the user in the mod file, otherwise default values are provided
%, huge_number=1D10 % value for replacing the infinite bounds on parameters by finite numbers. Used by some optimizers for numerical reasons
, mode_compute = 0 % specifies the optimizer for minimization of moments distance, note that by default there is a new optimizer
, optim = ('TolFun', 1e-6
,'TolX', 1e-6
,'MaxIter', 3000
,'MaxFunEvals', 1D6
,'UseParallel' , 1
%,'Jacobian' , '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_standard_errors
, se_tolx=1e-10
);
% Check results
fprintf('****************************************************************\n')
fprintf('Compare Results for perturbation order @{orderApp}\n')
fprintf('****************************************************************\n')
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;
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))])
if norm(dev_modelmoments)> 1e-4
error('Something wrong in the computation of moments at order @{orderApp}')
end
@#endfor
%--------------------------------------------------------------------------
% Replicate estimation at orderApp=3
%--------------------------------------------------------------------------
@#ifdef DoEstimation
method_of_moments(
mom_method = GMM % method of moments method; possible values: GMM|SMM
, datafile = 'AFVRR_data.mat' % name of filename with data
, bartlett_kernel_lag = 10 % bandwith in optimal weighting matrix
, order = 3 % order of Taylor approximation in perturbation
, pruning % use pruned state space system at higher-order
% , verbose % display and store intermediate estimation results
, weighting_matrix = ['DIAGONAL', 'OPTIMAL'] % weighting matrix in moments distance objective function; possible values: OPTIMAL|IDENTITY_MATRIX|DIAGONAL|filename
% , TeX % print TeX tables and graphics
% Optimization options that can be set by the user in the mod file, otherwise default values are provided
%, huge_number=1D10 % value for replacing the infinite bounds on parameters by finite numbers. Used by some optimizers for numerical reasons
, mode_compute = 13 % specifies the optimizer for minimization of moments distance, note that by default there is a new optimizer
, additional_optimizer_steps = [13]
, optim = ('TolFun', 1e-6
,'TolX', 1e-6
,'MaxIter', 3000
,'MaxFunEvals', 1D6
,'UseParallel' , 1
%,'Jacobian' , '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_standard_errors
, se_tolx=1e-10
);
@#endif

300
tests/estimation/method_of_moments/AFVRR/AFVRR_MFB.mod Normal file
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@ -0,0 +1,300 @@
% DSGE model based on replication files of
% Andreasen, Fernandez-Villaverde, Rubio-Ramirez (2018), The Pruned State-Space System for Non-Linear DSGE Models: Theory and Empirical Applications, Review of Economic Studies, 85, p. 1-49
% Adapted for Dynare by Willi Mutschler (@wmutschl, willi@mutschler.eu), Jan 2021
% =========================================================================
% 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/>.
% =========================================================================
% This is the model with Feedback M_FB
% Original code RunGMM_Feedback_estim_RRA.m by Martin M. Andreasen, Jan 2016
@#include "AFVRR_common.inc"
%--------------------------------------------------------------------------
% Parameter calibration taken from RunGMM_Feedback_estim_RRA.m
%--------------------------------------------------------------------------
% fixed parameters
INHABIT = 1;
PHI1 = 4;
PHI4 = 1;
KAPAone = 0;
DELTA = 0.025;
THETA = 0.36;
ETA = 6;
CHI = 0;
BETTAxhr = 0;
BETTAxhr40= 0;
RHOD = 0;
GAMA = 0.9999;
CONSxhr20 = 0;
% estimated parameters
BETTA = 0.997007023687000;
B = 0.692501768577000;
H = 0.339214495653000;
PHI2 = 0.688555040951000;
RRA = 24.346514272871001;
KAPAtwo = 10.018421876923000;
ALFA = 0.792507553312000;
RHOR = 0.849194030384000;
BETTAPAI = 2.060579322980000;
BETTAY = 0.220573712342000;
MYYPS = 1.001016690133000;
MYZ = 1.005356313981000;
RHOA = 0.784141391843000;
RHOG = 0.816924540497000;
PAI = 1.011924196487000;
CONSxhr40 = 0.878774662208000;
GoY = 0.207110300602000;
STDA = 0.013024450606000;
STDG = 0.051049871928000;
STDD = 0.008877423780000;
% endogenous parameters set via steady state, no need to initialize
%PHIzero = ;
%AA = ;
%PHI3 = ;
%negVf = ;
model_diagnostics;
% Model diagnostics show that some parameters are endogenously determined
% via the steady state, so we run steady to calibrate all parameters
steady;
model_diagnostics;
% Now all parameters are determined
resid;
check;
%--------------------------------------------------------------------------
% Shock distribution
%--------------------------------------------------------------------------
shocks;
var eps_a = STDA^2;
var eps_d = STDD^2;
var eps_g = STDG^2;
end;
%--------------------------------------------------------------------------
% Estimated Params block - these parameters will be estimated, we
% initialize at calibrated values
%--------------------------------------------------------------------------
estimated_params;
BETTA;
B;
H;
PHI2;
RRA;
KAPAtwo;
ALFA;
RHOR;
BETTAPAI;
BETTAY;
MYYPS;
MYZ;
RHOA;
RHOG;
PAI;
CONSxhr40;
GoY;
stderr eps_a;
stderr eps_g;
stderr eps_d;
end;
estimated_params_init(use_calibration);
end;
%--------------------------------------------------------------------------
% Compare whether toolbox yields equivalent moments at second order
%--------------------------------------------------------------------------
% Note that we compare results for orderApp=1|2 and not for orderApp=3, because
% there is a small error in the replication files of the original article in the
% computation of the covariance matrix of the extended innovations vector.
% The authors have been contacted, fixed it, and report that the results
% change only slightly at orderApp=3 to what they report in the paper. At
% orderApp=2 all is correct and so the following part tests whether we get
% the same model moments at the calibrated parameters (we do not optimize).
% We compare it to the replication file RunGMM_Feedback_estim_RRA.m with the
% following settings: orderApp=1|2, seOn=0, q_lag=10, weighting=1;
% scaled=0; optimizer=0; estimator=1; momentSet=2;
%
% Output of the replication files for orderApp=1
AndreasenEtAl.Q1 = 201778.9697;
AndreasenEtAl.moments1 =[ % note that we reshuffeled to be compatible with our matched moments block
{[ 1]} {'Ex' } {'Gr_C '} {' ' } {'0.024388' } {'0.023654' }
{[ 2]} {'Ex' } {'Gr_I '} {' ' } {'0.031046' } {'0.027719' }
{[ 3]} {'Ex' } {'Infl ' } {' ' } {'0.03757' } {'0.047415' }
{[ 4]} {'Ex' } {'r1 ' } {' ' } {'0.056048' } {'0.083059' }
{[ 5]} {'Ex' } {'r40 ' } {' ' } {'0.069929' } {'0.083059' }
{[ 6]} {'Ex' } {'xhr40 '} {' ' } {'0.017237' } {'0' }
{[ 7]} {'Ex' } {'GoY '} {' ' } {'-1.5745' } {'-1.5745' }
{[ 8]} {'Ex' } {'hours '} {' ' } {'-0.043353' } {'-0.043245' }
{[ 9]} {'Exx' } {'Gr_C '} {'Gr_C '} {'0.0013159' } {'0.0012253' }
{[17]} {'Exx' } {'Gr_C '} {'Gr_I '} {'0.0021789' } {'0.0015117' }
{[18]} {'Exx' } {'Gr_C '} {'Infl ' } {'0.00067495' } {'0.00080078' }
{[19]} {'Exx' } {'Gr_C '} {'r1 ' } {'0.0011655' } {'0.00182' }
{[20]} {'Exx' } {'Gr_C '} {'r40 ' } {'0.0015906' } {'0.001913' }
{[21]} {'Exx' } {'Gr_C '} {'xhr40 '} {'0.0020911' } {'0.0016326' }
{[10]} {'Exx' } {'Gr_I '} {'Gr_I '} {'0.0089104' } {'0.0040112' }
{[22]} {'Exx' } {'Gr_I '} {'Infl ' } {'0.00063139' } {'0.00060604' }
{[23]} {'Exx' } {'Gr_I '} {'r1 ' } {'0.0011031' } {'0.0021426' }
{[24]} {'Exx' } {'Gr_I '} {'r40 ' } {'0.0018445' } {'0.0022348' }
{[25]} {'Exx' } {'Gr_I '} {'xhr40 '} {'0.00095556' } {'0.0039852' }
{[11]} {'Exx' } {'Infl ' } {'Infl ' } {'0.0020268' } {'0.0030058' }
{[26]} {'Exx' } {'Infl ' } {'r1 ' } {'0.0025263' } {'0.0044951' }
{[27]} {'Exx' } {'Infl ' } {'r40 ' } {'0.0029126' } {'0.0042225' }
{[28]} {'Exx' } {'Infl ' } {'xhr40 '} {'-0.00077101'} {'-0.0021222' }
{[12]} {'Exx' } {'r1 ' } {'r1 ' } {'0.0038708' } {'0.0074776' }
{[29]} {'Exx' } {'r1 ' } {'r40 ' } {'0.0044773' } {'0.0071906' }
{[30]} {'Exx' } {'r1 ' } {'xhr40 '} {'-0.00048202'} {'-0.0006736' }
{[13]} {'Exx' } {'r40 ' } {'r40 ' } {'0.0054664' } {'0.0070599' }
{[31]} {'Exx' } {'r40 ' } {'xhr40 '} {'0.00053864' } {'-0.00036735'}
{[14]} {'Exx' } {'xhr40 '} {'xhr40 '} {'0.053097' } {'0.014516' }
{[15]} {'Exx' } {'GoY '} {'GoY '} {'2.4863' } {'2.4866' }
{[16]} {'Exx' } {'hours '} {'hours '} {'0.0018799' } {'0.0018713' }
{[32]} {'Exx1'} {'Gr_C '} {'Gr_C '} {'0.00077917' } {'0.00076856' }
{[33]} {'Exx1'} {'Gr_I '} {'Gr_I '} {'0.0050104' } {'0.002163' }
{[34]} {'Exx1'} {'Infl ' } {'Infl ' } {'0.0019503' } {'0.0028078' }
{[35]} {'Exx1'} {'r1 ' } {'r1 ' } {'0.0038509' } {'0.0074583' }
{[36]} {'Exx1'} {'r40 ' } {'r40 ' } {'0.0054699' } {'0.0070551' }
{[37]} {'Exx1'} {'xhr40 '} {'xhr40 '} {'-0.00098295'} {'7.2164e-16' }
{[38]} {'Exx1'} {'GoY '} {'GoY '} {'2.4868' } {'2.4856' }
{[39]} {'Exx1'} {'hours '} {'hours '} {'0.0018799' } {'0.0018708' }
];
% Output of the replication files for orderApp=2
AndreasenEtAl.Q2 = 59.3323;
AndreasenEtAl.moments2 =[ % note that we reshuffeled to be compatible with our matched moments block
{[ 1]} {'Ex' } {'Gr_C '} {' ' } {'0.024388' } {'0.023654' }
{[ 2]} {'Ex' } {'Gr_I '} {' ' } {'0.031046' } {'0.027719' }
{[ 3]} {'Ex' } {'Infl ' } {' ' } {'0.03757' } {'0.034565' }
{[ 4]} {'Ex' } {'r1 ' } {' ' } {'0.056048' } {'0.056419' }
{[ 5]} {'Ex' } {'r40 ' } {' ' } {'0.069929' } {'0.07087' }
{[ 6]} {'Ex' } {'xhr40 '} {' ' } {'0.017237' } {'0.01517' }
{[ 7]} {'Ex' } {'GoY '} {' ' } {'-1.5745' } {'-1.5743' }
{[ 8]} {'Ex' } {'hours '} {' ' } {'-0.043353' } {'-0.043352' }
{[ 9]} {'Exx' } {'Gr_C '} {'Gr_C '} {'0.0013159' } {'0.0012464' }
{[17]} {'Exx' } {'Gr_C '} {'Gr_I '} {'0.0021789' } {'0.0015247' }
{[18]} {'Exx' } {'Gr_C '} {'Infl ' } {'0.00067495' } {'0.0004867' }
{[19]} {'Exx' } {'Gr_C '} {'r1 ' } {'0.0011655' } {'0.0011867' }
{[20]} {'Exx' } {'Gr_C '} {'r40 ' } {'0.0015906' } {'0.0016146' }
{[21]} {'Exx' } {'Gr_C '} {'xhr40 '} {'0.0020911' } {'0.0021395' }
{[10]} {'Exx' } {'Gr_I '} {'Gr_I '} {'0.0089104' } {'0.0043272' }
{[22]} {'Exx' } {'Gr_I '} {'Infl ' } {'0.00063139' } {'0.00021752'}
{[23]} {'Exx' } {'Gr_I '} {'r1 ' } {'0.0011031' } {'0.0013919' }
{[24]} {'Exx' } {'Gr_I '} {'r40 ' } {'0.0018445' } {'0.0018899' }
{[25]} {'Exx' } {'Gr_I '} {'xhr40 '} {'0.00095556' } {'0.0037854' }
{[11]} {'Exx' } {'Infl ' } {'Infl ' } {'0.0020268' } {'0.0021043' }
{[26]} {'Exx' } {'Infl ' } {'r1 ' } {'0.0025263' } {'0.0026571' }
{[27]} {'Exx' } {'Infl ' } {'r40 ' } {'0.0029126' } {'0.0028566' }
{[28]} {'Exx' } {'Infl ' } {'xhr40 '} {'-0.00077101'} {'-0.0016279'}
{[12]} {'Exx' } {'r1 ' } {'r1 ' } {'0.0038708' } {'0.0039136' }
{[29]} {'Exx' } {'r1 ' } {'r40 ' } {'0.0044773' } {'0.0044118' }
{[30]} {'Exx' } {'r1 ' } {'xhr40 '} {'-0.00048202'} {'0.00016791'}
{[13]} {'Exx' } {'r40 ' } {'r40 ' } {'0.0054664' } {'0.0052851' }
{[31]} {'Exx' } {'r40 ' } {'xhr40 '} {'0.00053864' } {'0.00062143'}
{[14]} {'Exx' } {'xhr40 '} {'xhr40 '} {'0.053097' } {'0.018126' }
{[15]} {'Exx' } {'GoY '} {'GoY '} {'2.4863' } {'2.4863' }
{[16]} {'Exx' } {'hours '} {'hours '} {'0.0018799' } {'0.0018806' }
{[32]} {'Exx1'} {'Gr_C '} {'Gr_C '} {'0.00077917' } {'0.00078586'}
{[33]} {'Exx1'} {'Gr_I '} {'Gr_I '} {'0.0050104' } {'0.0021519' }
{[34]} {'Exx1'} {'Infl ' } {'Infl ' } {'0.0019503' } {'0.0019046' }
{[35]} {'Exx1'} {'r1 ' } {'r1 ' } {'0.0038509' } {'0.0038939' }
{[36]} {'Exx1'} {'r40 ' } {'r40 ' } {'0.0054699' } {'0.0052792' }
{[37]} {'Exx1'} {'xhr40 '} {'xhr40 '} {'-0.00098295'} {'0.00023012'}
{[38]} {'Exx1'} {'GoY '} {'GoY '} {'2.4868' } {'2.4852' }
{[39]} {'Exx1'} {'hours '} {'hours '} {'0.0018799' } {'0.0018801' }
];
@#for orderApp in 1:2
method_of_moments(
mom_method = GMM % method of moments method; possible values: GMM|SMM
, datafile = 'AFVRR_data.mat' % name of filename with data
, bartlett_kernel_lag = 10 % bandwith in optimal weighting matrix
, order = @{orderApp} % order of Taylor approximation in perturbation
, pruning % use pruned state space system at higher-order
% , verbose % display and store intermediate estimation results
, weighting_matrix = ['DIAGONAL'] % weighting matrix in moments distance objective function; possible values: OPTIMAL|IDENTITY_MATRIX|DIAGONAL|filename
% , TeX % print TeX tables and graphics
% Optimization options that can be set by the user in the mod file, otherwise default values are provided
%, huge_number=1D10 % value for replacing the infinite bounds on parameters by finite numbers. Used by some optimizers for numerical reasons
, mode_compute = 0 % specifies the optimizer for minimization of moments distance, note that by default there is a new optimizer
, optim = ('TolFun', 1e-6
,'TolX', 1e-6
,'MaxIter', 3000
,'MaxFunEvals', 1D6
,'UseParallel' , 1
%,'Jacobian' , '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_standard_errors
, se_tolx=1e-10
);
% Check results
fprintf('****************************************************************\n')
fprintf('Compare Results for perturbation order @{orderApp}\n')
fprintf('****************************************************************\n')
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;
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))])
if norm(dev_modelmoments)> 1e-4
warning('Something wrong in the computation of moments at order @{orderApp}')
end
@#endfor
%--------------------------------------------------------------------------
% Replicate estimation at orderApp=3
%--------------------------------------------------------------------------
@#ifdef DoEstimation
method_of_moments(
mom_method = GMM % method of moments method; possible values: GMM|SMM
, datafile = 'AFVRR_data.mat' % name of filename with data
, bartlett_kernel_lag = 10 % bandwith in optimal weighting matrix
, order = 3 % order of Taylor approximation in perturbation
, pruning % use pruned state space system at higher-order
% , verbose % display and store intermediate estimation results
, weighting_matrix = ['DIAGONAL', 'Optimal'] % weighting matrix in moments distance objective function; possible values: OPTIMAL|IDENTITY_MATRIX|DIAGONAL|filename
% , TeX % print TeX tables and graphics
% Optimization options that can be set by the user in the mod file, otherwise default values are provided
%, huge_number=1D10 % value for replacing the infinite bounds on parameters by finite numbers. Used by some optimizers for numerical reasons
, mode_compute = 13 % specifies the optimizer for minimization of moments distance, note that by default there is a new optimizer
, additional_optimizer_steps = [13]
, optim = ('TolFun', 1e-6
,'TolX', 1e-6
,'MaxIter', 3000
,'MaxFunEvals', 1D6
,'UseParallel' , 1
%,'Jacobian' , '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_standard_errors
, se_tolx=1e-10
);
@#endif

299
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% DSGE model based on replication files of
% Andreasen, Fernandez-Villaverde, Rubio-Ramirez (2018), The Pruned State-Space System for Non-Linear DSGE Models: Theory and Empirical Applications, Review of Economic Studies, 85, p. 1-49
% Adapted for Dynare by Willi Mutschler (@wmutschl, willi@mutschler.eu), Jan 2021
% =========================================================================
% 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/>.
% =========================================================================
% This is the model with feedback and calibrated RRA
% Original code RunGMM_Feedback_estim_RRA_5.m by Martin M. Andreasen, Jan 2016
@#include "AFVRR_common.inc"
%--------------------------------------------------------------------------
% Parameter calibration taken from RunGMM_Feedback_estim_RRA_5.m
%--------------------------------------------------------------------------
% fixed parameters
INHABIT = 1;
PHI1 = 4;
PHI4 = 1;
KAPAone = 0;
DELTA = 0.025;
THETA = 0.36;
ETA = 6;
CHI = 0;
BETTAxhr = 0;
BETTAxhr40= 0;
RHOD = 0;
GAMA = 0.9999;
CONSxhr20 = 0;
RRA = 5;
% estimated parameters
BETTA = 0.996850651147000;
B = 0.684201133923000;
H = 0.338754441432000;
PHI2 = 0.738293581320000;
KAPAtwo = 11.664785970704999;
ALFA = 0.831836572237000;
RHOR = 0.772754520116000;
BETTAPAI = 3.020381242896000;
BETTAY = 0.288367683973000;
MYYPS = 1.000911709188000;
MYZ = 1.005433723022000;
RHOA = 0.749465413198000;
RHOG = 0.847225569814000;
PAI = 1.010428794858000;
CONSxhr40 = 0.992863217133000;
GoY = 0.207099399789000;
STDA = 0.015621059978000;
STDG = 0.047539390956000;
STDD = 0.008623441943000;
% endogenous parameters set via steady state, no need to initialize
%PHIzero = ;
%AA = ;
%PHI3 = ;
%negVf = ;
model_diagnostics;
% Model diagnostics show that some parameters are endogenously determined
% via the steady state, so we run steady to calibrate all parameters
steady;
model_diagnostics;
% Now all parameters are determined
resid;
check;
%--------------------------------------------------------------------------
% Shock distribution
%--------------------------------------------------------------------------
shocks;
var eps_a = STDA^2;
var eps_d = STDD^2;
var eps_g = STDG^2;
end;
%--------------------------------------------------------------------------
% Estimated Params block - these parameters will be estimated, we
% initialize at calibrated values
%--------------------------------------------------------------------------
estimated_params;
BETTA;
B;
H;
PHI2;
KAPAtwo;
ALFA;
RHOR;
BETTAPAI;
BETTAY;
MYYPS;
MYZ;
RHOA;
RHOG;
PAI;
CONSxhr40;
GoY;
stderr eps_a;
stderr eps_g;
stderr eps_d;
end;
estimated_params_init(use_calibration);
end;
%--------------------------------------------------------------------------
% Compare whether toolbox yields equivalent moments at second order
%--------------------------------------------------------------------------
% Note that we compare results for orderApp=1|2 and not for orderApp=3, because
% there is a small error in the replication files of the original article in the
% computation of the covariance matrix of the extended innovations vector.
% The authors have been contacted, fixed it, and report that the results
% change only slightly at orderApp=3 to what they report in the paper. At
% orderApp=2 all is correct and so the following part tests whether we get
% the same model moments at the calibrated parameters (we do not optimize).
% We compare it to the replication file RunGMM_Feedback_estim_RRA.m with the
% following settings: orderApp=1|2, seOn=1, q_lag=10, weighting=1+1;
% scaled=0; optimizer=0; estimator=1; momentSet=2;
%
% Output of the replication files for orderApp=1
AndreasenEtAl.Q1 = 60275.3715;
AndreasenEtAl.moments1 =[ % note that we reshuffeled to be compatible with our matched moments block
{[ 1]} {'Ex' } {'Gr_C '} {' ' } {'0.024388' } {'0.023726' }
{[ 2]} {'Ex' } {'Gr_I '} {' ' } {'0.031046' } {'0.027372' }
{[ 3]} {'Ex' } {'Infl ' } {' ' } {'0.03757' } {'0.041499' }
{[ 4]} {'Ex' } {'r1 ' } {' ' } {'0.056048' } {'0.077843' }
{[ 5]} {'Ex' } {'r40 ' } {' ' } {'0.069929' } {'0.077843' }
{[ 6]} {'Ex' } {'xhr40 '} {' ' } {'0.017237' } {'0' }
{[ 7]} {'Ex' } {'GoY '} {' ' } {'-1.5745' } {'-1.5746' }
{[ 8]} {'Ex' } {'hours '} {' ' } {'-0.043353' } {'-0.043299' }
{[ 9]} {'Exx' } {'Gr_C '} {'Gr_C '} {'0.0013159' } {'0.0012763' }
{[17]} {'Exx' } {'Gr_C '} {'Gr_I '} {'0.0021789' } {'0.0017759' }
{[18]} {'Exx' } {'Gr_C '} {'Infl ' } {'0.00067495' } {'0.00077354' }
{[19]} {'Exx' } {'Gr_C '} {'r1 ' } {'0.0011655' } {'0.0016538' }
{[20]} {'Exx' } {'Gr_C '} {'r40 ' } {'0.0015906' } {'0.0017949' }
{[21]} {'Exx' } {'Gr_C '} {'xhr40 '} {'0.0020911' } {'0.0017847' }
{[10]} {'Exx' } {'Gr_I '} {'Gr_I '} {'0.0089104' } {'0.0053424' }
{[22]} {'Exx' } {'Gr_I '} {'Infl ' } {'0.00063139' } {'0.00064897' }
{[23]} {'Exx' } {'Gr_I '} {'r1 ' } {'0.0011031' } {'0.0019533' }
{[24]} {'Exx' } {'Gr_I '} {'r40 ' } {'0.0018445' } {'0.0020602' }
{[25]} {'Exx' } {'Gr_I '} {'xhr40 '} {'0.00095556' } {'0.0064856' }
{[11]} {'Exx' } {'Infl ' } {'Infl ' } {'0.0020268' } {'0.0020922' }
{[26]} {'Exx' } {'Infl ' } {'r1 ' } {'0.0025263' } {'0.0036375' }
{[27]} {'Exx' } {'Infl ' } {'r40 ' } {'0.0029126' } {'0.0034139' }
{[28]} {'Exx' } {'Infl ' } {'xhr40 '} {'-0.00077101'} {'-0.0011665' }
{[12]} {'Exx' } {'r1 ' } {'r1 ' } {'0.0038708' } {'0.0066074' }
{[29]} {'Exx' } {'r1 ' } {'r40 ' } {'0.0044773' } {'0.0062959' }
{[30]} {'Exx' } {'r1 ' } {'xhr40 '} {'-0.00048202'} {'-0.00075499'}
{[13]} {'Exx' } {'r40 ' } {'r40 ' } {'0.0054664' } {'0.0061801' }
{[31]} {'Exx' } {'r40 ' } {'xhr40 '} {'0.00053864' } {'-0.00030456'}
{[14]} {'Exx' } {'xhr40 '} {'xhr40 '} {'0.053097' } {'0.012048' }
{[15]} {'Exx' } {'GoY '} {'GoY '} {'2.4863' } {'2.4872' }
{[16]} {'Exx' } {'hours '} {'hours '} {'0.0018799' } {'0.0018759' }
{[32]} {'Exx1'} {'Gr_C '} {'Gr_C '} {'0.00077917' } {'0.00080528' }
{[33]} {'Exx1'} {'Gr_I '} {'Gr_I '} {'0.0050104' } {'0.0017036' }
{[34]} {'Exx1'} {'Infl ' } {'Infl ' } {'0.0019503' } {'0.0020185' }
{[35]} {'Exx1'} {'r1 ' } {'r1 ' } {'0.0038509' } {'0.0065788' }
{[36]} {'Exx1'} {'r40 ' } {'r40 ' } {'0.0054699' } {'0.0061762' }
{[37]} {'Exx1'} {'xhr40 '} {'xhr40 '} {'-0.00098295'} {'-4.5519e-15'}
{[38]} {'Exx1'} {'GoY '} {'GoY '} {'2.4868' } {'2.4863' }
{[39]} {'Exx1'} {'hours '} {'hours '} {'0.0018799' } {'0.0018755' }
];
% Output of the replication files for orderApp=2
AndreasenEtAl.Q2 = 140.8954;
AndreasenEtAl.moments2 =[ % note that we reshuffeled to be compatible with our matched moments block
{[ 1]} {'Ex' } {'Gr_C '} {' ' } {'0.024388' } {'0.023726' }
{[ 2]} {'Ex' } {'Gr_I '} {' ' } {'0.031046' } {'0.027372' }
{[ 3]} {'Ex' } {'Infl ' } {' ' } {'0.03757' } {'0.034618' }
{[ 4]} {'Ex' } {'r1 ' } {' ' } {'0.056048' } {'0.056437' }
{[ 5]} {'Ex' } {'r40 ' } {' ' } {'0.069929' } {'0.07051' }
{[ 6]} {'Ex' } {'xhr40 '} {' ' } {'0.017237' } {'0.014242' }
{[ 7]} {'Ex' } {'GoY '} {' ' } {'-1.5745' } {'-1.574' }
{[ 8]} {'Ex' } {'hours '} {' ' } {'-0.043353' } {'-0.043351' }
{[ 9]} {'Exx' } {'Gr_C '} {'Gr_C '} {'0.0013159' } {'0.0012917' }
{[17]} {'Exx' } {'Gr_C '} {'Gr_I '} {'0.0021789' } {'0.0017862' }
{[18]} {'Exx' } {'Gr_C '} {'Infl ' } {'0.00067495' } {'0.00061078' }
{[19]} {'Exx' } {'Gr_C '} {'r1 ' } {'0.0011655' } {'0.0011494' }
{[20]} {'Exx' } {'Gr_C '} {'r40 ' } {'0.0015906' } {'0.0016149' }
{[21]} {'Exx' } {'Gr_C '} {'xhr40 '} {'0.0020911' } {'0.002203' }
{[10]} {'Exx' } {'Gr_I '} {'Gr_I '} {'0.0089104' } {'0.0054317' }
{[22]} {'Exx' } {'Gr_I '} {'Infl ' } {'0.00063139' } {'0.00045278' }
{[23]} {'Exx' } {'Gr_I '} {'r1 ' } {'0.0011031' } {'0.0013672' }
{[24]} {'Exx' } {'Gr_I '} {'r40 ' } {'0.0018445' } {'0.0018557' }
{[25]} {'Exx' } {'Gr_I '} {'xhr40 '} {'0.00095556' } {'0.0067742' }
{[11]} {'Exx' } {'Infl ' } {'Infl ' } {'0.0020268' } {'0.0016583' }
{[26]} {'Exx' } {'Infl ' } {'r1 ' } {'0.0025263' } {'0.0024521' }
{[27]} {'Exx' } {'Infl ' } {'r40 ' } {'0.0029126' } {'0.002705' }
{[28]} {'Exx' } {'Infl ' } {'xhr40 '} {'-0.00077101'} {'-0.00065007'}
{[12]} {'Exx' } {'r1 ' } {'r1 ' } {'0.0038708' } {'0.0038274' }
{[29]} {'Exx' } {'r1 ' } {'r40 ' } {'0.0044773' } {'0.004297' }
{[30]} {'Exx' } {'r1 ' } {'xhr40 '} {'-0.00048202'} {'6.3243e-05' }
{[13]} {'Exx' } {'r40 ' } {'r40 ' } {'0.0054664' } {'0.0051686' }
{[31]} {'Exx' } {'r40 ' } {'xhr40 '} {'0.00053864' } {'0.00066645' }
{[14]} {'Exx' } {'xhr40 '} {'xhr40 '} {'0.053097' } {'0.013543' }
{[15]} {'Exx' } {'GoY '} {'GoY '} {'2.4863' } {'2.4858' }
{[16]} {'Exx' } {'hours '} {'hours '} {'0.0018799' } {'0.0018804' }
{[32]} {'Exx1'} {'Gr_C '} {'Gr_C '} {'0.00077917' } {'0.00081772' }
{[33]} {'Exx1'} {'Gr_I '} {'Gr_I '} {'0.0050104' } {'0.0017106' }
{[34]} {'Exx1'} {'Infl ' } {'Infl ' } {'0.0019503' } {'0.0015835' }
{[35]} {'Exx1'} {'r1 ' } {'r1 ' } {'0.0038509' } {'0.0037985' }
{[36]} {'Exx1'} {'r40 ' } {'r40 ' } {'0.0054699' } {'0.0051642' }
{[37]} {'Exx1'} {'xhr40 '} {'xhr40 '} {'-0.00098295'} {'0.00020285' }
{[38]} {'Exx1'} {'GoY '} {'GoY '} {'2.4868' } {'2.4848' }
{[39]} {'Exx1'} {'hours '} {'hours '} {'0.0018799' } {'0.0018799' }
];
@#for orderApp in 1:2
method_of_moments(
mom_method = GMM % method of moments method; possible values: GMM|SMM
, datafile = 'AFVRR_data.mat' % name of filename with data
, bartlett_kernel_lag = 10 % bandwith in optimal weighting matrix
, order = @{orderApp} % order of Taylor approximation in perturbation
, pruning % use pruned state space system at higher-order
% , verbose % display and store intermediate estimation results
, weighting_matrix = ['DIAGONAL'] % weighting matrix in moments distance objective function; possible values: OPTIMAL|IDENTITY_MATRIX|DIAGONAL|filename
% , TeX % print TeX tables and graphics
% Optimization options that can be set by the user in the mod file, otherwise default values are provided
%, huge_number=1D10 % value for replacing the infinite bounds on parameters by finite numbers. Used by some optimizers for numerical reasons
, mode_compute = 0 % specifies the optimizer for minimization of moments distance, note that by default there is a new optimizer
, optim = ('TolFun', 1e-6
,'TolX', 1e-6
,'MaxIter', 3000
,'MaxFunEvals', 1D6
,'UseParallel' , 1
%,'Jacobian' , '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_standard_errors
, se_tolx=1e-10
);
% Check results
fprintf('****************************************************************\n')
fprintf('Compare Results for perturbation order @{orderApp}\n')
fprintf('****************************************************************\n')
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;
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))])
if norm(dev_modelmoments)> 1e-4
warning('Something wrong in the computation of moments at order @{orderApp}')
end
@#endfor
%--------------------------------------------------------------------------
% Replicate estimation at orderApp=3
%--------------------------------------------------------------------------
@#ifdef DoEstimation
method_of_moments(
mom_method = GMM % method of moments method; possible values: GMM|SMM
, datafile = 'AFVRR_data.mat' % name of filename with data
, bartlett_kernel_lag = 10 % bandwith in optimal weighting matrix
, order = 3 % order of Taylor approximation in perturbation
, pruning % use pruned state space system at higher-order
% , verbose % display and store intermediate estimation results
, weighting_matrix = ['DIAGONAL', 'Optimal'] % weighting matrix in moments distance objective function; possible values: OPTIMAL|IDENTITY_MATRIX|DIAGONAL|filename
% , TeX % print TeX tables and graphics
% Optimization options that can be set by the user in the mod file, otherwise default values are provided
%, huge_number=1D10 % value for replacing the infinite bounds on parameters by finite numbers. Used by some optimizers for numerical reasons
, mode_compute = 13 % specifies the optimizer for minimization of moments distance, note that by default there is a new optimizer
, additional_optimizer_steps = [13]
, optim = ('TolFun', 1e-6
,'TolX', 1e-6
,'MaxIter', 3000
,'MaxFunEvals', 1D6
,'UseParallel' , 1
%,'Jacobian' , '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_standard_errors
, se_tolx=1e-10
);
@#endif

540
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@ -0,0 +1,540 @@
% DSGE model based on replication files of
% Andreasen, Fernandez-Villaverde, Rubio-Ramirez (2018), The Pruned State-Space System for Non-Linear DSGE Models: Theory and Empirical Applications, Review of Economic Studies, 85, p. 1-49
% Original code by Martin M. Andreasen, Jan 2016
% Adapted for Dynare by Willi Mutschler (@wmutschl, willi@mutschler.eu), Jan 2021
% =========================================================================
% 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/>.
% =========================================================================
%--------------------------------------------------------------------------
% Variable declaration
%--------------------------------------------------------------------------
var
ln_k
ln_s
ln_a
ln_g
ln_d
ln_c
ln_r
ln_pai
ln_h
ln_q
ln_evf
ln_iv
ln_x2
ln_la
ln_goy
ln_Esdf
xhr20
xhr40
Exhr
@#for i in 1:40
ln_p@{i}
@#endfor
Obs_Gr_C
Obs_Gr_I
Obs_Infl
Obs_r1
Obs_r40
Obs_xhr40
Obs_GoY
Obs_hours
;
predetermined_variables ln_k ln_s;
varobs Obs_Gr_C Obs_Gr_I Obs_Infl Obs_r1 Obs_r40 Obs_xhr40 Obs_GoY Obs_hours;
%--------------------------------------------------------------------------
% Exogenous shocks
%--------------------------------------------------------------------------
varexo
eps_a
eps_d
eps_g
;
%--------------------------------------------------------------------------
% Parameter declaration
%--------------------------------------------------------------------------
parameters
BETTA
B
INHABIT
H
PHI1
PHI2
RRA
PHI4
KAPAone
KAPAtwo
DELTA
THETA
ETA
ALFA
CHI
RHOR
BETTAPAI
BETTAY
MYYPS
MYZ
RHOA
%STDA
RHOG
%STDG
RHOD
%STDD
CONSxhr40
BETTAxhr
BETTAxhr40
CONSxhr20
PAI
GAMA
GoY
%auxiliary
PHIzero
AA
PHI3
negVf
;
%--------------------------------------------------------------------------
% Model equations
%--------------------------------------------------------------------------
% Based on DSGE_model_NegVf_yieldCurve.m and DSGE_model_PosVf_yieldCurve.m
% Note that we include an auxiliary parameter negVf to distinguish whether
% the steady state value function is positive (negVf=0) or negative (negVf=1).
% This parameter is endogenously determined in the steady_state_model block.
model;
%--------------------------------------------------------------------------
% Auxiliary expressions
%--------------------------------------------------------------------------
% do exp transform such that variables are logged variables
@#for var in [ "k", "s", "c", "r", "a", "g", "d", "pai", "h", "q", "evf", "iv", "x2", "la", "goy", "Esdf" ]
#@{var}_ba1 = exp(ln_@{var}(-1));
#@{var}_cu = exp(ln_@{var});
#@{var}_cup = exp(ln_@{var}(+1));
@#endfor
@#for i in 1:40
#p@{i}_cu = exp(ln_p@{i});
#p@{i}_cup = exp(ln_p@{i}(+1));
@#endfor
% these variables are not transformed
#xhr20_cu = xhr20;
#xhr20_cup = xhr20(+1);
#xhr40_cu = xhr40;
#xhr40_cup = xhr40(+1);
#Exhr_cu = Exhr;
#Exhr_cup = Exhr(+1);
% auxiliary steady state variables
#K = exp(steady_state(ln_k));
#IV = exp(steady_state(ln_iv));
#C = exp(steady_state(ln_c));
#Y = (C + IV)/(1-GoY);
#R = exp(steady_state(ln_r));
#G = Y-C-IV;
#removeMeanXhr = 1;
% The atemporal relations if possible
% No stochastic trend in investment specific shocks
#myyps_cu = MYYPS;
#myyps_cup = MYYPS;
% No stochastic trend in non-stationary technology shocks
#myz_cu = MYZ;
#myz_cup = MYZ;
% Defining myzstar
#MYZSTAR = MYYPS^(THETA/(1-THETA))*MYZ;
#myzstar_cu = myyps_cu ^(THETA/(1-THETA))*myz_cu;
#myzstar_cup= myyps_cup^(THETA/(1-THETA))*myz_cup;
% The expression for the value function (only valid for deterministic trends!)
% Note that we make use of auxiliary parameter negVf to switch signs
#mvf_cup = -negVf*(d_cup/(1-PHI2)*((c_cup-B*c_cu*MYZSTAR^-1)^(1-PHI2)-1) + d_cup*PHIzero/(1-PHI1)*(1-h_cup)^(1-PHI1) - negVf* BETTA*MYZSTAR^((1-PHI4)*(1-PHI2))*AA*evf_cup^(1/(1-PHI3)));
% The growth rate in lambda
#myla_cup = (la_cup/la_cu)*(AA*evf_cu^(1/(1-PHI3))/mvf_cup)^PHI3*myzstar_cup^(-PHI2*(1-PHI4)-PHI4);
% The relation between the optimal price for the firms and the pris and inflation
%ptil_cu = ((1-ALFA*(pai_ba1^CHI/pai_cu )^(1-ETA))/(1-ALFA))^(1/(1-ETA));
%ptil_cup = ((1-ALFA*(pai_cu ^CHI/pai_cup)^(1-ETA))/(1-ALFA))^(1/(1-ETA));
#ptil_cu = ((1-ALFA*(1/pai_cu )^(1-ETA))/(1-ALFA))^(1/(1-ETA));
#ptil_cup = ((1-ALFA*(1/pai_cup)^(1-ETA))/(1-ALFA))^(1/(1-ETA));
% From the households' FOC for labor
#w_cu = d_cu*PHIzero*(1-h_cu )^(-PHI1)/la_cu;
#w_cup = d_cu*PHIzero*(1-h_cup)^(-PHI1)/la_cup;
% Shouldn't w_cup include d_cup? Let's stick to the original (wrong) code in the replication files as results don't change dramatically... [@wmutschl]
% The firms' FOC for labor
#mc_cu = w_cu /((1-THETA)*a_cu *myyps_cu ^(-THETA/(1-THETA))*myz_cu ^-THETA *k_cu ^THETA*h_cu ^(-THETA));
#mc_cup = w_cup/((1-THETA)*a_cup*myyps_cup^(-THETA/(1-THETA))*myz_cup^-THETA *k_cup^THETA*h_cup^(-THETA));
% The firms' FOC for capital
#rk_cu = mc_cu *THETA* a_cu *myyps_cu *myz_cu ^(1-THETA)*k_cu ^(THETA-1)*h_cu ^(1-THETA);
#rk_cup = mc_cup*THETA* a_cup*myyps_cup*myz_cup^(1-THETA)*k_cup^(THETA-1)*h_cup^(1-THETA);
% The income identity
#y_cu = c_cu + iv_cu + g_cu;
%--------------------------------------------------------------------------
% Actual model equations
%--------------------------------------------------------------------------
[name='Expected value of the value function']
0 = -evf_cu + (mvf_cup/AA)^(1-PHI3);
[name='Households FOC for capital']
0 = -q_cu+BETTA*myla_cup/myyps_cup*(rk_cup+q_cup*(1-DELTA) -q_cup*KAPAtwo/2*(iv_cup/k_cup*myyps_cup*myzstar_cup - IV/K*MYYPS*MYZSTAR)^2 +q_cup*KAPAtwo*(iv_cup/k_cup*myyps_cup*myzstar_cup - IV/K*MYYPS*MYZSTAR)*iv_cup/k_cup*myyps_cup*myzstar_cup);
[name='Households FOC for investments']
0 = -1+q_cu*(1-KAPAone/2*(iv_cu/IV-1)^2-iv_cu/IV*KAPAone*(iv_cu/IV-1)-KAPAtwo*(iv_cu/k_cu*myyps_cu*myzstar_cu - IV/K*MYYPS*MYZSTAR));
[name='Euler equation for consumption']
0 = -1+BETTA*r_cu*exp(CONSxhr40*xhr40_cu + CONSxhr20*xhr20_cu)*myla_cup/pai_cup;
[name='Households FOC for consumption']
0 = -la_cu + d_cu*(c_cu -B*c_ba1*myzstar_cu^-1)^(-PHI2) -INHABIT*B*BETTA*d_cup*(AA*evf_cu^(1/(1-PHI3))/mvf_cup)^PHI3*(c_cup -B*c_cu*myzstar_cup^-1)^(-PHI2)*myzstar_cup^(-PHI2*(1-PHI4)-PHI4);
[name='Nonlinear pricing, relation for x1 = (ETA-1)/ETA*x2']
0= -(ETA-1)/ETA*x2_cu+y_cu*mc_cu*ptil_cu^(-ETA-1) +ALFA*BETTA*myla_cup*(ptil_cu/ptil_cup)^(-ETA-1)*(1/pai_cup)^(-ETA)*(ETA-1)/ETA*x2_cup*myzstar_cup;
[name='Nonlinear pricing, relation for x2']
0=-x2_cu+y_cu*ptil_cu^-ETA +ALFA*BETTA*myla_cup*(ptil_cu/ptil_cup)^(-ETA)*(1/pai_cup)^(1-ETA)*x2_cup*myzstar_cup;
[name='Nonlinear pricing, relation for s']
0= -s_cup+(1-ALFA)*ptil_cu^(-ETA)+ALFA*(pai_cu/1)^ETA*s_cu;
[name='Interest rate rule']
0 = -log(r_cu/R)+RHOR*log(r_ba1/R)+(1-RHOR)*(BETTAPAI*log(pai_cu/PAI)+BETTAY*log(y_cu/Y) + BETTAxhr*(BETTAxhr40*xhr40_cu - removeMeanXhr*Exhr_cu));
[name='Production function']
0 = -y_cu*s_cup + a_cu *(k_cu *myyps_cu ^(-1/(1-THETA))*myz_cu ^-1)^THETA*h_cu ^(1-THETA);
[name='Relation for physical capital stock']
0= -k_cup + (1-DELTA)*k_cu*(myyps_cu*myzstar_cu)^-1 + iv_cu - iv_cu*KAPAone/2*(iv_cu/IV-1)^2 - k_cu*(myyps_cu*myzstar_cu)^-1*KAPAtwo/2*(iv_cu/k_cu*myyps_cu*myzstar_cu - IV/K*MYYPS*MYZSTAR)^2;
[name='Goverment spending over output']
0=-goy_cu + g_cu/y_cu;
[name='The yield curve: p1']
0= -p1_cu + 1/r_cu;
@#for i in 2:40
[name='The yield curve: p@{i}']
0= -p@{i}_cu + BETTA*myla_cup/pai_cup*p@{i-1}_cup;
@#endfor
[name='Stochastic discount factor']
0= -Esdf_cu+ BETTA*myla_cup/pai_cup;
[name='Expected 5 year excess holding period return']
0= -xhr20_cu+ log(p19_cup) - log(p20_cu) - log(r_cu);
[name='Expected 10 year excess holding period return']
0= -xhr40_cu+ log(p39_cup) - log(p40_cu) - log(r_cu);
[name='Mean of expected excess holding period return in Taylor rule']
0= -Exhr_cu + (1-GAMA)*(BETTAxhr40*xhr40_cu) + GAMA*Exhr_cup;
[name='Exogenous process for productivity']
0 = -log(a_cu)+RHOA*log(a_ba1) + eps_a;
[name='Exogenous process for government spending']
0 = -log(g_cu/G)+RHOG*log(g_ba1/G) + eps_g;
[name='Exogenous process for discount factor shifter']
0 = -log(d_cu)+RHOD*log(d_ba1) + eps_d;
[name='Observable annualized consumption growth']
Obs_Gr_C = 4*( ln_c -ln_c(-1) + log(MYZSTAR));
[name='Observable annualized investment growth']
Obs_Gr_I = 4*( ln_iv - ln_iv(-1) + log(MYZSTAR)+log(MYYPS));
[name='Observable annualized inflation']
Obs_Infl = 4*( ln_pai);
[name='Observable annualized one-quarter nominal yield']
Obs_r1 = 4*( ln_r);
[name='Observable annualized 10-year nominal yield']
Obs_r40 = 4*( -1/40*ln_p40);
[name='Observable annualized 10-year ex post excess holding period return']
Obs_xhr40 = 4*( ln_p39 - ln_p40(-1) - ln_r(-1) );
[name='Observable annualized log ratio of government spending to GDP']
Obs_GoY = 4*( 1/4*ln_goy);
[name='Observable annualized log of hours']
Obs_hours = 4*( 1/100*ln_h);
end;
%--------------------------------------------------------------------------
% Steady State Computations
%--------------------------------------------------------------------------
% Based on DSGE_model_yieldCurve_ss.m, getPHI3.m, ObjectGMM.m
% Note that we include an auxiliary parameter negVf to distinguish whether
% the steady state value function is positive (negVf=0) or negative (negVf=1).
% This parameter is endogenously determined in the steady_state_model block.
steady_state_model;
% The growth rate in the firms' fixed costs
MYZSTARBAR = MYYPS^(THETA/(1-THETA))*MYZ;
% The growth rate for lampda
MYLABAR = MYZSTARBAR^(-PHI2*(1-PHI4)-PHI4);
% The relative optimal price for firms
PTILBAR = ((1-ALFA*PAI^((CHI-1)*(1-ETA)))/(1-ALFA))^(1/(1-ETA));
% The state variable s for distortions between output and produktion
SBAR = ((1-ALFA)*PTILBAR^(-ETA))/(1-ALFA*PAI^((1-CHI)*ETA));
% The 1-period interest rate
RBAR = PAI/(BETTA*MYLABAR);
% The market price of capital
QBAR = 1;
% The real price of renting capital
RKBAR = QBAR*(MYYPS/(BETTA*MYLABAR)-(1-DELTA));
% The marginal costs in the firms
MCBAR = (1-ALFA*BETTA*MYLABAR*PAI^((1-CHI)*ETA)*MYZSTARBAR)*(ETA-1)/ETA*PTILBAR/(1-ALFA*BETTA*MYLABAR*PAI^((CHI-1)*(1-ETA))*MYZSTARBAR);
% The capital stock
KBAR = H*(RKBAR/(MCBAR*THETA*MYYPS*MYZ^(1-THETA)))^(1/(THETA-1));
% The wage level
WBAR = MCBAR*(1-THETA)*MYYPS^(-THETA/(1-THETA))*MYZ^-THETA*(KBAR/H)^THETA;
% The level of investment
IVBAR = KBAR - (1-DELTA)*KBAR*MYYPS^(-1/(1-THETA))*MYZ^-1;
% The consumption level
CBAR = ((1-GoY)*(KBAR*MYYPS^(-1/(1-THETA))*MYZ^-1)^THETA*H^(1-THETA))/SBAR-IVBAR;
% The output level
YBAR = (CBAR + IVBAR)/(1-GoY);
% The value of lambda
LABAR = (CBAR-B*CBAR*MYZSTARBAR^-1)^-PHI2 - INHABIT*B*BETTA*(CBAR-B*CBAR*MYZSTARBAR^-1)^-PHI2*MYZSTARBAR^(-PHI2*(1-PHI4)-PHI4);
% The value of PHIzero
PHIzero = LABAR*WBAR*(1-H)^PHI1;
% The level of the value function
VFBAR = 1/(1-BETTA*MYZSTARBAR^((1-PHI4)*(1-PHI2)))*(1/(1-PHI2)*((CBAR-B*CBAR*MYZSTARBAR^-1)^(1-PHI2)-1)+PHIzero/(1-PHI1)*(1-H)^(1-PHI1));
UBAR = 1/(1-PHI2)*((CBAR-B*CBAR*MYZSTARBAR^-1)^(1-PHI2)-1)+PHIzero/(1-PHI1)*(1-H)^(1-PHI1);
[AA, EVFBAR, PHI3, negVf, info]= AFVRR_steady_helper(VFBAR,RBAR,IVBAR,CBAR,KBAR,LABAR,QBAR,YBAR, BETTA,B,PAI,H,PHIzero,PHI1,PHI2,THETA,MYYPS,MYZ,INHABIT,RRA,CONSxhr40);
% The value of X2
X2BAR = YBAR*PTILBAR^(-ETA)/(1-BETTA*ALFA*MYLABAR*PAI^((CHI-1)*(1-ETA))*MYZSTARBAR);
% Government spending
GBAR = GoY*YBAR;
%**************************************************************************
% map into model variables
ln_k = log(KBAR);
ln_s = log(SBAR);
ln_c_ba1 = log(CBAR);
ln_r_ba1 = log(RBAR);
ln_a = log(1);
ln_g = log(GBAR);
ln_d = log(1);
ln_c = log(CBAR);
ln_r = log(RBAR);
ln_pai = log(PAI);
ln_h = log(H);
ln_q = log(QBAR);
ln_evf = log(EVFBAR);
ln_iv = log(IVBAR);
ln_x2 = log(X2BAR);
ln_la = log(LABAR);
ln_goy = log(GoY);
ln_Esdf = log(1/RBAR);
xhr20 = 0;
xhr40 = 0;
Exhr = 0;
% The yield curve
ln_p1 = log((1/RBAR)^1);
ln_p2 = log((1/RBAR)^2);
ln_p3 = log((1/RBAR)^3);
ln_p4 = log((1/RBAR)^4);
ln_p5 = log((1/RBAR)^5);
ln_p6 = log((1/RBAR)^6);
ln_p7 = log((1/RBAR)^7);
ln_p8 = log((1/RBAR)^8);
ln_p9 = log((1/RBAR)^9);
ln_p10 = log((1/RBAR)^10);
ln_p11 = log((1/RBAR)^11);
ln_p12 = log((1/RBAR)^12);
ln_p13 = log((1/RBAR)^13);
ln_p14 = log((1/RBAR)^14);
ln_p15 = log((1/RBAR)^15);
ln_p16 = log((1/RBAR)^16);
ln_p17 = log((1/RBAR)^17);
ln_p18 = log((1/RBAR)^18);
ln_p19 = log((1/RBAR)^19);
ln_p20 = log((1/RBAR)^20);
ln_p21 = log((1/RBAR)^21);
ln_p22 = log((1/RBAR)^22);
ln_p23 = log((1/RBAR)^23);
ln_p24 = log((1/RBAR)^24);
ln_p25 = log((1/RBAR)^25);
ln_p26 = log((1/RBAR)^26);
ln_p27 = log((1/RBAR)^27);
ln_p28 = log((1/RBAR)^28);
ln_p29 = log((1/RBAR)^29);
ln_p30 = log((1/RBAR)^30);
ln_p31 = log((1/RBAR)^31);
ln_p32 = log((1/RBAR)^32);
ln_p33 = log((1/RBAR)^33);
ln_p34 = log((1/RBAR)^34);
ln_p35 = log((1/RBAR)^35);
ln_p36 = log((1/RBAR)^36);
ln_p37 = log((1/RBAR)^37);
ln_p38 = log((1/RBAR)^38);
ln_p39 = log((1/RBAR)^39);
ln_p40 = log((1/RBAR)^40);
Obs_Gr_C = 4*( log(MYZSTARBAR) );
Obs_Gr_I = 4*( log(MYZSTARBAR)+log(MYYPS) );
Obs_Infl = 4*( ln_pai );
Obs_r1 = 4*( ln_r );
Obs_r40 = 4*( -1/40*ln_p40 );
Obs_xhr40 = 4*( xhr40 );
Obs_GoY = 4*( 1/4*ln_goy );
Obs_hours = 4*( 1/100*ln_h );
end;
%--------------------------------------------------------------------------
% Declare moments to use in estimation
%--------------------------------------------------------------------------
% These are the moments used in the paper; corresponds to momentSet=2 in the replication files
matched_moments;
%mean
Obs_Gr_C;
Obs_Gr_I;
Obs_Infl;
Obs_r1;
Obs_r40;
Obs_xhr40;
Obs_GoY;
Obs_hours;
% all variances
Obs_Gr_C*Obs_Gr_C;
Obs_Gr_I*Obs_Gr_I;
Obs_Infl*Obs_Infl;
Obs_r1*Obs_r1;
Obs_r40*Obs_r40;
Obs_xhr40*Obs_xhr40;
Obs_GoY*Obs_GoY;
Obs_hours*Obs_hours;
% covariance excluding GoY and hours
Obs_Gr_C*Obs_Gr_I;
Obs_Gr_C*Obs_Infl;
Obs_Gr_C*Obs_r1;
Obs_Gr_C*Obs_r40;
Obs_Gr_C*Obs_xhr40;
%Obs_Gr_C*Obs_GoY;
%Obs_Gr_C*Obs_hours;
Obs_Gr_I*Obs_Infl;
Obs_Gr_I*Obs_r1;
Obs_Gr_I*Obs_r40;
Obs_Gr_I*Obs_xhr40;
%Obs_Gr_I*Obs_GoY;
%Obs_Gr_I*Obs_hours;
Obs_Infl*Obs_r1;
Obs_Infl*Obs_r40;
Obs_Infl*Obs_xhr40;
%Obs_Infl*Obs_GoY;
%Obs_Infl*Obs_hours;
Obs_r1*Obs_r40;
Obs_r1*Obs_xhr40;
%Obs_r1*Obs_GoY;
%Obs_r1*Obs_hours;
Obs_r40*Obs_xhr40;
%Obs_r40*Obs_GoY;
%Obs_r40*Obs_hours;
%Obs_xhr40*Obs_GoY;
%Obs_xhr40*Obs_hours;
%Obs_GoY*Obs_hours;
%first autocovariance
Obs_Gr_C*Obs_Gr_C(-1);
Obs_Gr_I*Obs_Gr_I(-1);
Obs_Infl*Obs_Infl(-1);
Obs_r1*Obs_r1(-1);
Obs_r40*Obs_r40(-1);
Obs_xhr40*Obs_xhr40(-1);
Obs_GoY*Obs_GoY(-1);
Obs_hours*Obs_hours(-1);
end;
%--------------------------------------------------------------------------
% Create Data
%--------------------------------------------------------------------------
@#ifdef CreateData
verbatim;
% From 1961Q3 to 2007Q4
DataUS = xlsread('Data_PruningPaper_v5.xlsx','Data_used','E3:M188');
% ANNUALIZED (except for hours and GoY)
% 1 2 3 4 5 6 7 8 9
% Lables: Date Gr_C Gr_I GoY hours Infl_C r1 r40 xhr40
%label_data = {'Gr_C ', 'Gr_I ','Infl ', 'r1 ', 'r40 ', 'xhr40 ','GoY ', 'hours '};
%DataUS = [DataUS(:,2:3) DataUS(:,6:8) DataUS(:,9) log(DataUS(:,4)) 4*log(DataUS(:,5))/100];
Obs_Gr_C = DataUS(:,2);
Obs_Gr_I = DataUS(:,3);
Obs_Infl = DataUS(:,6);
Obs_r1 = DataUS(:,7);
Obs_r40 = DataUS(:,8);
Obs_xhr40 = DataUS(:,9);
Obs_GoY = log(DataUS(:,4));
Obs_hours = 4*log(DataUS(:,5))/100;
save('AFVRR_data.mat','Obs_Gr_C','Obs_Gr_I','Obs_Infl','Obs_r1','Obs_r40','Obs_xhr40','Obs_GoY','Obs_hours');
pause(1);
end;
@#endif

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tests/estimation/method_of_moments/AFVRR/AFVRR_steady_helper.m Normal file
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@ -0,0 +1,80 @@
% DSGE model based on replication files of
% Andreasen, Fernandez-Villaverde, Rubio-Ramirez (2018), The Pruned State-Space System for Non-Linear DSGE Models: Theory and Empirical Applications, Review of Economic Studies, 85, p. 1-49
% Adapted for Dynare by Willi Mutschler (@wmutschl, willi@mutschler.eu), Jan 2021
% =========================================================================
% 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/>.
% =========================================================================
% This is a helper function to compute steady state values and endogenous parameters
% Based on DSGE_model_yieldCurve_ss.m, getPHI3.m, ObjectGMM.m
function [AA, EVFBAR, PHI3, negVf, info]= AFVRR_steady_helper(VFBAR,RBAR,IVBAR,CBAR,KBAR,LABAR,QBAR,YBAR, BETTA,B,PAI,H,PHIzero,PHI1,PHI2,THETA,MYYPS,MYZ,INHABIT,RRA,CONSxhr40)
% We get nice values of EVF by setting AA app. equal to VF.
% The value of the expected value function raised to the power 1-PHI3
% Also we check bounds on other variables
% % Adding PHI3 to params. Note that PHI3 only affects the value function in
% % steady state, hence the value we assign to PHI3 is irrelevant
% PHI3 = -100;
info=0;
AA = NaN;
EVFBAR = NaN;
PHI3 = NaN;
negVf = NaN;
MYZSTAR = MYYPS^(THETA/(1-THETA))*MYZ;
% The wage level
WBAR = PHIzero*(1-H)^(-PHI1)/LABAR;
RRAc = RRA;
if INHABIT == 1
PHI3 = (RRAc - PHI2/((1-B*MYZSTAR^-1)/(1-BETTA*B)+PHI2/PHI1*WBAR*(1-H)/CBAR))/((1-PHI2)/((1-B*MYZSTAR^-1)/(1-BETTA*B)-(CBAR-B*CBAR*MYZSTAR^-1)^PHI2/((1-BETTA*B)*CBAR)+WBAR*(1-H)/CBAR*(1-PHI2)/(1-PHI1)));
else
PHI3 = (RRAc - PHI2/(1-B*MYZSTAR^-1+PHI2/PHI1*WBAR*(1-H)/CBAR))/((1-PHI2)/(1-B*MYZSTAR^-1-(CBAR-B*CBAR*MYZSTAR^-1)^PHI2/((1-BETTA*B)*CBAR)+WBAR*(1-H)/CBAR*(1-PHI2)/(1-PHI1)));
end
if abs(PHI3) > 30000
disp('abs of PHI3 exceeds 30000')
info=1;
return
end
if CONSxhr40 > 1
info=1;
return
end
if VFBAR < 0
AA = -VFBAR;
EVFBAR = (-VFBAR/AA)^(1-PHI3);
negVf = 1;
else
AA = VFBAR;
EVFBAR = (VFBAR/AA)^(1-PHI3);
negVf = -1;
disp('Positive Value Function');
end
if RBAR < 1 || IVBAR < 0 || CBAR < 0 || KBAR < 0 || PAI < 1 || H < 0 || H > 1 || QBAR < 0 || YBAR < 0
info = 1;
end
end

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tests/estimation/method_of_moments/AnScho_MoM.mod → tests/estimation/method_of_moments/AnScho/AnScho_MoM.mod
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@ -1,8 +1,8 @@
% DSGE model used in replication files of
% DSGE model used in replication files of
% An, Sungbae and Schorfheide, Frank, (2007), Bayesian Analysis of DSGE Models, Econometric Reviews, 26, issue 2-4, p. 113-172.
% Adapted by Willi Mutschler (@wmutschl, willi@mutschler.eu)
% =========================================================================
% Copyright (C) 2020 Dynare Team
% Copyright (C) 2020-2021 Dynare Team
%
% This file is part of Dynare.
%
@ -203,28 +203,33 @@ end
@#for mommethod in ["GMM", "SMM"]
method_of_moments(
% Necessery options
mom_method = @{mommethod} % method of moments method; possible values: GMM|SMM
, datafile = 'AnScho_MoM_data_@{orderApp}.mat' % name of filename with data
% Necessery options
mom_method = @{mommethod} % method of moments method; possible values: GMM|SMM
, datafile = 'AnScho_MoM_data_@{orderApp}.mat' % name of filename with data
% Options for both GMM and SMM
% Options for both GMM and SMM
% , bartlett_kernel_lag = 20 % bandwith in optimal weighting matrix
, order = @{orderApp} % order of Taylor approximation in perturbation
% , penalized_estimator % use penalized optimization
% , penalized_estimator % include deviation from prior mean as additional moment restriction and use prior precision as weight
, pruning % use pruned state space system at higher-order
% , verbose % display and store intermediate estimation results
, weighting_matrix = ['optimal'] % weighting matrix in moments distance objective function; possible values: OPTIMAL|IDENTITY_MATRIX|DIAGONAL|filename
, additional_optimizer_steps = [4] % vector of numbers for the iterations in the 2-step feasible method of moments
% , prefilter=0 % demean each data series by its empirical mean and use centered moments
%
% Options for SMM
, 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']
%, weighting_matrix_scaling_factor=1 % scaling of weighting matrix in objective function
, se_tolx=1e-6 % step size for numerical computation of standard errors
% Options for SMM
% , burnin=500 % number of periods dropped at beginning of simulation
% , bounded_shock_support % trim shocks in simulation to +- 2 stdev
% , drop = 500 % number of periods dropped at beginning of simulation
% , seed = 24051986 % seed used in simulations
% , simulation_multiple = 5 % multiple of the data length used for simulation
%
% General options
%, dirname = 'MM' % directory in which to store estimation output
% Options for GMM
@#if mommethod == "GMM"
, analytic_standard_errors % compute standard errors using analytical derivatives
@#endif
% General options
% , dirname = 'MM' % directory in which to store estimation output
% , graph_format = EPS % specify the file format(s) for graphs saved to disk
% , nodisplay % do not display the graphs, but still save them to disk
% , nograph % do not create graphs (which implies that they are not saved to the disk nor displayed)
@ -232,44 +237,50 @@ end
% , plot_priors = 1 % control plotting of priors
% , prior_trunc = 1e-10 % probability of extreme values of the prior density that is ignored when computing bounds for the parameters
% , TeX % print TeX tables and graphics
%
% Data and model options
%, first_obs = 501 % number of first observation
% , logdata % if loglinear is set, this option is necessary if the user provides data already in logs, otherwise the log transformation will be applied twice (this may result in complex data)
% , loglinear % computes a log-linear approximation of the model instead of a linear approximation
, nobs = 250 % number of observations
% , xls_sheet = willi % name of sheet with data in Excel
% Data and model options
% , first_obs = 501 % number of first observation
% , logdata % if data is already in logs
, nobs = 250 % number of observations
% , prefilter=0 % demean each data series by its empirical mean and use centered moments
% , xls_sheet = data % name/number of sheet with data in Excel
% , xls_range = B2:D200 % range of data in Excel sheet
%
% Optimization options that can be set by the user in the mod file, otherwise default values are provided
% , analytic_derivation % uses analytic derivatives to compute standard errors for GMM
%, huge_number=1D10 % value for replacing the infinite bounds on parameters by finite numbers. Used by some optimizers for numerical reasons
, mode_compute = @{optimizer} % specifies the optimizer for minimization of moments distance, note that by default there is a new optimizer
%, optim = ('TolFun', 1e-5
% ,'TolX', 1e-6
% ) % 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
% , tolf = 1e-5 % convergence criterion on function value for numerical differentiation
% , tolx = 1e-6 % convergence criterion on funciton input for numerical differentiation
%
% % Numerical algorithms options
% Optimization options that can be set by the user in the mod file, otherwise default values are provided
% , huge_number=1e7 % value for replacing the infinite bounds on parameters by finite numbers. Used by some optimizers for numerical reasons
, mode_compute = @{optimizer} % specifies the optimizer for minimization of moments distance
, additional_optimizer_steps = [1] % vector of additional mode-finders run after mode_compute
% optim: a list of NAME and VALUE pairs to set options for the optimization routines. Available options depend on mode_compute, some exemplary common options:
, optim = ('TolFun' , 1e-6 % termination tolerance on the function value, a positive scalar
,'TolX' , 1e-6 % termination tolerance on x, a positive scalar
,'MaxIter' , 3000 % maximum number of iterations allowed, a positive integer
,'MaxFunEvals' , 1D6 % maximum number of function evaluations allowed, a positive integer
% ,'UseParallel' , 1 % when true (and supported by optimizer) solver estimates gradients in parallel (using Matlab/Octave's parallel toolbox)
% ,'Jacobian' , 'off' % when 'off' gradient-based solvers approximate Jacobian using finite differences; for GMM we can also pass the analytical Jacobian to gradient-based solvers by setting this 'on'
)
, silent_optimizer % run minimization of moments distance silently without displaying results or saving files in between
% Numerical algorithms options
% , aim_solver % Use AIM algorithm to compute perturbation approximation
% , k_order_solver % use k_order_solver in higher order perturbation approximations
% , dr=default % method used to compute the decision rule; possible values are DEFAULT, CYCLE_REDUCTION, LOGARITHMIC_REDUCTION
% , dr_cycle_reduction_tol = 1e-7 % convergence criterion used in the cycle reduction algorithm
% , dr_logarithmic_reduction_tol = 1e-12 % convergence criterion used in the logarithmic reduction algorithm
% , dr_logarithmic_reduction_maxiter = 100 % maximum number of iterations used in the logarithmic reduction algorithm
% , dr_logarithmic_reduction_tol = 1e-12 % convergence criterion used in the cycle reduction algorithm
% , k_order_solver % use k_order_solver in higher order perturbation approximations
% , lyapunov = DEFAULT % algorithm used to solve lyapunov equations; possible values are DEFAULT, FIXED_POINT, DOUBLING, SQUARE_ROOT_SOLVER
% , lyapunov_complex_threshold = 1e-15 % complex block threshold for the upper triangular matrix in symmetric Lyapunov equation solver
% , lyapunov_fixed_point_tol = 1e-10 % convergence criterion used in the fixed point Lyapunov solver
% , lyapunov_doubling_tol = 1e-16 % convergence criterion used in the doubling algorithm
% , sylvester = default % algorithm to solve Sylvester equation; possible values are DEFAULT, FIXED_POINT
% , sylvester_fixed_point_tol = 1e-12 % convergence criterion used in the fixed point Sylvester solver
% , qz_criterium = 0.999999 % value used to split stable from unstable eigenvalues in reordering the Generalized Schur decomposition used for solving first order problems [IS THIS CORRET @wmutschl]
% , qz_criterium = 0.999999 % value used to split stable from unstable eigenvalues in reordering the Generalized Schur decomposition used for solving first order problems
% , qz_zero_threshold = 1e-6 % value used to test if a generalized eigenvalue is 0/0 in the generalized Schur decomposition
@#if mommethod == "GMM"
, analytic_standard_errors
@#endif
% , schur_vec_tol=1e-11 % tolerance level used to find nonstationary variables in Schur decomposition of the transition matrix
% , mode_check % plot the target function for values around the computed minimum for each estimated parameter in turn
% , mode_check_neighbourhood_size = 5 % width of the window (expressed in percentage deviation) around the computed minimum to be displayed on the diagnostic plots
% , mode_check_symmetric_plots=1 % ensure that the check plots are symmetric around the minimum
% , mode_check_number_of_points = 20 % number of points around the minimum where the target function is evaluated (for each parameter)
);
@#endfor

0
tests/estimation/method_of_moments/RBC_Andreasen_Data_2.mat → tests/estimation/method_of_moments/RBC/RBC_Andreasen_Data_2.mat
View File

230
tests/estimation/method_of_moments/RBC/RBC_MoM_Andreasen.mod Normal file
View File

@ -0,0 +1,230 @@
% Tests SMM and GMM routines
%
% 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/>.
% =========================================================================
% Define testscenario
@#define orderApp = 2
@#define estimParams = 1
% Note that we will set the numerical optimization tolerance levels very large to speed up the testsuite
@#define optimizer = 13
@#include "RBC_MoM_common.inc"
shocks;
var u_a; stderr 0.0072;
end;
varobs c iv n;
@#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
% Simulate data
%stoch_simul(order=@{orderApp},pruning,nodisplay,nomoments,periods=500);
%save('RBC_MoM_data_@{orderApp}.mat', options_.varobs{:} );
%pause(1);
estimated_params_init(use_calibration);
end;
%--------------------------------------------------------------------------
% 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);
c*c(-3);
n*n(-3);
iv*iv(-3);
c*c(-5);
n*n(-5);
iv*iv(-5);
end;
% get indices in declaration order
ic = strmatch('c', M_.endo_names,'exact');
iiv = strmatch('iv', M_.endo_names,'exact');
in = strmatch('n', M_.endo_names,'exact');
% first entry: number of variable in declaration order
% second entry: lag
% third entry: power
matched_moments_ = {
[ic ] [0 ], [1 ];
[in ] [0 ], [1 ];
[iiv ] [0 ], [1 ];
[ic ic ] [0 0], [1 1];
[ic iiv] [0 0], [1 1];
%[ic in ] [0 0], [1 1];
%[iiv ic ] [0 0], [1 1];
[in iiv] [0 0], [1 1];
[iiv iiv] [0 0], [1 1];
[ic in] [0 0], [1 1];
%[in iiv] [0 0], [1 1];
[in in ] [0 0], [1 1];
[ic ic ] [0 -1], [1 1];
[in in ] [0 -1], [1 1];
[iiv iiv] [0 -1], [1 1];
[ic ic ] [0 -3], [1 1];
[in in ] [0 -3], [1 1];
[iiv iiv] [0 -3], [1 1];
[ic ic ] [0 -5], [1 1];
[in in ] [0 -5], [1 1];
[iiv iiv] [0 -5], [1 1];
};
if ~isequal(M_.matched_moments,matched_moments_)
error('Translation to matched_moments-block failed')
end
method_of_moments(
% Necessery options
mom_method = GMM % method of moments method; possible values: GMM|SMM
, datafile = 'RBC_Andreasen_Data_2.mat' % name of filename with data
% Options for both GMM and SMM
% , bartlett_kernel_lag = 20 % bandwith in optimal weighting matrix
, order = @{orderApp} % order of Taylor approximation in perturbation
% , penalized_estimator % include deviation from prior mean as additional moment restriction and use prior precision as weight
% , pruning % use pruned state space system at higher-order
% , verbose % display and store intermediate estimation results
, weighting_matrix = ['DIAGONAL','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']
% , weighting_matrix_scaling_factor=1 % scaling of weighting matrix in objective function
, se_tolx=1e-6 % step size for numerical computation of standard errors
% Options for SMM
% , burnin=500 % number of periods dropped at beginning of simulation
% , bounded_shock_support % trim shocks in simulation to +- 2 stdev
% , seed = 24051986 % seed used in simulations
% , simulation_multiple = 5 % multiple of the data length used for simulation
% Options for GMM
% , analytic_standard_errors % compute standard errors using analytical derivatives
% General options
% , dirname = 'MM' % directory in which to store estimation output
% , graph_format = EPS % specify the file format(s) for graphs saved to disk
% , nodisplay % do not display the graphs, but still save them to disk
% , nograph % do not create graphs (which implies that they are not saved to the disk nor displayed)
% , noprint % do not print stuff to console
% , plot_priors = 1 % control plotting of priors
% , prior_trunc = 1e-10 % probability of extreme values of the prior density that is ignored when computing bounds for the parameters
, TeX % print TeX tables and graphics
% Data and model options
% , first_obs = 501 % number of first observation
% , logdata % if data is already in logs
% , nobs = 250 % number of observations
% , prefilter=0 % demean each data series by its empirical mean and use centered moments
% , xls_sheet = data % name/number of sheet with data in Excel
% , xls_range = B2:D200 % range of data in Excel sheet
% Optimization options that can be set by the user in the mod file, otherwise default values are provided
% , huge_number=1e7 % value for replacing the infinite bounds on parameters by finite numbers. Used by some optimizers for numerical reasons
, mode_compute = 3 % specifies the optimizer for minimization of moments distance
, additional_optimizer_steps = [13] % vector of additional mode-finders run after mode_compute
% optim: a list of NAME and VALUE pairs to set options for the optimization routines. Available options depend on mode_compute, some exemplary common options:
, optim = ('TolFun' , 1D-6 % termination tolerance on the function value, a positive scalar
,'TolX' , 1e-6 % termination tolerance on x, a positive scalar
% ,'MaxIter' , 3000 % maximum number of iterations allowed, a positive integer
% ,'MaxFunEvals' , 1D6 % maximum number of function evaluations allowed, a positive integer
% ,'UseParallel' , 1 % when true (and supported by optimizer) solver estimates gradients in parallel (using Matlab/Octave's parallel toolbox)
% ,'Jacobian' , 'off' % when 'off' gradient-based solvers approximate Jacobian using finite differences; for GMM we can also pass the analytical Jacobian to gradient-based solvers by setting this 'on'
)
% , silent_optimizer % run minimization of moments distance silently without displaying results or saving files in between
% Numerical algorithms options
% , aim_solver % Use AIM algorithm to compute perturbation approximation
% , k_order_solver % use k_order_solver in higher order perturbation approximations
% , dr=default % method used to compute the decision rule; possible values are DEFAULT, CYCLE_REDUCTION, LOGARITHMIC_REDUCTION
% , dr_cycle_reduction_tol = 1e-7 % convergence criterion used in the cycle reduction algorithm
% , dr_logarithmic_reduction_tol = 1e-12 % convergence criterion used in the logarithmic reduction algorithm
% , dr_logarithmic_reduction_maxiter = 100 % maximum number of iterations used in the logarithmic reduction algorithm
% , lyapunov = DEFAULT % algorithm used to solve lyapunov equations; possible values are DEFAULT, FIXED_POINT, DOUBLING, SQUARE_ROOT_SOLVER
% , lyapunov_complex_threshold = 1e-15 % complex block threshold for the upper triangular matrix in symmetric Lyapunov equation solver
% , lyapunov_fixed_point_tol = 1e-10 % convergence criterion used in the fixed point Lyapunov solver
% , lyapunov_doubling_tol = 1e-16 % convergence criterion used in the doubling algorithm
% , sylvester = default % algorithm to solve Sylvester equation; possible values are DEFAULT, FIXED_POINT
% , sylvester_fixed_point_tol = 1e-12 % convergence criterion used in the fixed point Sylvester solver
% , qz_criterium = 0.999999 % value used to split stable from unstable eigenvalues in reordering the Generalized Schur decomposition used for solving first order problems
% , qz_zero_threshold = 1e-6 % value used to test if a generalized eigenvalue is 0/0 in the generalized Schur decomposition
% , schur_vec_tol=1e-11 % tolerance level used to find nonstationary variables in Schur decomposition of the transition matrix
, mode_check % plot the target function for values around the computed minimum for each estimated parameter in turn
% , mode_check_neighbourhood_size = 5 % width of the window (expressed in percentage deviation) around the computed minimum to be displayed on the diagnostic plots
% , mode_check_symmetric_plots=1 % ensure that the check plots are symmetric around the minimum
% , mode_check_number_of_points = 20 % number of points around the minimum where the target function is evaluated (for each parameter)
);

89
tests/estimation/method_of_moments/RBC_MoM_SMM_ME.mod → tests/estimation/method_of_moments/RBC/RBC_MoM_SMM_ME.mod
View File

@ -1,3 +1,5 @@
% =========================================================================
% Copyright (C) 2020-2021 Dynare Team
%
% This file is part of Dynare.
%
@ -137,30 +139,31 @@ end
@#for mommethod in ["SMM"]
method_of_moments(
% Necessery options
mom_method = @{mommethod} % method of moments method; possible values: GMM|SMM
, datafile = 'RBC_MoM_data_@{orderApp}.mat' % name of filename with data
% Necessery options
mom_method = @{mommethod} % method of moments method; possible values: GMM|SMM
, datafile = 'RBC_MoM_data_@{orderApp}.mat' % name of filename with data
% Options for both GMM and SMM
% Options for both GMM and SMM
% , bartlett_kernel_lag = 20 % bandwith in optimal weighting matrix
, order = @{orderApp} % order of Taylor approximation in perturbation
% , penalized_estimator % use penalized optimization
% , penalized_estimator % include deviation from prior mean as additional moment restriction and use prior precision as weight
, pruning % use pruned state space system at higher-order
% , verbose % display and store intermediate estimation results
, weighting_matrix = ['identity_matrix'] % weighting matrix in moments distance objective function; possible values: OPTIMAL|IDENTITY_MATRIX|DIAGONAL|filename
, weighting_matrix_scaling_factor = 10
, burnin=250
%, additional_optimizer_steps = [4] % vector of additional mode-finders run after mode_compute
% , prefilter=0 % demean each data series by its empirical mean and use centered moments
%
% Options for SMM
, weighting_matrix = ['identity_matrix'] % 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']
, weighting_matrix_scaling_factor=10 % scaling of weighting matrix in objective function
% , se_tolx=1e-6 % step size for numerical computation of standard errors
% Options for SMM
, burnin=250 % number of periods dropped at beginning of simulation
% , bounded_shock_support % trim shocks in simulation to +- 2 stdev
% , drop = 500 % number of periods dropped at beginning of simulation
% , seed = 24051986 % seed used in simulations
% , simulation_multiple = 5 % multiple of the data length used for simulation
%
% General options
%, dirname = 'MM' % directory in which to store estimation output
% Options for GMM
% , analytic_standard_errors % compute standard errors using analytical derivatives
% General options
% , dirname = 'MM' % directory in which to store estimation output
% , graph_format = EPS % specify the file format(s) for graphs saved to disk
% , nodisplay % do not display the graphs, but still save them to disk
% , nograph % do not create graphs (which implies that they are not saved to the disk nor displayed)
@ -168,41 +171,49 @@ end
% , plot_priors = 1 % control plotting of priors
% , prior_trunc = 1e-10 % probability of extreme values of the prior density that is ignored when computing bounds for the parameters
% , TeX % print TeX tables and graphics
%
% Data and model options
%, first_obs = 501 % number of first observation
% , logdata % if loglinear is set, this option is necessary if the user provides data already in logs, otherwise the log transformation will be applied twice (this may result in complex data)
% , loglinear % computes a log-linear approximation of the model instead of a linear approximation
%, nobs = 500 % number of observations
% , xls_sheet = willi % name of sheet with data in Excel
% Data and model options
% , first_obs = 501 % number of first observation
% , logdata % if data is already in logs
% , nobs = 250 % number of observations
% , prefilter=0 % demean each data series by its empirical mean and use centered moments
% , xls_sheet = data % name/number of sheet with data in Excel
% , xls_range = B2:D200 % range of data in Excel sheet
%
% Optimization options that can be set by the user in the mod file, otherwise default values are provided
% , analytic_derivation % uses analytic derivatives to compute standard errors for GMM
%, huge_number=1D10 % value for replacing the infinite bounds on parameters by finite numbers. Used by some optimizers for numerical reasons
, mode_compute = @{optimizer} % specifies the optimizer for minimization of moments distance, note that by default there is a new optimizer
%, optim = ('TolFun', 1e-3
% ,'TolX', 1e-5
% ) % 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
% , tolf = 1e-5 % convergence criterion on function value for numerical differentiation
% , tolx = 1e-6 % convergence criterion on funciton input for numerical differentiation
%
% % Numerical algorithms options
% Optimization options that can be set by the user in the mod file, otherwise default values are provided
% , huge_number=1e7 % value for replacing the infinite bounds on parameters by finite numbers. Used by some optimizers for numerical reasons
, mode_compute = @{optimizer} % specifies the optimizer for minimization of moments distance
%, additional_optimizer_steps = [1 2 3 4] % vector of additional mode-finders run after mode_compute
% optim: a list of NAME and VALUE pairs to set options for the optimization routines. Available options depend on mode_compute, some exemplary common options:
% , optim = ('TolFun' , 1e-6 % termination tolerance on the function value, a positive scalar
% ,'TolX' , 1e-6 % termination tolerance on x, a positive scalar
% ,'MaxIter' , 3000 % maximum number of iterations allowed, a positive integer
% ,'MaxFunEvals' , 1D6 % maximum number of function evaluations allowed, a positive integer
% ,'UseParallel' , 1 % when true (and supported by optimizer) solver estimates gradients in parallel (using Matlab/Octave's parallel toolbox)
% ,'Jacobian' , 'off' % when 'off' gradient-based solvers approximate Jacobian using finite differences; for GMM we can also pass the analytical Jacobian to gradient-based solvers by setting this 'on'
% )
% , silent_optimizer % run minimization of moments distance silently without displaying results or saving files in between
% Numerical algorithms options
% , aim_solver % Use AIM algorithm to compute perturbation approximation
% , k_order_solver % use k_order_solver in higher order perturbation approximations
% , dr=default % method used to compute the decision rule; possible values are DEFAULT, CYCLE_REDUCTION, LOGARITHMIC_REDUCTION
% , dr_cycle_reduction_tol = 1e-7 % convergence criterion used in the cycle reduction algorithm
% , dr_logarithmic_reduction_tol = 1e-12 % convergence criterion used in the logarithmic reduction algorithm
% , dr_logarithmic_reduction_maxiter = 100 % maximum number of iterations used in the logarithmic reduction algorithm
% , dr_logarithmic_reduction_tol = 1e-12 % convergence criterion used in the cycle reduction algorithm
% , k_order_solver % use k_order_solver in higher order perturbation approximations
% , lyapunov = DEFAULT % algorithm used to solve lyapunov equations; possible values are DEFAULT, FIXED_POINT, DOUBLING, SQUARE_ROOT_SOLVER
% , lyapunov_complex_threshold = 1e-15 % complex block threshold for the upper triangular matrix in symmetric Lyapunov equation solver
% , lyapunov_fixed_point_tol = 1e-10 % convergence criterion used in the fixed point Lyapunov solver
% , lyapunov_doubling_tol = 1e-16 % convergence criterion used in the doubling algorithm
% , sylvester = default % algorithm to solve Sylvester equation; possible values are DEFAULT, FIXED_POINT
% , sylvester_fixed_point_tol = 1e-12 % convergence criterion used in the fixed point Sylvester solver
% , qz_criterium = 0.999999 % value used to split stable from unstable eigenvalues in reordering the Generalized Schur decomposition used for solving first order problems [IS THIS CORRET @wmutschl]
% , qz_criterium = 0.999999 % value used to split stable from unstable eigenvalues in reordering the Generalized Schur decomposition used for solving first order problems
% , qz_zero_threshold = 1e-6 % value used to test if a generalized eigenvalue is 0/0 in the generalized Schur decomposition
% , schur_vec_tol=1e-11 % tolerance level used to find nonstationary variables in Schur decomposition of the transition matrix
% , mode_check % plot the target function for values around the computed minimum for each estimated parameter in turn
% , mode_check_neighbourhood_size = 5 % width of the window (expressed in percentage deviation) around the computed minimum to be displayed on the diagnostic plots
% , mode_check_symmetric_plots=1 % ensure that the check plots are symmetric around the minimum
% , mode_check_number_of_points = 20 % number of points around the minimum where the target function is evaluated (for each parameter)
);
@#endfor

18
tests/estimation/method_of_moments/RBC_MoM_common.inc → tests/estimation/method_of_moments/RBC/RBC_MoM_common.inc
View File

@ -2,7 +2,23 @@
% Andreasen, Fernández-Villaverde, Rubio-Ramírez (2018): "The Pruned State-Space System for Non-Linear DSGE Models: Theory and Empirical Applications", Review of Economic Studies, 85(1):1-49.
% Adapted by Willi Mutschler (@wmutschl, willi@mutschler.eu)
% =========================================================================
% Copyright (C) 2020 Dynare Team
% 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/>.
% =========================================================================
var k $K$
c $C$

146
tests/estimation/method_of_moments/RBC/RBC_MoM_optimizer.mod Normal file
View File

@ -0,0 +1,146 @@
% Test optimizers
%
% 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/>.
% =========================================================================
% TO DO
% [ ] fix optimizers 11 and 12;
% note that 12 and 102 require GADS_Toolbox which is not available on servers, but need to be tested locally
% Define testscenario
@#define orderApp = 2
% Note that we will set the numerical optimization tolerance levels very large to speed up the testsuite
@#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;
% reduce options to speed up testsuite
options_.newrat.maxiter = 10;
options_.newrat.tolerance.f = 1e-2;
options_.newrat.tolerance.f_analytic = 1e-2;
options_.mh_jscale = 0.6;
options_.gmhmaxlik.iterations=1;
options_.gmhmaxlik.number=2000;
options_.gmhmaxlik.nclimb=2000;
options_.gmhmaxlik.nscale=2000;
options_.gmhmaxlik.target=0.5;
options_.solveopt.MaxIter=300;
options_.solveopt.LBGradientStep=1e-3;
options_.solveopt.TolFun = 1e-3;
options_.solveopt.TolX = 1e-3;
options_.solveopt.TolXConstraint=1e-3;
@#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;
@#define OPTIMIZERS = [1, 2, 3, 4, 5, 7, 8, 9, 10, 13, 101]
@#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;
@#define OPTIMIZERS = [1, 2, 3, 4, 7, 8, 9, 10, 13, 101]
@#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;
@#define OPTIMIZERS = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 13, 101]
@#endif
estimated_params_init(use_calibration);
end;
@#for optimizer in OPTIMIZERS
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 = @{orderApp} % 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']
, nograph % do not create graphs (which implies that they are not saved to the disk nor displayed)
, mode_compute = @{optimizer} % specifies the optimizer for minimization of moments distance
@#if optimizer == 102
, optim = ('TolFun' , 1D-3 % termination tolerance on the function value, a positive scalar
,'MaxIter' , 300 % maximum number of iterations allowed, a positive integer
,'MaxFunEvals' , 1D3 % maximum number of function evaluations allowed, a positive integer
)
@#else
, optim = ('TolFun' , 1D-3 % termination tolerance on the function value, a positive scalar
,'TolX' , 1e-3 % termination tolerance on x, a positive scalar
,'MaxIter' , 300 % maximum number of iterations allowed, a positive integer
,'MaxFunEvals' , 1D3 % maximum number of function evaluations allowed, a positive integer
)
@#endif
%, silent_optimizer % run minimization of moments distance silently without displaying results or saving files in between
);
@#endfor
@#endfor

89
tests/estimation/method_of_moments/RBC_MoM_prefilter.mod → tests/estimation/method_of_moments/RBC/RBC_MoM_prefilter.mod
View File

@ -1,6 +1,6 @@
% Tests SMM and GMM routines with prefilter, explicit initialization, and estimated_params_init(use_calibration);
%
% Copyright (C) 2020 Dynare Team
% Copyright (C) 2020-2021 Dynare Team
%
% This file is part of Dynare.
%
@ -110,31 +110,31 @@ save('test_matrix.mat','weighting_matrix')
@#for mommethod in ["GMM", "SMM"]
method_of_moments(
% Necessery options
mom_method = @{mommethod} % method of moments method; possible values: GMM|SMM
, datafile = 'RBC_MoM_data_@{orderApp}.mat' % name of filename with data
% Necessery options
mom_method = @{mommethod} % method of moments method; possible values: GMM|SMM
, datafile = 'RBC_MoM_data_@{orderApp}.mat' % name of filename with data
% Options for both GMM and SMM
% Options for both GMM and SMM
% , bartlett_kernel_lag = 20 % bandwith in optimal weighting matrix
, order = @{orderApp} % order of Taylor approximation in perturbation
% , penalized_estimator % use penalized optimization
% , penalized_estimator % include deviation from prior mean as additional moment restriction and use prior precision as weight
, pruning % use pruned state space system at higher-order
% , verbose % display and store intermediate estimation results
% , weighting_matrix = 'test_matrix.mat' % weighting matrix in moments distance objective function; possible values: OPTIMAL|IDENTITY_MATRIX|DIAGONAL|filename
, weighting_matrix =['test_matrix.mat','optimal']
%, weighting_matrix = optimal % weighting matrix in moments distance objective function; possible values: OPTIMAL|IDENTITY_MATRIX|DIAGONAL|filename
%, additional_optimizer_steps = [4] % vector of additional mode-finders run after mode_compute
, prefilter=1 % demean each data series by its empirical mean and use centered moments
, se_tolx=1e-5
%
% Options for SMM
, weighting_matrix = ['test_matrix.mat','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']
%, weighting_matrix_scaling_factor=1 % scaling of weighting matrix in objective function
, se_tolx=1e-5 % step size for numerical computation of standard errors
% Options for SMM
, burnin=500 % number of periods dropped at beginning of simulation
% , bounded_shock_support % trim shocks in simulation to +- 2 stdev
, burnin = 500 % number of periods dropped at beginning of simulation
% , seed = 24051986 % seed used in simulations
% , simulation_multiple = 5 % multiple of the data length used for simulation
%
% General options
%, dirname = 'MM' % directory in which to store estimation output
% Options for GMM
% , analytic_standard_errors % compute standard errors using analytical derivatives
% General options
% , dirname = 'MM' % directory in which to store estimation output
% , graph_format = EPS % specify the file format(s) for graphs saved to disk
% , nodisplay % do not display the graphs, but still save them to disk
% , nograph % do not create graphs (which implies that they are not saved to the disk nor displayed)
@ -142,38 +142,49 @@ save('test_matrix.mat','weighting_matrix')
% , plot_priors = 1 % control plotting of priors
% , prior_trunc = 1e-10 % probability of extreme values of the prior density that is ignored when computing bounds for the parameters
% , TeX % print TeX tables and graphics
%
% Data and model options
%, first_obs = 501 % number of first observation
% , logdata % if loglinear is set, this option is necessary if the user provides data already in logs, otherwise the log transformation will be applied twice (this may result in complex data)
% , loglinear % computes a log-linear approximation of the model instead of a linear approximation
%, nobs = 500 % number of observations
% , xls_sheet = willi % name of sheet with data in Excel
% Data and model options
% , first_obs = 501 % number of first observation
% , logdata % if data is already in logs
, nobs = 250 % number of observations
, prefilter=1 % demean each data series by its empirical mean and use centered moments
% , xls_sheet = data % name/number of sheet with data in Excel
% , xls_range = B2:D200 % range of data in Excel sheet
%
% Optimization options that can be set by the user in the mod file, otherwise default values are provided
% , analytic_derivation % uses analytic derivatives to compute standard errors for GMM
%, huge_number=1D10 % value for replacing the infinite bounds on parameters by finite numbers. Used by some optimizers for numerical reasons
, mode_compute = @{optimizer} % specifies the optimizer for minimization of moments distance, note that by default there is a new optimizer
%, optim = ('TolFun', 1e-3
% ,'TolX', 1e-5
% ) % 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
%
% % Numerical algorithms options
% Optimization options that can be set by the user in the mod file, otherwise default values are provided
% , huge_number=1e7 % value for replacing the infinite bounds on parameters by finite numbers. Used by some optimizers for numerical reasons
, mode_compute = @{optimizer} % specifies the optimizer for minimization of moments distance
%, additional_optimizer_steps = [7] % vector of additional mode-finders run after mode_compute
% optim: a list of NAME and VALUE pairs to set options for the optimization routines. Available options depend on mode_compute, some exemplary common options:
% , optim = ('TolFun' , 1e-6 % termination tolerance on the function value, a positive scalar
% ,'TolX' , 1e-6 % termination tolerance on x, a positive scalar
% ,'MaxIter' , 3000 % maximum number of iterations allowed, a positive integer
% ,'MaxFunEvals' , 1D6 % maximum number of function evaluations allowed, a positive integer
% ,'UseParallel' , 1 % when true (and supported by optimizer) solver estimates gradients in parallel (using Matlab/Octave's parallel toolbox)
% ,'Jacobian' , 'off' % when 'off' gradient-based solvers approximate Jacobian using finite differences; for GMM we can also pass the analytical Jacobian to gradient-based solvers by setting this 'on'
% )
% , silent_optimizer % run minimization of moments distance silently without displaying results or saving files in between
% Numerical algorithms options
% , aim_solver % Use AIM algorithm to compute perturbation approximation
% , k_order_solver % use k_order_solver in higher order perturbation approximations
% , dr=default % method used to compute the decision rule; possible values are DEFAULT, CYCLE_REDUCTION, LOGARITHMIC_REDUCTION
% , dr_cycle_reduction_tol = 1e-7 % convergence criterion used in the cycle reduction algorithm
% , dr_logarithmic_reduction_tol = 1e-12 % convergence criterion used in the logarithmic reduction algorithm
% , dr_logarithmic_reduction_maxiter = 100 % maximum number of iterations used in the logarithmic reduction algorithm
% , dr_logarithmic_reduction_tol = 1e-12 % convergence criterion used in the cycle reduction algorithm
% , k_order_solver % use k_order_solver in higher order perturbation approximations
% , lyapunov = DEFAULT % algorithm used to solve lyapunov equations; possible values are DEFAULT, FIXED_POINT, DOUBLING, SQUARE_ROOT_SOLVER
% , lyapunov_complex_threshold = 1e-15 % complex block threshold for the upper triangular matrix in symmetric Lyapunov equation solver
% , lyapunov_fixed_point_tol = 1e-10 % convergence criterion used in the fixed point Lyapunov solver
% , lyapunov_doubling_tol = 1e-16 % convergence criterion used in the doubling algorithm
% , sylvester = default % algorithm to solve Sylvester equation; possible values are DEFAULT, FIXED_POINT
% , sylvester_fixed_point_tol = 1e-12 % convergence criterion used in the fixed point Sylvester solver
% , qz_criterium = 0.999999 % value used to split stable from unstable eigenvalues in reordering the Generalized Schur decomposition used for solving first order problems [IS THIS CORRET @wmutschl]
% , qz_criterium = 0.999999 % value used to split stable from unstable eigenvalues in reordering the Generalized Schur decomposition used for solving first order problems
% , qz_zero_threshold = 1e-6 % value used to test if a generalized eigenvalue is 0/0 in the generalized Schur decomposition
% , schur_vec_tol=1e-11 % tolerance level used to find nonstationary variables in Schur decomposition of the transition matrix
% , mode_check % plot the target function for values around the computed minimum for each estimated parameter in turn
% , mode_check_neighbourhood_size = 5 % width of the window (expressed in percentage deviation) around the computed minimum to be displayed on the diagnostic plots
% , mode_check_symmetric_plots=1 % ensure that the check plots are symmetric around the minimum
% , mode_check_number_of_points = 20 % number of points around the minimum where the target function is evaluated (for each parameter)
);
@#endfor

40
tests/estimation/method_of_moments/RBC/RBC_MoM_steady_helper.m Normal file
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@ -0,0 +1,40 @@
% =========================================================================
% 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/>.
% =========================================================================
function [N, info]= RBC_MoM_steady_helper(THETA,ETAl,ETAc,BETTA,B,C_O_N,W)
info=0;
if ~isreal(C_O_N)
info=1;
N=NaN;
return;
end
if ETAc == 1 && ETAl == 1
N = (1-BETTA*B)*(C_O_N*(1-B))^-1*W/THETA/(1+(1-BETTA*B)*(C_O_N*(1-B))^-1*W/THETA);
else
% No closed-form solution use a fixed-point algorithm
N0 = 1/3;
try
[N, ~, exitflag] = fsolve(@(N) THETA*(1-N)^(-ETAl)*N^ETAc - (1-BETTA*B)*(C_O_N*(1-B))^(-ETAc)*W, N0,optimset('Display','off','TolX',1e-12,'TolFun',1e-12));
if exitflag<1
info=1;
end
catch
N=NaN;
info=1;
end
end

227
tests/estimation/method_of_moments/RBC_MoM_Andreasen.mod
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@ -1,227 +0,0 @@
% Tests SMM and GMM routines
%
% Copyright (C) 2020 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/>.
% =========================================================================
% Define testscenario
@#define orderApp = 2
@#define estimParams = 1
% Note that we will set the numerical optimization tolerance levels very large to speed up the testsuite
@#define optimizer = 13
@#include "RBC_MoM_common.inc"
shocks;
var u_a; stderr 0.0072;
end;
varobs c iv n;
@#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
% Simulate data
%stoch_simul(order=@{orderApp},pruning,nodisplay,nomoments,periods=500);
%save('RBC_MoM_data_@{orderApp}.mat', options_.varobs{:} );
%pause(1);
estimated_params_init(use_calibration);
end;
%--------------------------------------------------------------------------
% 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);
c*c(-3);
n*n(-3);
iv*iv(-3);
c*c(-5);
n*n(-5);
iv*iv(-5);
end;
% get indices in declaration order
ic = strmatch('c', M_.endo_names,'exact');
iiv = strmatch('iv', M_.endo_names,'exact');
in = strmatch('n', M_.endo_names,'exact');
% first entry: number of variable in declaration order
% second entry: lag
% third entry: power
matched_moments_ = {
[ic ] [0 ], [1 ];
[in ] [0 ], [1 ];
[iiv ] [0 ], [1 ];
[ic ic ] [0 0], [1 1];
[ic iiv] [0 0], [1 1];
%[ic in ] [0 0], [1 1];
%[iiv ic ] [0 0], [1 1];
[in iiv] [0 0], [1 1];
[iiv iiv] [0 0], [1 1];
[ic in] [0 0], [1 1];
%[in iiv] [0 0], [1 1];
[in in ] [0 0], [1 1];
[ic ic ] [0 -1], [1 1];
[in in ] [0 -1], [1 1];
[iiv iiv] [0 -1], [1 1];
[ic ic ] [0 -3], [1 1];
[in in ] [0 -3], [1 1];
[iiv iiv] [0 -3], [1 1];
[ic ic ] [0 -5], [1 1];
[in in ] [0 -5], [1 1];
[iiv iiv] [0 -5], [1 1];
};
if ~isequal(M_.matched_moments,matched_moments_)
error('Translation to matched_moments-block failed')
end
method_of_moments(
% Necessery options
mom_method = GMM % method of moments method; possible values: GMM|SMM
, datafile = 'RBC_Andreasen_Data_2.mat' % name of filename with data
% Options for both GMM and SMM
%, bartlett_kernel_lag = 20 % bandwith in optimal weighting matrix
, order = @{orderApp} % order of Taylor approximation in perturbation
%, penalized_estimator % use penalized optimization
%, pruning % use pruned state space system at higher-order
%, verbose % display and store intermediate estimation results
, weighting_matrix = ['DIAGONAL','OPTIMAL'] % weighting matrix in moments distance objective function; possible values: OPTIMAL|IDENTITY_MATRIX|DIAGONAL|filename
%, weighting_matrix_scaling_factor=1
, additional_optimizer_steps = [13] % vector of additional mode-finders run after mode_compute
%, prefilter=0 % demean each data series by its empirical mean and use centered moments
%
% Options for SMM
%, bounded_shock_support % trim shocks in simulation to +- 2 stdev
%, drop = 500 % number of periods dropped at beginning of simulation
%, seed = 24051986 % seed used in simulations
%, simulation_multiple = 5 % multiple of the data length used for simulation
%, burnin = 200
%
% General options
%, dirname = 'MM' % directory in which to store estimation output
%, graph_format = EPS % specify the file format(s) for graphs saved to disk
%, nodisplay % do not display the graphs, but still save them to disk
%, nograph % do not create graphs (which implies that they are not saved to the disk nor displayed)
%, noprint % do not print stuff to console
%, plot_priors = 1 % control plotting of priors
%, prior_trunc = 1e-10 % probability of extreme values of the prior density that is ignored when computing bounds for the parameters
, TeX % print TeX tables and graphics
%
% Data and model options
%, first_obs = 501 % number of first observation
%, logdata % if loglinear is set, this option is necessary if the user provides data already in logs, otherwise the log transformation will be applied twice (this may result in complex data)
%, loglinear % computes a log-linear approximation of the model instead of a linear approximation
%, nobs = 50 % number of observations
% , xls_sheet = willi % name of sheet with data in Excel
% , xls_range = B2:D200 % range of data in Excel sheet
%
% Optimization options that can be set by the user in the mod file, otherwise default values are provided
%, analytic_derivation % uses analytic derivatives to compute standard errors for GMM
%, huge_number=1D10 % value for replacing the infinite bounds on parameters by finite numbers. Used by some optimizers for numerical reasons
, mode_compute = 13 % specifies the optimizer for minimization of moments distance, note that by default there is a new optimizer
, optim = ('TolFun', 1D-6
,'TolX', 1D-6
) % 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
, se_tolx = 1e-6 % convergence criterion on funciton input for numerical differentiation
%
% % Numerical algorithms options
%, aim_solver % Use AIM algorithm to compute perturbation approximation
%, dr=DEFAULT % method used to compute the decision rule; possible values are DEFAULT, CYCLE_REDUCTION, LOGARITHMIC_REDUCTION
%, dr_cycle_reduction_tol = 1e-7 % convergence criterion used in the cycle reduction algorithm
%, dr_logarithmic_reduction_maxiter = 100 % maximum number of iterations used in the logarithmic reduction algorithm
%, dr_logarithmic_reduction_tol = 1e-12 % convergence criterion used in the cycle reduction algorithm
%, k_order_solver % use k_order_solver in higher order perturbation approximations
%, lyapunov = DEFAULT % algorithm used to solve lyapunov equations; possible values are DEFAULT, FIXED_POINT, DOUBLING, SQUARE_ROOT_SOLVER
%, lyapunov_complex_threshold = 1e-15 % complex block threshold for the upper triangular matrix in symmetric Lyapunov equation solver
%, lyapunov_fixed_point_tol = 1e-10 % convergence criterion used in the fixed point Lyapunov solver
%, lyapunov_doubling_tol = 1e-16 % convergence criterion used in the doubling algorithm
%, sylvester = default % algorithm to solve Sylvester equation; possible values are DEFAULT, FIXED_POINT
%, sylvester_fixed_point_tol = 1e-12 % convergence criterion used in the fixed point Sylvester solver
%, qz_criterium = 0.999999 % value used to split stable from unstable eigenvalues in reordering the Generalized Schur decomposition used for solving first order problems [IS THIS CORRET @wmutschl]
%, qz_zero_threshold = 1e-6 % value used to test if a generalized eigenvalue is 0/0 in the generalized Schur decomposition
, mode_check
%, mode_check_neighbourhood_size=0.5
%, mode_check_symmetric_plots=0
%, mode_check_number_of_points=25
);

22
tests/estimation/method_of_moments/RBC_MoM_steady_helper.m
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@ -1,22 +0,0 @@
function [N, info]= RBC_MoM_steady_helper(THETA,ETAl,ETAc,BETTA,B,C_O_N,W)
info=0;
if ~isreal(C_O_N)
info=1;
N=NaN;
return;
end
if ETAc == 1 && ETAl == 1
N = (1-BETTA*B)*(C_O_N*(1-B))^-1*W/THETA/(1+(1-BETTA*B)*(C_O_N*(1-B))^-1*W/THETA);
else
% No closed-form solution use a fixed-point algorithm
N0 = 1/3;
try
[N, ~, exitflag] = fsolve(@(N) THETA*(1-N)^(-ETAl)*N^ETAc - (1-BETTA*B)*(C_O_N*(1-B))^(-ETAc)*W, N0,optimset('Display','off','TolX',1e-12,'TolFun',1e-12));
if exitflag<1
info=1;
end
catch
N=NaN;
info=1;
end
end

74
tests/estimation/method_of_moments/RBC_MoM_steadystate.m
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@ -1,74 +0,0 @@
% By Willi Mutschler, September 26, 2016. Email: willi@mutschler.eu
function [ys,params,check] = RBCmodel_steadystate(ys,exo,M_,options_)
%% Step 0: initialize indicator and set options for numerical solver
check = 0;
options = optimset('Display','off','TolX',1e-12,'TolFun',1e-12);
params = M_.params;
%% Step 1: read out parameters to access them with their name
for ii = 1:M_.param_nbr
eval([ M_.param_names{ii} ' = M_.params(' int2str(ii) ');']);
end
%% Step 2: Check parameter restrictions
if ETAc*ETAl<1 % parameter violates restriction (here it is artifical)
check=1; %set failure indicator
return; %return without updating steady states
end
%% Step 3: Enter model equations here
A = 1;
RK = 1/BETTA - (1-DELTA);
K_O_N = (RK/(A*(1-ALFA)))^(-1/ALFA);
if K_O_N <= 0
check = 1; % set failure indicator
return; % return without updating steady states
end
W = A*ALFA*(K_O_N)^(1-ALFA);
IV_O_N = DELTA*K_O_N;
Y_O_N = A*K_O_N^(1-ALFA);
C_O_N = Y_O_N - IV_O_N;
if C_O_N <= 0
check = 1; % set failure indicator
return; % return without updating steady states
end
% The labor level
if ETAc == 1 && ETAl == 1
N = (1-BETTA*B)*(C_O_N*(1-B))^-1*W/THETA/(1+(1-BETTA*B)*(C_O_N*(1-B))^-1*W/THETA);
else
% No closed-form solution use a fixed-point algorithm
N0 = 1/3;
[N,~,exitflag] = fsolve(@(N) THETA*(1-N)^(-ETAl)*N^ETAc - (1-BETTA*B)*(C_O_N*(1-B))^(-ETAc)*W, N0,options);
if exitflag <= 0
check = 1; % set failure indicator
return % return without updating steady states
end
end
C=C_O_N*N;
Y=Y_O_N*N;
IV=IV_O_N*N;
K=K_O_N*N;
LA = (C-B*C)^(-ETAc)-BETTA*B*(C-B*C)^(-ETAc);
k=log(K);
c=log(C);
a=log(A);
iv=log(IV);
y=log(Y);
la=log(LA);
n=log(N);
rk=log(RK);
w=log(W);
%% Step 4: Update parameters and variables
params=NaN(M_.param_nbr,1);
for iter = 1:M_.param_nbr %update parameters set in the file
eval([ 'params(' num2str(iter) ') = ' M_.param_names{iter} ';' ])
end
for ii = 1:M_.orig_endo_nbr %auxiliary variables are set automatically
eval(['ys(' int2str(ii) ') = ' M_.endo_names{ii} ';']);
end
end