303 lines
17 KiB
Modula-2
303 lines
17 KiB
Modula-2
% DSGE model based on replication files of
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% 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
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% Adapted for Dynare by Willi Mutschler (@wmutschl, willi@mutschler.eu), Jan 2021
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% =========================================================================
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% Copyright © 2021 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
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% =========================================================================
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% This is the model with feedback and calibrated RRA
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% Original code RunGMM_Feedback_estim_RRA_5.m by Martin M. Andreasen, Jan 2016
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@#include "AFVRR_common.inc"
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%--------------------------------------------------------------------------
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% Parameter calibration taken from RunGMM_Feedback_estim_RRA_5.m
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%--------------------------------------------------------------------------
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% fixed parameters
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INHABIT = 1;
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PHI1 = 4;
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PHI4 = 1;
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KAPAone = 0;
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DELTA = 0.025;
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THETA = 0.36;
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ETA = 6;
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CHI = 0;
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BETTAxhr = 0;
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BETTAxhr40= 0;
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RHOD = 0;
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GAMA = 0.9999;
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CONSxhr20 = 0;
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RRA = 5;
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% estimated parameters
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BETTA = 0.996850651147000;
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B = 0.684201133923000;
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H = 0.338754441432000;
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PHI2 = 0.738293581320000;
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KAPAtwo = 11.664785970704999;
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ALFA = 0.831836572237000;
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RHOR = 0.772754520116000;
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BETTAPAI = 3.020381242896000;
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BETTAY = 0.288367683973000;
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MYYPS = 1.000911709188000;
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MYZ = 1.005433723022000;
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RHOA = 0.749465413198000;
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RHOG = 0.847225569814000;
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PAI = 1.010428794858000;
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CONSxhr40 = 0.992863217133000;
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GoY = 0.207099399789000;
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STDA = 0.015621059978000;
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STDG = 0.047539390956000;
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STDD = 0.008623441943000;
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% endogenous parameters set via steady state, no need to initialize
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%PHIzero = ;
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%AA = ;
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%PHI3 = ;
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%negVf = ;
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model_diagnostics;
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% Model diagnostics show that some parameters are endogenously determined
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% via the steady state, so we run steady to calibrate all parameters
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steady;
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model_diagnostics;
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% Now all parameters are determined
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resid;
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check;
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%--------------------------------------------------------------------------
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% Shock distribution
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%--------------------------------------------------------------------------
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shocks;
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var eps_a = STDA^2;
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var eps_d = STDD^2;
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var eps_g = STDG^2;
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end;
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%--------------------------------------------------------------------------
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% Estimated Params block - these parameters will be estimated, we
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% initialize at calibrated values
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%--------------------------------------------------------------------------
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estimated_params;
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BETTA;
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B;
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H;
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PHI2;
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KAPAtwo;
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ALFA;
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RHOR;
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BETTAPAI;
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BETTAY;
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MYYPS;
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MYZ;
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RHOA;
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RHOG;
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PAI;
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CONSxhr40;
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GoY;
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stderr eps_a;
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stderr eps_g;
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stderr eps_d;
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end;
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estimated_params_init(use_calibration);
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end;
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%--------------------------------------------------------------------------
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% Compare whether toolbox yields equivalent moments at second order
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%--------------------------------------------------------------------------
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% Note that we compare results for orderApp=1|2 and not for orderApp=3, because
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% there is a small error in the replication files of the original article in the
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% computation of the covariance matrix of the extended innovations vector.
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% The authors have been contacted, fixed it, and report that the results
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% change only slightly at orderApp=3 to what they report in the paper. At
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% orderApp=2 all is correct and so the following part tests whether we get
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% the same model moments at the calibrated parameters (we do not optimize).
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% We compare it to the replication file RunGMM_Feedback_estim_RRA.m with the
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% following settings: orderApp=1|2, seOn=1, q_lag=10, weighting=1+1;
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% scaled=0; optimizer=0; estimator=1; momentSet=2;
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%
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% Output of the replication files for orderApp=1
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AndreasenEtAl.Q1 = 60275.3715;
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AndreasenEtAl.moments1 =[ % note that we reshuffeled to be compatible with our matched moments block
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{[ 1]} {'Ex' } {'Gr_C '} {' ' } {'0.024388' } {'0.023726' }
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{[ 2]} {'Ex' } {'Gr_I '} {' ' } {'0.031046' } {'0.027372' }
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{[ 3]} {'Ex' } {'Infl ' } {' ' } {'0.03757' } {'0.041499' }
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{[ 4]} {'Ex' } {'r1 ' } {' ' } {'0.056048' } {'0.077843' }
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{[ 5]} {'Ex' } {'r40 ' } {' ' } {'0.069929' } {'0.077843' }
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{[ 6]} {'Ex' } {'xhr40 '} {' ' } {'0.017237' } {'0' }
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{[ 7]} {'Ex' } {'GoY '} {' ' } {'-1.5745' } {'-1.5746' }
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{[ 8]} {'Ex' } {'hours '} {' ' } {'-0.043353' } {'-0.043299' }
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{[ 9]} {'Exx' } {'Gr_C '} {'Gr_C '} {'0.0013159' } {'0.0012763' }
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{[10]} {'Exx' } {'Gr_C '} {'Gr_I '} {'0.0021789' } {'0.0017759' }
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{[11]} {'Exx' } {'Gr_C '} {'Infl ' } {'0.00067495' } {'0.00077354' }
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{[12]} {'Exx' } {'Gr_C '} {'r1 ' } {'0.0011655' } {'0.0016538' }
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{[13]} {'Exx' } {'Gr_C '} {'r40 ' } {'0.0015906' } {'0.0017949' }
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{[14]} {'Exx' } {'Gr_C '} {'xhr40 '} {'0.0020911' } {'0.0017847' }
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{[15]} {'Exx' } {'Gr_I '} {'Gr_I '} {'0.0089104' } {'0.0053424' }
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{[16]} {'Exx' } {'Gr_I '} {'Infl ' } {'0.00063139' } {'0.00064897' }
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{[17]} {'Exx' } {'Gr_I '} {'r1 ' } {'0.0011031' } {'0.0019533' }
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{[18]} {'Exx' } {'Gr_I '} {'r40 ' } {'0.0018445' } {'0.0020602' }
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{[19]} {'Exx' } {'Gr_I '} {'xhr40 '} {'0.00095556' } {'0.0064856' }
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{[20]} {'Exx' } {'Infl ' } {'Infl ' } {'0.0020268' } {'0.0020922' }
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{[21]} {'Exx' } {'Infl ' } {'r1 ' } {'0.0025263' } {'0.0036375' }
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{[22]} {'Exx' } {'Infl ' } {'r40 ' } {'0.0029126' } {'0.0034139' }
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{[23]} {'Exx' } {'Infl ' } {'xhr40 '} {'-0.00077101'} {'-0.0011665' }
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{[24]} {'Exx' } {'r1 ' } {'r1 ' } {'0.0038708' } {'0.0066074' }
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{[25]} {'Exx' } {'r1 ' } {'r40 ' } {'0.0044773' } {'0.0062959' }
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{[26]} {'Exx' } {'r1 ' } {'xhr40 '} {'-0.00048202'} {'-0.00075499'}
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{[27]} {'Exx' } {'r40 ' } {'r40 ' } {'0.0054664' } {'0.0061801' }
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{[28]} {'Exx' } {'r40 ' } {'xhr40 '} {'0.00053864' } {'-0.00030456'}
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{[29]} {'Exx' } {'xhr40 '} {'xhr40 '} {'0.053097' } {'0.012048' }
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{[30]} {'Exx' } {'GoY '} {'GoY '} {'2.4863' } {'2.4872' }
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{[31]} {'Exx' } {'hours '} {'hours '} {'0.0018799' } {'0.0018759' }
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{[32]} {'Exx1'} {'Gr_C '} {'Gr_C '} {'0.00077917' } {'0.00080528' }
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{[33]} {'Exx1'} {'Gr_I '} {'Gr_I '} {'0.0050104' } {'0.0017036' }
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{[34]} {'Exx1'} {'Infl ' } {'Infl ' } {'0.0019503' } {'0.0020185' }
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{[35]} {'Exx1'} {'r1 ' } {'r1 ' } {'0.0038509' } {'0.0065788' }
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{[36]} {'Exx1'} {'r40 ' } {'r40 ' } {'0.0054699' } {'0.0061762' }
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{[37]} {'Exx1'} {'xhr40 '} {'xhr40 '} {'-0.00098295'} {'-4.5519e-15'}
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{[38]} {'Exx1'} {'GoY '} {'GoY '} {'2.4868' } {'2.4863' }
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{[39]} {'Exx1'} {'hours '} {'hours '} {'0.0018799' } {'0.0018755' }
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];
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% Output of the replication files for orderApp=2
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AndreasenEtAl.Q2 = 140.8954;
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AndreasenEtAl.moments2 =[ % note that we reshuffeled to be compatible with our matched moments block
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{[ 1]} {'Ex' } {'Gr_C '} {' ' } {'0.024388' } {'0.023726' }
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{[ 2]} {'Ex' } {'Gr_I '} {' ' } {'0.031046' } {'0.027372' }
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{[ 3]} {'Ex' } {'Infl ' } {' ' } {'0.03757' } {'0.034618' }
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{[ 4]} {'Ex' } {'r1 ' } {' ' } {'0.056048' } {'0.056437' }
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{[ 5]} {'Ex' } {'r40 ' } {' ' } {'0.069929' } {'0.07051' }
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{[ 6]} {'Ex' } {'xhr40 '} {' ' } {'0.017237' } {'0.014242' }
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{[ 7]} {'Ex' } {'GoY '} {' ' } {'-1.5745' } {'-1.574' }
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{[ 8]} {'Ex' } {'hours '} {' ' } {'-0.043353' } {'-0.043351' }
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{[ 9]} {'Exx' } {'Gr_C '} {'Gr_C '} {'0.0013159' } {'0.0012917' }
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{[10]} {'Exx' } {'Gr_C '} {'Gr_I '} {'0.0021789' } {'0.0017862' }
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{[11]} {'Exx' } {'Gr_C '} {'Infl ' } {'0.00067495' } {'0.00061078' }
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{[12]} {'Exx' } {'Gr_C '} {'r1 ' } {'0.0011655' } {'0.0011494' }
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{[13]} {'Exx' } {'Gr_C '} {'r40 ' } {'0.0015906' } {'0.0016149' }
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{[14]} {'Exx' } {'Gr_C '} {'xhr40 '} {'0.0020911' } {'0.002203' }
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{[15]} {'Exx' } {'Gr_I '} {'Gr_I '} {'0.0089104' } {'0.0054317' }
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{[16]} {'Exx' } {'Gr_I '} {'Infl ' } {'0.00063139' } {'0.00045278' }
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{[17]} {'Exx' } {'Gr_I '} {'r1 ' } {'0.0011031' } {'0.0013672' }
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{[18]} {'Exx' } {'Gr_I '} {'r40 ' } {'0.0018445' } {'0.0018557' }
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{[19]} {'Exx' } {'Gr_I '} {'xhr40 '} {'0.00095556' } {'0.0067742' }
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{[20]} {'Exx' } {'Infl ' } {'Infl ' } {'0.0020268' } {'0.0016583' }
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{[21]} {'Exx' } {'Infl ' } {'r1 ' } {'0.0025263' } {'0.0024521' }
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{[22]} {'Exx' } {'Infl ' } {'r40 ' } {'0.0029126' } {'0.002705' }
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{[23]} {'Exx' } {'Infl ' } {'xhr40 '} {'-0.00077101'} {'-0.00065007'}
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{[24]} {'Exx' } {'r1 ' } {'r1 ' } {'0.0038708' } {'0.0038274' }
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{[25]} {'Exx' } {'r1 ' } {'r40 ' } {'0.0044773' } {'0.004297' }
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{[26]} {'Exx' } {'r1 ' } {'xhr40 '} {'-0.00048202'} {'6.3243e-05' }
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{[27]} {'Exx' } {'r40 ' } {'r40 ' } {'0.0054664' } {'0.0051686' }
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{[28]} {'Exx' } {'r40 ' } {'xhr40 '} {'0.00053864' } {'0.00066645' }
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{[29]} {'Exx' } {'xhr40 '} {'xhr40 '} {'0.053097' } {'0.013543' }
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{[30]} {'Exx' } {'GoY '} {'GoY '} {'2.4863' } {'2.4858' }
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{[31]} {'Exx' } {'hours '} {'hours '} {'0.0018799' } {'0.0018804' }
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{[32]} {'Exx1'} {'Gr_C '} {'Gr_C '} {'0.00077917' } {'0.00081772' }
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{[33]} {'Exx1'} {'Gr_I '} {'Gr_I '} {'0.0050104' } {'0.0017106' }
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{[34]} {'Exx1'} {'Infl ' } {'Infl ' } {'0.0019503' } {'0.0015835' }
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{[35]} {'Exx1'} {'r1 ' } {'r1 ' } {'0.0038509' } {'0.0037985' }
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{[36]} {'Exx1'} {'r40 ' } {'r40 ' } {'0.0054699' } {'0.0051642' }
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{[37]} {'Exx1'} {'xhr40 '} {'xhr40 '} {'-0.00098295'} {'0.00020285' }
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{[38]} {'Exx1'} {'GoY '} {'GoY '} {'2.4868' } {'2.4848' }
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{[39]} {'Exx1'} {'hours '} {'hours '} {'0.0018799' } {'0.0018799' }
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];
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@#for orderApp in 1:2
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method_of_moments(
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mom_method = GMM % method of moments method; possible values: GMM|SMM
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, datafile = 'AFVRR_data.mat' % name of filename with data
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, bartlett_kernel_lag = 10 % bandwith in optimal weighting matrix
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, order = @{orderApp} % order of Taylor approximation in perturbation
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, pruning % use pruned state space system at higher-order
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% , verbose % display and store intermediate estimation results
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, weighting_matrix = ['DIAGONAL'] % weighting matrix in moments distance objective function; possible values: OPTIMAL|IDENTITY_MATRIX|DIAGONAL|filename
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% , TeX % print TeX tables and graphics
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% Optimization options that can be set by the user in the mod file, otherwise default values are provided
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%, huge_number=1D10 % value for replacing the infinite bounds on parameters by finite numbers. Used by some optimizers for numerical reasons
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, mode_compute = 0 % specifies the optimizer for minimization of moments distance, note that by default there is a new optimizer
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, optim = ('TolFun', 1e-6
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,'TolX', 1e-6
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,'MaxIter', 3000
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,'MaxFunEvals', 1D6
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,'UseParallel' , 1
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%,'Jacobian' , 'on'
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) % a list of NAME and VALUE pairs to set options for the optimization routines. Available options depend on mode_compute
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%, silent_optimizer % run minimization of moments distance silently without displaying results or saving files in between
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%, analytic_standard_errors
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, se_tolx=1e-10
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);
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% Check results
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fprintf('****************************************************************\n')
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fprintf('Compare Results for perturbation order @{orderApp}\n')
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fprintf('****************************************************************\n')
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dev_Q = AndreasenEtAl.Q@{orderApp} - oo_.mom.Q;
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dev_datamoments = str2double(AndreasenEtAl.moments@{orderApp}(:,5)) - oo_.mom.data_moments;
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dev_modelmoments = str2double(AndreasenEtAl.moments@{orderApp}(:,6)) - oo_.mom.model_moments;
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% There is no table command in Octave
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% The table command also crashes on MATLAB R2014a because it does not like variable names
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if ~isoctave && ~matlab_ver_less_than('8.4')
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table([AndreasenEtAl.Q@{orderApp} ; str2double(AndreasenEtAl.moments@{orderApp}(:,5)) ; str2double(AndreasenEtAl.moments@{orderApp}(:,6))],...
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[oo_.mom.Q ; oo_.mom.data_moments ; oo_.mom.model_moments ],...
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[dev_Q ; dev_datamoments ; dev_modelmoments ],...
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'VariableNames', {'Andreasen et al', 'Dynare', 'dev'})
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end
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if norm(dev_modelmoments)> 1e-4
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warning('Something wrong in the computation of moments at order @{orderApp}')
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end
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@#endfor
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%--------------------------------------------------------------------------
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% Replicate estimation at orderApp=3
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%--------------------------------------------------------------------------
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@#ifdef DoEstimation
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method_of_moments(
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mom_method = GMM % method of moments method; possible values: GMM|SMM
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, datafile = 'AFVRR_data.mat' % name of filename with data
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, bartlett_kernel_lag = 10 % bandwith in optimal weighting matrix
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, order = 3 % order of Taylor approximation in perturbation
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, pruning % use pruned state space system at higher-order
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% , verbose % display and store intermediate estimation results
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, weighting_matrix = ['DIAGONAL', 'Optimal'] % weighting matrix in moments distance objective function; possible values: OPTIMAL|IDENTITY_MATRIX|DIAGONAL|filename
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% , TeX % print TeX tables and graphics
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% Optimization options that can be set by the user in the mod file, otherwise default values are provided
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%, huge_number=1D10 % value for replacing the infinite bounds on parameters by finite numbers. Used by some optimizers for numerical reasons
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, mode_compute = 13 % specifies the optimizer for minimization of moments distance, note that by default there is a new optimizer
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, additional_optimizer_steps = [13]
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, optim = ('TolFun', 1e-6
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,'TolX', 1e-6
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,'MaxIter', 3000
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,'MaxFunEvals', 1D6
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,'UseParallel' , 1
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%,'Jacobian' , 'on'
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) % a list of NAME and VALUE pairs to set options for the optimization routines. Available options depend on mode_compute
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, silent_optimizer % run minimization of moments distance silently without displaying results or saving files in between
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%, analytic_standard_errors
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, se_tolx=1e-10
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);
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@#endif
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