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dynare/tests/estimation/method_of_moments/AFVRR/AFVRR_MFB.mod

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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 © 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
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