diff --git a/tests/estimation/method_of_moments/AFVRR/AFVRR_M0.mod b/tests/estimation/method_of_moments/AFVRR/AFVRR_M0.mod
index 4bb485c8de0607a2f9551e0f0d49dfe9d4cd3a6c..c1282a7d11fbad55a43385706b579937a52a0621 100644
--- a/tests/estimation/method_of_moments/AFVRR/AFVRR_M0.mod
+++ b/tests/estimation/method_of_moments/AFVRR/AFVRR_M0.mod
@@ -1,302 +1,302 @@
-% 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 <https://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' }
- {[10]} {'Exx' } {'Gr_C '} {'Gr_I '} {'0.0021789' } {'0.0016052' }
- {[11]} {'Exx' } {'Gr_C '} {'Infl ' } {'0.00067495' } {'0.00090947' }
- {[12]} {'Exx' } {'Gr_C '} {'r1 ' } {'0.0011655' } {'0.0016016' }
- {[13]} {'Exx' } {'Gr_C '} {'r40 ' } {'0.0015906' } {'0.0017076' }
- {[14]} {'Exx' } {'Gr_C '} {'xhr40 '} {'0.0020911' } {'0.0013997' }
- {[15]} {'Exx' } {'Gr_I '} {'Gr_I '} {'0.0089104' } {'0.0055317' }
- {[16]} {'Exx' } {'Gr_I '} {'Infl ' } {'0.00063139' } {'0.00050106' }
- {[17]} {'Exx' } {'Gr_I '} {'r1 ' } {'0.0011031' } {'0.0018178' }
- {[18]} {'Exx' } {'Gr_I '} {'r40 ' } {'0.0018445' } {'0.0020186' }
- {[19]} {'Exx' } {'Gr_I '} {'xhr40 '} {'0.00095556' } {'0.0064471' }
- {[20]} {'Exx' } {'Infl ' } {'Infl ' } {'0.0020268' } {'0.0030519' }
- {[21]} {'Exx' } {'Infl ' } {'r1 ' } {'0.0025263' } {'0.0042181' }
- {[22]} {'Exx' } {'Infl ' } {'r40 ' } {'0.0029126' } {'0.0039217' }
- {[23]} {'Exx' } {'Infl ' } {'xhr40 '} {'-0.00077101'} {'-0.0019975' }
- {[24]} {'Exx' } {'r1 ' } {'r1 ' } {'0.0038708' } {'0.0061403' }
- {[25]} {'Exx' } {'r1 ' } {'r40 ' } {'0.0044773' } {'0.0058343' }
- {[26]} {'Exx' } {'r1 ' } {'xhr40 '} {'-0.00048202'} {'-0.00089501'}
- {[27]} {'Exx' } {'r40 ' } {'r40 ' } {'0.0054664' } {'0.0056883' }
- {[28]} {'Exx' } {'r40 ' } {'xhr40 '} {'0.00053864' } {'-0.00041184'}
- {[29]} {'Exx' } {'xhr40 '} {'xhr40 '} {'0.053097' } {'0.016255' }
- {[30]} {'Exx' } {'GoY '} {'GoY '} {'2.4863' } {'2.4919' }
- {[31]} {'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' }
- {[10]} {'Exx' } {'Gr_C '} {'Gr_I '} {'0.0021789' } {'0.0016067' }
- {[11]} {'Exx' } {'Gr_C '} {'Infl ' } {'0.00067495' } {'0.00059406'}
- {[12]} {'Exx' } {'Gr_C '} {'r1 ' } {'0.0011655' } {'0.0011949' }
- {[13]} {'Exx' } {'Gr_C '} {'r40 ' } {'0.0015906' } {'0.0016104' }
- {[14]} {'Exx' } {'Gr_C '} {'xhr40 '} {'0.0020911' } {'0.0020245' }
- {[15]} {'Exx' } {'Gr_I '} {'Gr_I '} {'0.0089104' } {'0.0060254' }
- {[16]} {'Exx' } {'Gr_I '} {'Infl ' } {'0.00063139' } {'8.3563e-05'}
- {[17]} {'Exx' } {'Gr_I '} {'r1 ' } {'0.0011031' } {'0.0013176' }
- {[18]} {'Exx' } {'Gr_I '} {'r40 ' } {'0.0018445' } {'0.0019042' }
- {[19]} {'Exx' } {'Gr_I '} {'xhr40 '} {'0.00095556' } {'0.0064261' }
- {[20]} {'Exx' } {'Infl ' } {'Infl ' } {'0.0020268' } {'0.0020735' }
- {[21]} {'Exx' } {'Infl ' } {'r1 ' } {'0.0025263' } {'0.0027621' }
- {[22]} {'Exx' } {'Infl ' } {'r40 ' } {'0.0029126' } {'0.0029257' }
- {[23]} {'Exx' } {'Infl ' } {'xhr40 '} {'-0.00077101'} {'-0.0012165'}
- {[24]} {'Exx' } {'r1 ' } {'r1 ' } {'0.0038708' } {'0.0040235' }
- {[25]} {'Exx' } {'r1 ' } {'r40 ' } {'0.0044773' } {'0.0044702' }
- {[26]} {'Exx' } {'r1 ' } {'xhr40 '} {'-0.00048202'} {'0.00030542'}
- {[27]} {'Exx' } {'r40 ' } {'r40 ' } {'0.0054664' } {'0.0052718' }
- {[28]} {'Exx' } {'r40 ' } {'xhr40 '} {'0.00053864' } {'0.0010045' }
- {[29]} {'Exx' } {'xhr40 '} {'xhr40 '} {'0.053097' } {'0.018416' }
- {[30]} {'Exx' } {'GoY '} {'GoY '} {'2.4863' } {'2.4853' }
- {[31]} {'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;
-
-% There is no table command in Octave
-% The table command also crashes on MATLAB R2014a because it does not like variable names
-if ~isoctave && ~matlab_ver_less_than('8.4')
-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'})
-end
-
-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
+% 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 <https://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' }
+ {[10]} {'Exx' } {'Gr_C '} {'Gr_I '} {'0.0021789' } {'0.0016052' }
+ {[11]} {'Exx' } {'Gr_C '} {'Infl ' } {'0.00067495' } {'0.00090947' }
+ {[12]} {'Exx' } {'Gr_C '} {'r1 ' } {'0.0011655' } {'0.0016016' }
+ {[13]} {'Exx' } {'Gr_C '} {'r40 ' } {'0.0015906' } {'0.0017076' }
+ {[14]} {'Exx' } {'Gr_C '} {'xhr40 '} {'0.0020911' } {'0.0013997' }
+ {[15]} {'Exx' } {'Gr_I '} {'Gr_I '} {'0.0089104' } {'0.0055317' }
+ {[16]} {'Exx' } {'Gr_I '} {'Infl ' } {'0.00063139' } {'0.00050106' }
+ {[17]} {'Exx' } {'Gr_I '} {'r1 ' } {'0.0011031' } {'0.0018178' }
+ {[18]} {'Exx' } {'Gr_I '} {'r40 ' } {'0.0018445' } {'0.0020186' }
+ {[19]} {'Exx' } {'Gr_I '} {'xhr40 '} {'0.00095556' } {'0.0064471' }
+ {[20]} {'Exx' } {'Infl ' } {'Infl ' } {'0.0020268' } {'0.0030519' }
+ {[21]} {'Exx' } {'Infl ' } {'r1 ' } {'0.0025263' } {'0.0042181' }
+ {[22]} {'Exx' } {'Infl ' } {'r40 ' } {'0.0029126' } {'0.0039217' }
+ {[23]} {'Exx' } {'Infl ' } {'xhr40 '} {'-0.00077101'} {'-0.0019975' }
+ {[24]} {'Exx' } {'r1 ' } {'r1 ' } {'0.0038708' } {'0.0061403' }
+ {[25]} {'Exx' } {'r1 ' } {'r40 ' } {'0.0044773' } {'0.0058343' }
+ {[26]} {'Exx' } {'r1 ' } {'xhr40 '} {'-0.00048202'} {'-0.00089501'}
+ {[27]} {'Exx' } {'r40 ' } {'r40 ' } {'0.0054664' } {'0.0056883' }
+ {[28]} {'Exx' } {'r40 ' } {'xhr40 '} {'0.00053864' } {'-0.00041184'}
+ {[29]} {'Exx' } {'xhr40 '} {'xhr40 '} {'0.053097' } {'0.016255' }
+ {[30]} {'Exx' } {'GoY '} {'GoY '} {'2.4863' } {'2.4919' }
+ {[31]} {'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' }
+ {[10]} {'Exx' } {'Gr_C '} {'Gr_I '} {'0.0021789' } {'0.0016067' }
+ {[11]} {'Exx' } {'Gr_C '} {'Infl ' } {'0.00067495' } {'0.00059406'}
+ {[12]} {'Exx' } {'Gr_C '} {'r1 ' } {'0.0011655' } {'0.0011949' }
+ {[13]} {'Exx' } {'Gr_C '} {'r40 ' } {'0.0015906' } {'0.0016104' }
+ {[14]} {'Exx' } {'Gr_C '} {'xhr40 '} {'0.0020911' } {'0.0020245' }
+ {[15]} {'Exx' } {'Gr_I '} {'Gr_I '} {'0.0089104' } {'0.0060254' }
+ {[16]} {'Exx' } {'Gr_I '} {'Infl ' } {'0.00063139' } {'8.3563e-05'}
+ {[17]} {'Exx' } {'Gr_I '} {'r1 ' } {'0.0011031' } {'0.0013176' }
+ {[18]} {'Exx' } {'Gr_I '} {'r40 ' } {'0.0018445' } {'0.0019042' }
+ {[19]} {'Exx' } {'Gr_I '} {'xhr40 '} {'0.00095556' } {'0.0064261' }
+ {[20]} {'Exx' } {'Infl ' } {'Infl ' } {'0.0020268' } {'0.0020735' }
+ {[21]} {'Exx' } {'Infl ' } {'r1 ' } {'0.0025263' } {'0.0027621' }
+ {[22]} {'Exx' } {'Infl ' } {'r40 ' } {'0.0029126' } {'0.0029257' }
+ {[23]} {'Exx' } {'Infl ' } {'xhr40 '} {'-0.00077101'} {'-0.0012165'}
+ {[24]} {'Exx' } {'r1 ' } {'r1 ' } {'0.0038708' } {'0.0040235' }
+ {[25]} {'Exx' } {'r1 ' } {'r40 ' } {'0.0044773' } {'0.0044702' }
+ {[26]} {'Exx' } {'r1 ' } {'xhr40 '} {'-0.00048202'} {'0.00030542'}
+ {[27]} {'Exx' } {'r40 ' } {'r40 ' } {'0.0054664' } {'0.0052718' }
+ {[28]} {'Exx' } {'r40 ' } {'xhr40 '} {'0.00053864' } {'0.0010045' }
+ {[29]} {'Exx' } {'xhr40 '} {'xhr40 '} {'0.053097' } {'0.018416' }
+ {[30]} {'Exx' } {'GoY '} {'GoY '} {'2.4863' } {'2.4853' }
+ {[31]} {'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;
+
+% There is no table command in Octave
+% The table command also crashes on MATLAB R2014a because it does not like variable names
+if ~isoctave && ~matlab_ver_less_than('8.4')
+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'})
+end
+
+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
diff --git a/tests/estimation/method_of_moments/AFVRR/AFVRR_MFB.mod b/tests/estimation/method_of_moments/AFVRR/AFVRR_MFB.mod
index 80d0a77f314ff31aa4d8701a3bc813f8e2cff877..3d98e486b0bca7293286d50495374bc24bdca1dc 100644
--- a/tests/estimation/method_of_moments/AFVRR/AFVRR_MFB.mod
+++ b/tests/estimation/method_of_moments/AFVRR/AFVRR_MFB.mod
@@ -1,303 +1,303 @@
-% 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 <https://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' }
- {[10]} {'Exx' } {'Gr_C '} {'Gr_I '} {'0.0021789' } {'0.0015117' }
- {[11]} {'Exx' } {'Gr_C '} {'Infl ' } {'0.00067495' } {'0.00080078' }
- {[12]} {'Exx' } {'Gr_C '} {'r1 ' } {'0.0011655' } {'0.00182' }
- {[13]} {'Exx' } {'Gr_C '} {'r40 ' } {'0.0015906' } {'0.001913' }
- {[14]} {'Exx' } {'Gr_C '} {'xhr40 '} {'0.0020911' } {'0.0016326' }
- {[15]} {'Exx' } {'Gr_I '} {'Gr_I '} {'0.0089104' } {'0.0040112' }
- {[16]} {'Exx' } {'Gr_I '} {'Infl ' } {'0.00063139' } {'0.00060604' }
- {[17]} {'Exx' } {'Gr_I '} {'r1 ' } {'0.0011031' } {'0.0021426' }
- {[18]} {'Exx' } {'Gr_I '} {'r40 ' } {'0.0018445' } {'0.0022348' }
- {[19]} {'Exx' } {'Gr_I '} {'xhr40 '} {'0.00095556' } {'0.0039852' }
- {[20]} {'Exx' } {'Infl ' } {'Infl ' } {'0.0020268' } {'0.0030058' }
- {[21]} {'Exx' } {'Infl ' } {'r1 ' } {'0.0025263' } {'0.0044951' }
- {[22]} {'Exx' } {'Infl ' } {'r40 ' } {'0.0029126' } {'0.0042225' }
- {[23]} {'Exx' } {'Infl ' } {'xhr40 '} {'-0.00077101'} {'-0.0021222' }
- {[24]} {'Exx' } {'r1 ' } {'r1 ' } {'0.0038708' } {'0.0074776' }
- {[25]} {'Exx' } {'r1 ' } {'r40 ' } {'0.0044773' } {'0.0071906' }
- {[26]} {'Exx' } {'r1 ' } {'xhr40 '} {'-0.00048202'} {'-0.0006736' }
- {[27]} {'Exx' } {'r40 ' } {'r40 ' } {'0.0054664' } {'0.0070599' }
- {[28]} {'Exx' } {'r40 ' } {'xhr40 '} {'0.00053864' } {'-0.00036735'}
- {[29]} {'Exx' } {'xhr40 '} {'xhr40 '} {'0.053097' } {'0.014516' }
- {[30]} {'Exx' } {'GoY '} {'GoY '} {'2.4863' } {'2.4866' }
- {[31]} {'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' }
- {[10]} {'Exx' } {'Gr_C '} {'Gr_I '} {'0.0021789' } {'0.0015247' }
- {[11]} {'Exx' } {'Gr_C '} {'Infl ' } {'0.00067495' } {'0.0004867' }
- {[12]} {'Exx' } {'Gr_C '} {'r1 ' } {'0.0011655' } {'0.0011867' }
- {[13]} {'Exx' } {'Gr_C '} {'r40 ' } {'0.0015906' } {'0.0016146' }
- {[14]} {'Exx' } {'Gr_C '} {'xhr40 '} {'0.0020911' } {'0.0021395' }
- {[15]} {'Exx' } {'Gr_I '} {'Gr_I '} {'0.0089104' } {'0.0043272' }
- {[16]} {'Exx' } {'Gr_I '} {'Infl ' } {'0.00063139' } {'0.00021752'}
- {[17]} {'Exx' } {'Gr_I '} {'r1 ' } {'0.0011031' } {'0.0013919' }
- {[18]} {'Exx' } {'Gr_I '} {'r40 ' } {'0.0018445' } {'0.0018899' }
- {[19]} {'Exx' } {'Gr_I '} {'xhr40 '} {'0.00095556' } {'0.0037854' }
- {[20]} {'Exx' } {'Infl ' } {'Infl ' } {'0.0020268' } {'0.0021043' }
- {[21]} {'Exx' } {'Infl ' } {'r1 ' } {'0.0025263' } {'0.0026571' }
- {[22]} {'Exx' } {'Infl ' } {'r40 ' } {'0.0029126' } {'0.0028566' }
- {[23]} {'Exx' } {'Infl ' } {'xhr40 '} {'-0.00077101'} {'-0.0016279'}
- {[24]} {'Exx' } {'r1 ' } {'r1 ' } {'0.0038708' } {'0.0039136' }
- {[25]} {'Exx' } {'r1 ' } {'r40 ' } {'0.0044773' } {'0.0044118' }
- {[26]} {'Exx' } {'r1 ' } {'xhr40 '} {'-0.00048202'} {'0.00016791'}
- {[27]} {'Exx' } {'r40 ' } {'r40 ' } {'0.0054664' } {'0.0052851' }
- {[28]} {'Exx' } {'r40 ' } {'xhr40 '} {'0.00053864' } {'0.00062143'}
- {[29]} {'Exx' } {'xhr40 '} {'xhr40 '} {'0.053097' } {'0.018126' }
- {[30]} {'Exx' } {'GoY '} {'GoY '} {'2.4863' } {'2.4863' }
- {[31]} {'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;
-
-% There is no table command in Octave
-% The table command also crashes on MATLAB R2014a because it does not like variable names
-if ~isoctave && ~matlab_ver_less_than('8.4')
-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'})
-end
-
-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
+% 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 <https://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' }
+ {[10]} {'Exx' } {'Gr_C '} {'Gr_I '} {'0.0021789' } {'0.0015117' }
+ {[11]} {'Exx' } {'Gr_C '} {'Infl ' } {'0.00067495' } {'0.00080078' }
+ {[12]} {'Exx' } {'Gr_C '} {'r1 ' } {'0.0011655' } {'0.00182' }
+ {[13]} {'Exx' } {'Gr_C '} {'r40 ' } {'0.0015906' } {'0.001913' }
+ {[14]} {'Exx' } {'Gr_C '} {'xhr40 '} {'0.0020911' } {'0.0016326' }
+ {[15]} {'Exx' } {'Gr_I '} {'Gr_I '} {'0.0089104' } {'0.0040112' }
+ {[16]} {'Exx' } {'Gr_I '} {'Infl ' } {'0.00063139' } {'0.00060604' }
+ {[17]} {'Exx' } {'Gr_I '} {'r1 ' } {'0.0011031' } {'0.0021426' }
+ {[18]} {'Exx' } {'Gr_I '} {'r40 ' } {'0.0018445' } {'0.0022348' }
+ {[19]} {'Exx' } {'Gr_I '} {'xhr40 '} {'0.00095556' } {'0.0039852' }
+ {[20]} {'Exx' } {'Infl ' } {'Infl ' } {'0.0020268' } {'0.0030058' }
+ {[21]} {'Exx' } {'Infl ' } {'r1 ' } {'0.0025263' } {'0.0044951' }
+ {[22]} {'Exx' } {'Infl ' } {'r40 ' } {'0.0029126' } {'0.0042225' }
+ {[23]} {'Exx' } {'Infl ' } {'xhr40 '} {'-0.00077101'} {'-0.0021222' }
+ {[24]} {'Exx' } {'r1 ' } {'r1 ' } {'0.0038708' } {'0.0074776' }
+ {[25]} {'Exx' } {'r1 ' } {'r40 ' } {'0.0044773' } {'0.0071906' }
+ {[26]} {'Exx' } {'r1 ' } {'xhr40 '} {'-0.00048202'} {'-0.0006736' }
+ {[27]} {'Exx' } {'r40 ' } {'r40 ' } {'0.0054664' } {'0.0070599' }
+ {[28]} {'Exx' } {'r40 ' } {'xhr40 '} {'0.00053864' } {'-0.00036735'}
+ {[29]} {'Exx' } {'xhr40 '} {'xhr40 '} {'0.053097' } {'0.014516' }
+ {[30]} {'Exx' } {'GoY '} {'GoY '} {'2.4863' } {'2.4866' }
+ {[31]} {'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' }
+ {[10]} {'Exx' } {'Gr_C '} {'Gr_I '} {'0.0021789' } {'0.0015247' }
+ {[11]} {'Exx' } {'Gr_C '} {'Infl ' } {'0.00067495' } {'0.0004867' }
+ {[12]} {'Exx' } {'Gr_C '} {'r1 ' } {'0.0011655' } {'0.0011867' }
+ {[13]} {'Exx' } {'Gr_C '} {'r40 ' } {'0.0015906' } {'0.0016146' }
+ {[14]} {'Exx' } {'Gr_C '} {'xhr40 '} {'0.0020911' } {'0.0021395' }
+ {[15]} {'Exx' } {'Gr_I '} {'Gr_I '} {'0.0089104' } {'0.0043272' }
+ {[16]} {'Exx' } {'Gr_I '} {'Infl ' } {'0.00063139' } {'0.00021752'}
+ {[17]} {'Exx' } {'Gr_I '} {'r1 ' } {'0.0011031' } {'0.0013919' }
+ {[18]} {'Exx' } {'Gr_I '} {'r40 ' } {'0.0018445' } {'0.0018899' }
+ {[19]} {'Exx' } {'Gr_I '} {'xhr40 '} {'0.00095556' } {'0.0037854' }
+ {[20]} {'Exx' } {'Infl ' } {'Infl ' } {'0.0020268' } {'0.0021043' }
+ {[21]} {'Exx' } {'Infl ' } {'r1 ' } {'0.0025263' } {'0.0026571' }
+ {[22]} {'Exx' } {'Infl ' } {'r40 ' } {'0.0029126' } {'0.0028566' }
+ {[23]} {'Exx' } {'Infl ' } {'xhr40 '} {'-0.00077101'} {'-0.0016279'}
+ {[24]} {'Exx' } {'r1 ' } {'r1 ' } {'0.0038708' } {'0.0039136' }
+ {[25]} {'Exx' } {'r1 ' } {'r40 ' } {'0.0044773' } {'0.0044118' }
+ {[26]} {'Exx' } {'r1 ' } {'xhr40 '} {'-0.00048202'} {'0.00016791'}
+ {[27]} {'Exx' } {'r40 ' } {'r40 ' } {'0.0054664' } {'0.0052851' }
+ {[28]} {'Exx' } {'r40 ' } {'xhr40 '} {'0.00053864' } {'0.00062143'}
+ {[29]} {'Exx' } {'xhr40 '} {'xhr40 '} {'0.053097' } {'0.018126' }
+ {[30]} {'Exx' } {'GoY '} {'GoY '} {'2.4863' } {'2.4863' }
+ {[31]} {'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;
+
+% There is no table command in Octave
+% The table command also crashes on MATLAB R2014a because it does not like variable names
+if ~isoctave && ~matlab_ver_less_than('8.4')
+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'})
+end
+
+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