Factorize Kalman filter unit tests
Johannes Pfeifer
Stéphane Adjemian (Hermes)
parent
36ccec75ce
commit
06ff0c7bb6
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@ -443,6 +443,12 @@ observation_trends_and_prefiltering/ML/Trend_loglinear_no_prefilter_first_obs.m.
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observation_trends_and_prefiltering/ML/Trend_loglinear_no_prefilter_first_obs.o.trs: observation_trends_and_prefiltering/ML/Trend_loglinear_no_prefilter.o.trs
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observation_trends_and_prefiltering/ML/Trend_loglinear_no_prefilter.m.trs: observation_trends_and_prefiltering/ML/Trend_no_prefilter.m.trs
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observation_trends_and_prefiltering/ML/Trend_loglinear_no_prefilter.o.trs: observation_trends_and_prefiltering/ML/Trend_no_prefilter.o.trs
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kalman/likelihood_from_dynare/fs2000_corr_ME.m.trs: kalman/likelihood_from_dynare/fs2000_uncorr_ME.m.trs
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kalman/likelihood_from_dynare/fs2000_corr_ME.o.trs: kalman/likelihood_from_dynare/fs2000_uncorr_ME.o.trs
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kalman/likelihood_from_dynare/fs2000_uncorr_ME_missing.m.trs: kalman/likelihood_from_dynare/fs2000_uncorr_ME.m.trs
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kalman/likelihood_from_dynare/fs2000_uncorr_ME_missing.o.trs: kalman/likelihood_from_dynare/fs2000_uncorr_ME.o.trs
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kalman/likelihood_from_dynare/fs2000_corr_ME_missing.m.trs: kalman/likelihood_from_dynare/fs2000_uncorr_ME.m.trs
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kalman/likelihood_from_dynare/fs2000_corr_ME_missing.o.trs: kalman/likelihood_from_dynare/fs2000_uncorr_ME.o.trs
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lmmcp/sw_newton.m.trs: lmmcp/sw_lmmcp.m.trs
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lmmcp/sw_newton.o.trs: lmmcp/sw_lmmcp.o.trs
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@ -531,12 +537,11 @@ EXTRA_DIST = \
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ms-sbvar/archive-files/specification_2v2c.dat \
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recursive/data_ca1.m \
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kalman_filter_smoother/fsdat_simul.m \
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kalman/likelihood_from_dynare/fsdat_simul_corr_ME_missing.m \
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kalman/likelihood_from_dynare/fsdat_simul_uncorr_ME.m \
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kalman/likelihood_from_dynare/fsdat_simul_uncorr_ME_missing.m \
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kalman/lik_init/fs2000_common.inc \
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kalman/lik_init/fs2000_ns_common.inc \
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kalman_filter_smoother/compare_results_simulation/fsdat_simul_logged.m \
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kalman/likelihood_from_dynare/fs2000_model.inc \
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kalman/likelihood_from_dynare/fs2000_estimation_check.inc \
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identification/kim/kim2_steadystate.m \
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identification/as2007/as2007_steadystate.m \
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estimation/fsdat_simul.m \
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@ -1,104 +1,4 @@
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/*
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* This file is based on the cash in advance model described
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* Frank Schorfheide (2000): "Loss function-based evaluation of DSGE models",
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* Journal of Applied Econometrics, 15(6), 645-670.
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*
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* The equations are taken from J. Nason and T. Cogley (1994): "Testing the
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* implications of long-run neutrality for monetary business cycle models",
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* Journal of Applied Econometrics, 9, S37-S70.
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* Note that there is an initial minus sign missing in equation (A1), p. S63.
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*
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* This implementation was written by Michel Juillard. Please note that the
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* following copyright notice only applies to this Dynare implementation of the
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* model.
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*/
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/*
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* Copyright (C) 2004-2013 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 <http://www.gnu.org/licenses/>.
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*/
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var m P c e W R k d n l gy_obs gp_obs y dA;
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varexo e_a e_m;
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parameters alp bet gam mst rho psi del theta;
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alp = 0.33;
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bet = 0.99;
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gam = 0.003;
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mst = 1.011;
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rho = 0.7;
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psi = 0.787;
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del = 0.02;
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theta=0;
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model;
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dA = exp(gam+e_a);
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log(m) = (1-rho)*log(mst) + rho*log(m(-1))+e_m;
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-P/(c(+1)*P(+1)*m)+bet*P(+1)*(alp*exp(-alp*(gam+log(e(+1))))*k^(alp-1)*n(+1)^(1-alp)+(1-del)*exp(-(gam+log(e(+1)))))/(c(+2)*P(+2)*m(+1))=0;
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W = l/n;
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-(psi/(1-psi))*(c*P/(1-n))+l/n = 0;
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R = P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(-alp)/W;
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1/(c*P)-bet*P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)/(m*l*c(+1)*P(+1)) = 0;
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c+k = exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)+(1-del)*exp(-(gam+e_a))*k(-1);
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P*c = m;
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m-1+d = l;
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e = exp(e_a);
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y = k(-1)^alp*n^(1-alp)*exp(-alp*(gam+e_a));
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gy_obs = dA*y/y(-1);
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gp_obs = (P/P(-1))*m(-1)/dA;
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end;
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steady_state_model;
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dA = exp(gam);
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gst = 1/dA;
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m = mst;
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khst = ( (1-gst*bet*(1-del)) / (alp*gst^alp*bet) )^(1/(alp-1));
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xist = ( ((khst*gst)^alp - (1-gst*(1-del))*khst)/mst )^(-1);
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nust = psi*mst^2/( (1-alp)*(1-psi)*bet*gst^alp*khst^alp );
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n = xist/(nust+xist);
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P = xist + nust;
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k = khst*n;
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l = psi*mst*n/( (1-psi)*(1-n) );
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c = mst/P;
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d = l - mst + 1;
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y = k^alp*n^(1-alp)*gst^alp;
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R = mst/bet;
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W = l/n;
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ist = y-c;
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q = 1 - d;
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e = 1;
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gp_obs = m/dA;
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gy_obs = dA;
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end;
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varobs gp_obs gy_obs;
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shocks;
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var e_a; stderr 0.014;
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var e_m; stderr 0.005;
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corr gy_obs,gp_obs = 0.5;
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end;
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steady;
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@#include "fs2000_model.inc"
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estimated_params;
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alp, 0.356;
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@ -112,57 +12,7 @@ stderr gy_obs, 1;
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corr gp_obs, gy_obs,0;
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end;
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options_.TeX=1;
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options_.debug=1;
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@#define mode_file_name="fs2000_corr_ME_mode"
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@#define data_file_name="fsdat_simul_corr_ME"
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%%default
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options_.lik_init=1;
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estimation(kalman_algo=0,mode_compute=4,order=1,datafile='../../fs2000/fsdat_simul',smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;
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fval_algo_0=oo_.likelihood_at_initial_parameters;
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%%Multivariate Kalman Filter
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options_.lik_init=1;
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estimation(kalman_algo=1,mode_file=fs2000_corr_ME_mode,mode_compute=0,order=1,datafile='../../fs2000/fsdat_simul',smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;
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fval_algo_1=oo_.likelihood_at_initial_parameters;
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SmoothedMeasurementErrors(:,:,1)=cell2mat(struct2cell(oo_.SmoothedMeasurementErrors));
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SmoothedShocks(:,:,1)=cell2mat(struct2cell(oo_.SmoothedShocks));
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SmoothedVariables(:,:,1)=cell2mat(struct2cell(oo_.SmoothedVariables));
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%%Univariate Kalman Filter
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options_.lik_init=1;
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estimation(kalman_algo=3,mode_file=fs2000_corr_ME_mode,mode_compute=0,order=1,datafile='../../fs2000/fsdat_simul',smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;
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fval_algo_3=oo_.likelihood_at_initial_parameters;
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SmoothedMeasurementErrors(:,:,3)=cell2mat(struct2cell(oo_.SmoothedMeasurementErrors));
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SmoothedShocks(:,:,3)=cell2mat(struct2cell(oo_.SmoothedShocks));
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SmoothedVariables(:,:,3)=cell2mat(struct2cell(oo_.SmoothedVariables));
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%%Diffuse Multivariate Kalman Filter
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options_.lik_init=1;
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estimation(kalman_algo=2,mode_file=fs2000_corr_ME_mode,mode_compute=0,datafile='../../fs2000/fsdat_simul',smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;
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fval_algo_2=oo_.likelihood_at_initial_parameters;
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SmoothedMeasurementErrors(:,:,2)=cell2mat(struct2cell(oo_.SmoothedMeasurementErrors));
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SmoothedShocks(:,:,2)=cell2mat(struct2cell(oo_.SmoothedShocks));
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SmoothedVariables(:,:,2)=cell2mat(struct2cell(oo_.SmoothedVariables));
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%%Diffuse univariate Kalman Filter
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options_.lik_init=1;
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estimation(kalman_algo=4,mode_file=fs2000_corr_ME_mode,mode_compute=0,datafile='../../fs2000/fsdat_simul',smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;
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fval_algo_4=oo_.likelihood_at_initial_parameters;
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SmoothedMeasurementErrors(:,:,4)=cell2mat(struct2cell(oo_.SmoothedMeasurementErrors));
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SmoothedShocks(:,:,4)=cell2mat(struct2cell(oo_.SmoothedShocks));
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SmoothedVariables(:,:,4)=cell2mat(struct2cell(oo_.SmoothedVariables));
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if max(max(abs(SmoothedMeasurementErrors-repmat(SmoothedMeasurementErrors(:,:,1),1,1,4))))>1e-8
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error('SmoothedMeasurementErrors do not match')
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end
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if max(max(abs(SmoothedShocks-repmat(SmoothedShocks(:,:,1),1,1,4))))>1e-8
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error('SmoothedShocks do not match')
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end
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if max(max(abs(SmoothedVariables-repmat(SmoothedVariables(:,:,1),1,1,4))))>1e-8
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error('SmoothedVariables do not match')
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end
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if max(abs([fval_algo_2,fval_algo_3,fval_algo_4]-fval_algo_1))>1e-6
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error('Likelihoods do not match')
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end
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@#include "fs2000_estimation_check.inc"
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@ -1,104 +1,4 @@
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/*
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* This file is based on the cash in advance model described
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* Frank Schorfheide (2000): "Loss function-based evaluation of DSGE models",
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* Journal of Applied Econometrics, 15(6), 645-670.
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*
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* The equations are taken from J. Nason and T. Cogley (1994): "Testing the
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* implications of long-run neutrality for monetary business cycle models",
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* Journal of Applied Econometrics, 9, S37-S70.
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* Note that there is an initial minus sign missing in equation (A1), p. S63.
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*
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* This implementation was written by Michel Juillard. Please note that the
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* following copyright notice only applies to this Dynare implementation of the
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* model.
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*/
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/*
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* Copyright (C) 2004-2013 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 <http://www.gnu.org/licenses/>.
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*/
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var m P c e W R k d n l gy_obs gp_obs y dA;
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varexo e_a e_m;
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parameters alp bet gam mst rho psi del theta;
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alp = 0.33;
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bet = 0.99;
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gam = 0.003;
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mst = 1.011;
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rho = 0.7;
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psi = 0.787;
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del = 0.02;
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theta=0;
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model;
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dA = exp(gam+e_a);
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log(m) = (1-rho)*log(mst) + rho*log(m(-1))+e_m;
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-P/(c(+1)*P(+1)*m)+bet*P(+1)*(alp*exp(-alp*(gam+log(e(+1))))*k^(alp-1)*n(+1)^(1-alp)+(1-del)*exp(-(gam+log(e(+1)))))/(c(+2)*P(+2)*m(+1))=0;
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W = l/n;
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-(psi/(1-psi))*(c*P/(1-n))+l/n = 0;
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R = P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(-alp)/W;
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1/(c*P)-bet*P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)/(m*l*c(+1)*P(+1)) = 0;
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c+k = exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)+(1-del)*exp(-(gam+e_a))*k(-1);
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P*c = m;
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m-1+d = l;
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e = exp(e_a);
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y = k(-1)^alp*n^(1-alp)*exp(-alp*(gam+e_a));
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gy_obs = dA*y/y(-1);
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gp_obs = (P/P(-1))*m(-1)/dA;
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end;
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steady_state_model;
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dA = exp(gam);
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gst = 1/dA;
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m = mst;
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khst = ( (1-gst*bet*(1-del)) / (alp*gst^alp*bet) )^(1/(alp-1));
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xist = ( ((khst*gst)^alp - (1-gst*(1-del))*khst)/mst )^(-1);
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nust = psi*mst^2/( (1-alp)*(1-psi)*bet*gst^alp*khst^alp );
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n = xist/(nust+xist);
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P = xist + nust;
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k = khst*n;
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l = psi*mst*n/( (1-psi)*(1-n) );
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c = mst/P;
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d = l - mst + 1;
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y = k^alp*n^(1-alp)*gst^alp;
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R = mst/bet;
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W = l/n;
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ist = y-c;
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q = 1 - d;
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e = 1;
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gp_obs = m/dA;
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gy_obs = dA;
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end;
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varobs gp_obs gy_obs;
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shocks;
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var e_a; stderr 0.014;
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var e_m; stderr 0.005;
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corr gy_obs,gp_obs = 0.5;
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end;
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steady;
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@#include "fs2000_model.inc"
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estimated_params;
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alp, 0.356;
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@ -112,57 +12,7 @@ stderr gy_obs, 1;
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corr gp_obs, gy_obs,0;
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end;
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options_.TeX=1;
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options_.debug=1;
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@#define mode_file_name="fs2000_corr_ME_missing_mode"
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@#define data_file_name="fsdat_simul_corr_ME_missing"
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%%default
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options_.lik_init=1;
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estimation(kalman_algo=0,mode_compute=4,order=1,datafile=fsdat_simul_corr_ME_missing,smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;
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fval_algo_0=oo_.likelihood_at_initial_parameters;
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%%Multivariate Kalman Filter
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options_.lik_init=1;
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estimation(kalman_algo=1,mode_file=fs2000_corr_ME_missing_mode,mode_compute=0,order=1,datafile=fsdat_simul_corr_ME_missing,smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;
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fval_algo_1=oo_.likelihood_at_initial_parameters;
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SmoothedMeasurementErrors(:,:,1)=cell2mat(struct2cell(oo_.SmoothedMeasurementErrors));
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SmoothedShocks(:,:,1)=cell2mat(struct2cell(oo_.SmoothedShocks));
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SmoothedVariables(:,:,1)=cell2mat(struct2cell(oo_.SmoothedVariables));
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%%Univariate Kalman Filter
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options_.lik_init=1;
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estimation(kalman_algo=3,mode_file=fs2000_corr_ME_missing_mode,mode_compute=0,order=1,datafile=fsdat_simul_corr_ME_missing,smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;
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fval_algo_3=oo_.likelihood_at_initial_parameters;
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SmoothedMeasurementErrors(:,:,3)=cell2mat(struct2cell(oo_.SmoothedMeasurementErrors));
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SmoothedShocks(:,:,3)=cell2mat(struct2cell(oo_.SmoothedShocks));
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SmoothedVariables(:,:,3)=cell2mat(struct2cell(oo_.SmoothedVariables));
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%%Diffuse Multivariate Kalman Filter
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options_.lik_init=1;
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estimation(kalman_algo=2,mode_file=fs2000_corr_ME_missing_mode,mode_compute=0,order=1,datafile=fsdat_simul_corr_ME_missing,smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;
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fval_algo_2=oo_.likelihood_at_initial_parameters;
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SmoothedMeasurementErrors(:,:,2)=cell2mat(struct2cell(oo_.SmoothedMeasurementErrors));
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||||
SmoothedShocks(:,:,2)=cell2mat(struct2cell(oo_.SmoothedShocks));
|
||||
SmoothedVariables(:,:,2)=cell2mat(struct2cell(oo_.SmoothedVariables));
|
||||
|
||||
%%Diffuse univariate Kalman Filter
|
||||
options_.lik_init=1;
|
||||
estimation(kalman_algo=4,mode_file=fs2000_corr_ME_missing_mode,mode_compute=0,order=1,datafile=fsdat_simul_corr_ME_missing,smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;
|
||||
fval_algo_4=oo_.likelihood_at_initial_parameters;
|
||||
SmoothedMeasurementErrors(:,:,4)=cell2mat(struct2cell(oo_.SmoothedMeasurementErrors));
|
||||
SmoothedShocks(:,:,4)=cell2mat(struct2cell(oo_.SmoothedShocks));
|
||||
SmoothedVariables(:,:,4)=cell2mat(struct2cell(oo_.SmoothedVariables));
|
||||
|
||||
if max(max(abs(SmoothedMeasurementErrors-repmat(SmoothedMeasurementErrors(:,:,1),1,1,4))))>1e-8
|
||||
error('SmoothedMeasurementErrors do not match')
|
||||
end
|
||||
|
||||
if max(max(abs(SmoothedShocks-repmat(SmoothedShocks(:,:,1),1,1,4))))>1e-8
|
||||
error('SmoothedShocks do not match')
|
||||
end
|
||||
|
||||
if max(max(abs(SmoothedVariables-repmat(SmoothedVariables(:,:,1),1,1,4))))>1e-8
|
||||
error('SmoothedVariables do not match')
|
||||
end
|
||||
|
||||
if max(abs([fval_algo_2,fval_algo_3,fval_algo_4]-fval_algo_1))>1e-6
|
||||
error('Likelihoods do not match')
|
||||
end
|
||||
@#include "fs2000_estimation_check.inc"
|
||||
|
|
@ -0,0 +1,51 @@
|
|||
%%default
|
||||
options_.lik_init=1;
|
||||
estimation(kalman_algo=0,mode_compute=4,order=1,datafile=@{data_file_name},smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;
|
||||
fval_algo_0=oo_.likelihood_at_initial_parameters;
|
||||
%%Multivariate Kalman Filter
|
||||
options_.lik_init=1;
|
||||
estimation(kalman_algo=1,mode_file=@{mode_file_name},mode_compute=0,order=1,datafile=@{data_file_name},smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;
|
||||
fval_algo_1=oo_.likelihood_at_initial_parameters;
|
||||
SmoothedMeasurementErrors(:,:,1)=cell2mat(struct2cell(oo_.SmoothedMeasurementErrors));
|
||||
SmoothedShocks(:,:,1)=cell2mat(struct2cell(oo_.SmoothedShocks));
|
||||
SmoothedVariables(:,:,1)=cell2mat(struct2cell(oo_.SmoothedVariables));
|
||||
|
||||
%%Univariate Kalman Filter
|
||||
options_.lik_init=1;
|
||||
estimation(kalman_algo=3,mode_file=@{mode_file_name},mode_compute=0,order=1,datafile=@{data_file_name},smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;
|
||||
fval_algo_3=oo_.likelihood_at_initial_parameters;
|
||||
SmoothedMeasurementErrors(:,:,3)=cell2mat(struct2cell(oo_.SmoothedMeasurementErrors));
|
||||
SmoothedShocks(:,:,3)=cell2mat(struct2cell(oo_.SmoothedShocks));
|
||||
SmoothedVariables(:,:,3)=cell2mat(struct2cell(oo_.SmoothedVariables));
|
||||
|
||||
%%Diffuse Multivariate Kalman Filter
|
||||
options_.lik_init=1;
|
||||
estimation(kalman_algo=2,mode_file=@{mode_file_name},mode_compute=0,datafile=@{data_file_name},smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;
|
||||
fval_algo_2=oo_.likelihood_at_initial_parameters;
|
||||
SmoothedMeasurementErrors(:,:,2)=cell2mat(struct2cell(oo_.SmoothedMeasurementErrors));
|
||||
SmoothedShocks(:,:,2)=cell2mat(struct2cell(oo_.SmoothedShocks));
|
||||
SmoothedVariables(:,:,2)=cell2mat(struct2cell(oo_.SmoothedVariables));
|
||||
|
||||
%%Diffuse univariate Kalman Filter
|
||||
options_.lik_init=1;
|
||||
estimation(kalman_algo=4,mode_file=@{mode_file_name},mode_compute=0,datafile=@{data_file_name},smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;
|
||||
fval_algo_4=oo_.likelihood_at_initial_parameters;
|
||||
SmoothedMeasurementErrors(:,:,4)=cell2mat(struct2cell(oo_.SmoothedMeasurementErrors));
|
||||
SmoothedShocks(:,:,4)=cell2mat(struct2cell(oo_.SmoothedShocks));
|
||||
SmoothedVariables(:,:,4)=cell2mat(struct2cell(oo_.SmoothedVariables));
|
||||
|
||||
if max(max(abs(SmoothedMeasurementErrors-repmat(SmoothedMeasurementErrors(:,:,1),1,1,4))))>1e-8
|
||||
error('SmoothedMeasurementErrors do not match')
|
||||
end
|
||||
|
||||
if max(max(abs(SmoothedShocks-repmat(SmoothedShocks(:,:,1),1,1,4))))>1e-8
|
||||
error('SmoothedShocks do not match')
|
||||
end
|
||||
|
||||
if max(max(abs(SmoothedVariables-repmat(SmoothedVariables(:,:,1),1,1,4))))>1e-8
|
||||
error('SmoothedVariables do not match')
|
||||
end
|
||||
|
||||
if max(abs([fval_algo_2,fval_algo_3,fval_algo_4]-fval_algo_1))>1e-6
|
||||
error('Likelihoods do not match')
|
||||
end
|
||||
|
|
@ -0,0 +1,102 @@
|
|||
/*
|
||||
* This file is based on the cash in advance model described
|
||||
* Frank Schorfheide (2000): "Loss function-based evaluation of DSGE models",
|
||||
* Journal of Applied Econometrics, 15(6), 645-670.
|
||||
*
|
||||
* The equations are taken from J. Nason and T. Cogley (1994): "Testing the
|
||||
* implications of long-run neutrality for monetary business cycle models",
|
||||
* Journal of Applied Econometrics, 9, S37-S70.
|
||||
* Note that there is an initial minus sign missing in equation (A1), p. S63.
|
||||
*
|
||||
* This implementation was written by Michel Juillard. Please note that the
|
||||
* following copyright notice only applies to this Dynare implementation of the
|
||||
* model.
|
||||
*/
|
||||
|
||||
/*
|
||||
* Copyright (C) 2004-2013 Dynare Team
|
||||
*
|
||||
* This file is part of Dynare.
|
||||
*
|
||||
* Dynare is free software: you can redistribute it and/or modify
|
||||
* it under the terms of the GNU General Public License as published by
|
||||
* the Free Software Foundation, either version 3 of the License, or
|
||||
* (at your option) any later version.
|
||||
*
|
||||
* Dynare is distributed in the hope that it will be useful,
|
||||
* but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
* GNU General Public License for more details.
|
||||
*
|
||||
* You should have received a copy of the GNU General Public License
|
||||
* along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||
*/
|
||||
|
||||
var m P c e W R k d n l gy_obs gp_obs y dA;
|
||||
varexo e_a e_m;
|
||||
|
||||
parameters alp bet gam mst rho psi del theta;
|
||||
|
||||
alp = 0.33;
|
||||
bet = 0.99;
|
||||
gam = 0.003;
|
||||
mst = 1.011;
|
||||
rho = 0.7;
|
||||
psi = 0.787;
|
||||
del = 0.02;
|
||||
theta=0;
|
||||
|
||||
model;
|
||||
dA = exp(gam+e_a);
|
||||
log(m) = (1-rho)*log(mst) + rho*log(m(-1))+e_m;
|
||||
-P/(c(+1)*P(+1)*m)+bet*P(+1)*(alp*exp(-alp*(gam+log(e(+1))))*k^(alp-1)*n(+1)^(1-alp)+(1-del)*exp(-(gam+log(e(+1)))))/(c(+2)*P(+2)*m(+1))=0;
|
||||
W = l/n;
|
||||
-(psi/(1-psi))*(c*P/(1-n))+l/n = 0;
|
||||
R = P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(-alp)/W;
|
||||
1/(c*P)-bet*P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)/(m*l*c(+1)*P(+1)) = 0;
|
||||
c+k = exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)+(1-del)*exp(-(gam+e_a))*k(-1);
|
||||
P*c = m;
|
||||
m-1+d = l;
|
||||
e = exp(e_a);
|
||||
y = k(-1)^alp*n^(1-alp)*exp(-alp*(gam+e_a));
|
||||
gy_obs = dA*y/y(-1);
|
||||
gp_obs = (P/P(-1))*m(-1)/dA;
|
||||
end;
|
||||
|
||||
steady_state_model;
|
||||
dA = exp(gam);
|
||||
gst = 1/dA;
|
||||
m = mst;
|
||||
khst = ( (1-gst*bet*(1-del)) / (alp*gst^alp*bet) )^(1/(alp-1));
|
||||
xist = ( ((khst*gst)^alp - (1-gst*(1-del))*khst)/mst )^(-1);
|
||||
nust = psi*mst^2/( (1-alp)*(1-psi)*bet*gst^alp*khst^alp );
|
||||
n = xist/(nust+xist);
|
||||
P = xist + nust;
|
||||
k = khst*n;
|
||||
|
||||
l = psi*mst*n/( (1-psi)*(1-n) );
|
||||
c = mst/P;
|
||||
d = l - mst + 1;
|
||||
y = k^alp*n^(1-alp)*gst^alp;
|
||||
R = mst/bet;
|
||||
W = l/n;
|
||||
ist = y-c;
|
||||
q = 1 - d;
|
||||
|
||||
e = 1;
|
||||
|
||||
gp_obs = m/dA;
|
||||
gy_obs = dA;
|
||||
end;
|
||||
|
||||
varobs gp_obs gy_obs;
|
||||
|
||||
shocks;
|
||||
var e_a; stderr 0.014;
|
||||
var e_m; stderr 0.005;
|
||||
corr gy_obs,gp_obs = 0.5;
|
||||
end;
|
||||
|
||||
options_.TeX=1;
|
||||
options_.debug=1;
|
||||
|
||||
|
|
@ -1,119 +1,21 @@
|
|||
/*
|
||||
* This file is based on the cash in advance model described
|
||||
* Frank Schorfheide (2000): "Loss function-based evaluation of DSGE models",
|
||||
* Journal of Applied Econometrics, 15(6), 645-670.
|
||||
*
|
||||
* The equations are taken from J. Nason and T. Cogley (1994): "Testing the
|
||||
* implications of long-run neutrality for monetary business cycle models",
|
||||
* Journal of Applied Econometrics, 9, S37-S70.
|
||||
* Note that there is an initial minus sign missing in equation (A1), p. S63.
|
||||
*
|
||||
* This implementation was written by Michel Juillard. Please note that the
|
||||
* following copyright notice only applies to this Dynare implementation of the
|
||||
* model.
|
||||
*/
|
||||
|
||||
/*
|
||||
* Copyright (C) 2004-2013 Dynare Team
|
||||
*
|
||||
* This file is part of Dynare.
|
||||
*
|
||||
* Dynare is free software: you can redistribute it and/or modify
|
||||
* it under the terms of the GNU General Public License as published by
|
||||
* the Free Software Foundation, either version 3 of the License, or
|
||||
* (at your option) any later version.
|
||||
*
|
||||
* Dynare is distributed in the hope that it will be useful,
|
||||
* but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
* GNU General Public License for more details.
|
||||
*
|
||||
* You should have received a copy of the GNU General Public License
|
||||
* along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||
*/
|
||||
|
||||
var m P c e W R k d n l gy_obs gp_obs y dA;
|
||||
varexo e_a e_m;
|
||||
|
||||
parameters alp bet gam mst rho psi del theta;
|
||||
|
||||
alp = 0.33;
|
||||
bet = 0.99;
|
||||
gam = 0.003;
|
||||
mst = 1.011;
|
||||
rho = 0.7;
|
||||
psi = 0.787;
|
||||
del = 0.02;
|
||||
theta=0;
|
||||
|
||||
model;
|
||||
dA = exp(gam+e_a);
|
||||
log(m) = (1-rho)*log(mst) + rho*log(m(-1))+e_m;
|
||||
-P/(c(+1)*P(+1)*m)+bet*P(+1)*(alp*exp(-alp*(gam+log(e(+1))))*k^(alp-1)*n(+1)^(1-alp)+(1-del)*exp(-(gam+log(e(+1)))))/(c(+2)*P(+2)*m(+1))=0;
|
||||
W = l/n;
|
||||
-(psi/(1-psi))*(c*P/(1-n))+l/n = 0;
|
||||
R = P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(-alp)/W;
|
||||
1/(c*P)-bet*P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)/(m*l*c(+1)*P(+1)) = 0;
|
||||
c+k = exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)+(1-del)*exp(-(gam+e_a))*k(-1);
|
||||
P*c = m;
|
||||
m-1+d = l;
|
||||
e = exp(e_a);
|
||||
y = k(-1)^alp*n^(1-alp)*exp(-alp*(gam+e_a));
|
||||
gy_obs = dA*y/y(-1);
|
||||
gp_obs = (P/P(-1))*m(-1)/dA;
|
||||
end;
|
||||
|
||||
steady_state_model;
|
||||
dA = exp(gam);
|
||||
gst = 1/dA;
|
||||
m = mst;
|
||||
khst = ( (1-gst*bet*(1-del)) / (alp*gst^alp*bet) )^(1/(alp-1));
|
||||
xist = ( ((khst*gst)^alp - (1-gst*(1-del))*khst)/mst )^(-1);
|
||||
nust = psi*mst^2/( (1-alp)*(1-psi)*bet*gst^alp*khst^alp );
|
||||
n = xist/(nust+xist);
|
||||
P = xist + nust;
|
||||
k = khst*n;
|
||||
|
||||
l = psi*mst*n/( (1-psi)*(1-n) );
|
||||
c = mst/P;
|
||||
d = l - mst + 1;
|
||||
y = k^alp*n^(1-alp)*gst^alp;
|
||||
R = mst/bet;
|
||||
W = l/n;
|
||||
ist = y-c;
|
||||
q = 1 - d;
|
||||
|
||||
e = 1;
|
||||
|
||||
gp_obs = m/dA;
|
||||
gy_obs = dA;
|
||||
end;
|
||||
|
||||
varobs gp_obs gy_obs;
|
||||
|
||||
shocks;
|
||||
var e_a; stderr 0.014;
|
||||
var e_m; stderr 0.005;
|
||||
end;
|
||||
|
||||
steady;
|
||||
|
||||
// stoch_simul(periods=200, order=1,irf=0);
|
||||
// temp=oo_.endo_simul;
|
||||
// %add measurement error
|
||||
// oo_.endo_simul(strmatch('gy_obs',M_.endo_names,'exact'),:)=oo_.endo_simul(strmatch('gy_obs',M_.endo_names,'exact'),:)+0.05*randn(1,size(oo_.endo_simul,2));
|
||||
// oo_.endo_simul(strmatch('gp_obs',M_.endo_names,'exact'),:)=oo_.endo_simul(strmatch('gp_obs',M_.endo_names,'exact'),:)+0.05*randn(1,size(oo_.endo_simul,2));
|
||||
// datatomfile('fsdat_simul_uncorr_ME', char('gy_obs', 'gp_obs'));
|
||||
// oo_.endo_simul(strmatch('gy_obs',M_.endo_names,'exact'),[7,199])=NaN;
|
||||
// oo_.endo_simul(strmatch('gp_obs',M_.endo_names,'exact'),[151,199])=NaN;
|
||||
// datatomfile('fsdat_simul_uncorr_ME_missing', char('gy_obs', 'gp_obs'));
|
||||
// shock_mat=chol([1 0.5; 0.5 1])*0.05*randn(2,size(oo_.endo_simul,2));
|
||||
// oo_.endo_simul(strmatch('gy_obs',M_.endo_names,'exact'),:)=oo_.endo_simul(strmatch('gy_obs',M_.endo_names,'exact'),:)+shock_mat(1,:);
|
||||
// oo_.endo_simul(strmatch('gp_obs',M_.endo_names,'exact'),:)=oo_.endo_simul(strmatch('gp_obs',M_.endo_names,'exact'),:)+shock_mat(2,:);
|
||||
// oo_.endo_simul(strmatch('gy_obs',M_.endo_names,'exact'),[7,199])=NaN;
|
||||
// oo_.endo_simul(strmatch('gp_obs',M_.endo_names,'exact'),[151,199])=NaN;
|
||||
// datatomfile('fsdat_simul_corr_ME_missing', char('gy_obs', 'gp_obs'));
|
||||
@#include "fs2000_model.inc"
|
||||
|
||||
stoch_simul(periods=200, order=1,irf=0);
|
||||
temp=oo_.endo_simul;
|
||||
%add measurement error
|
||||
oo_.endo_simul(strmatch('gy_obs',M_.endo_names,'exact'),:)=oo_.endo_simul(strmatch('gy_obs',M_.endo_names,'exact'),:)+0.05*randn(1,size(oo_.endo_simul,2));
|
||||
oo_.endo_simul(strmatch('gp_obs',M_.endo_names,'exact'),:)=oo_.endo_simul(strmatch('gp_obs',M_.endo_names,'exact'),:)+0.05*randn(1,size(oo_.endo_simul,2));
|
||||
datatomfile('fsdat_simul_uncorr_ME', char('gy_obs', 'gp_obs'));
|
||||
oo_.endo_simul(strmatch('gy_obs',M_.endo_names,'exact'),[7,199])=NaN;
|
||||
oo_.endo_simul(strmatch('gp_obs',M_.endo_names,'exact'),[151,199])=NaN;
|
||||
datatomfile('fsdat_simul_uncorr_ME_missing', char('gy_obs', 'gp_obs'));
|
||||
shock_mat=chol([1 0.5; 0.5 1])*0.05*randn(2,size(oo_.endo_simul,2));
|
||||
oo_.endo_simul(strmatch('gy_obs',M_.endo_names,'exact'),:)=oo_.endo_simul(strmatch('gy_obs',M_.endo_names,'exact'),:)+shock_mat(1,:);
|
||||
oo_.endo_simul(strmatch('gp_obs',M_.endo_names,'exact'),:)=oo_.endo_simul(strmatch('gp_obs',M_.endo_names,'exact'),:)+shock_mat(2,:);
|
||||
datatomfile('fsdat_simul_corr_ME', char('gy_obs', 'gp_obs'));
|
||||
oo_.endo_simul(strmatch('gy_obs',M_.endo_names,'exact'),[7,199])=NaN;
|
||||
oo_.endo_simul(strmatch('gp_obs',M_.endo_names,'exact'),[151,199])=NaN;
|
||||
datatomfile('fsdat_simul_corr_ME_missing', char('gy_obs', 'gp_obs'));
|
||||
|
||||
estimated_params;
|
||||
alp, 0.356;
|
||||
|
|
@ -127,57 +29,7 @@ stderr gy_obs, 1;
|
|||
//corr gp_obs, gy_obs,0;
|
||||
end;
|
||||
|
||||
options_.TeX=1;
|
||||
options_.debug=1;
|
||||
@#define mode_file_name="fs2000_uncorr_ME_mode"
|
||||
@#define data_file_name="fsdat_simul_uncorr_ME"
|
||||
|
||||
%%default
|
||||
estimation(kalman_algo=0,mode_compute=4,order=1,datafile=fsdat_simul_uncorr_ME,smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;
|
||||
fval_algo_0=oo_.likelihood_at_initial_parameters;
|
||||
%%Multivariate Kalman Filter
|
||||
options_.lik_init=1;
|
||||
estimation(kalman_algo=1,mode_file=fs2000_uncorr_ME_mode,mode_compute=0,order=1,datafile=fsdat_simul_uncorr_ME,smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;
|
||||
fval_algo_1=oo_.likelihood_at_initial_parameters;
|
||||
SmoothedMeasurementErrors(:,:,1)=cell2mat(struct2cell(oo_.SmoothedMeasurementErrors));
|
||||
SmoothedShocks(:,:,1)=cell2mat(struct2cell(oo_.SmoothedShocks));
|
||||
SmoothedVariables(:,:,1)=cell2mat(struct2cell(oo_.SmoothedVariables));
|
||||
|
||||
%%Univariate Kalman Filter
|
||||
options_.lik_init=1;
|
||||
estimation(kalman_algo=3,mode_file=fs2000_uncorr_ME_mode,mode_compute=0,order=1,datafile=fsdat_simul_uncorr_ME,smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;
|
||||
fval_algo_3=oo_.likelihood_at_initial_parameters;
|
||||
SmoothedMeasurementErrors(:,:,3)=cell2mat(struct2cell(oo_.SmoothedMeasurementErrors));
|
||||
SmoothedShocks(:,:,3)=cell2mat(struct2cell(oo_.SmoothedShocks));
|
||||
SmoothedVariables(:,:,3)=cell2mat(struct2cell(oo_.SmoothedVariables));
|
||||
|
||||
%%Diffuse Multivariate Kalman Filter
|
||||
options_.lik_init=1;
|
||||
estimation(kalman_algo=2,mode_file=fs2000_uncorr_ME_mode,mode_compute=0,order=1,datafile=fsdat_simul_uncorr_ME,smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;
|
||||
fval_algo_2=oo_.likelihood_at_initial_parameters;
|
||||
SmoothedMeasurementErrors(:,:,2)=cell2mat(struct2cell(oo_.SmoothedMeasurementErrors));
|
||||
SmoothedShocks(:,:,2)=cell2mat(struct2cell(oo_.SmoothedShocks));
|
||||
SmoothedVariables(:,:,2)=cell2mat(struct2cell(oo_.SmoothedVariables));
|
||||
|
||||
%%Diffuse univariate Kalman Filter
|
||||
options_.lik_init=1;
|
||||
estimation(kalman_algo=4,mode_file=fs2000_uncorr_ME_mode,mode_compute=0,order=1,datafile=fsdat_simul_uncorr_ME,smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;
|
||||
fval_algo_4=oo_.likelihood_at_initial_parameters;
|
||||
SmoothedMeasurementErrors(:,:,4)=cell2mat(struct2cell(oo_.SmoothedMeasurementErrors));
|
||||
SmoothedShocks(:,:,4)=cell2mat(struct2cell(oo_.SmoothedShocks));
|
||||
SmoothedVariables(:,:,4)=cell2mat(struct2cell(oo_.SmoothedVariables));
|
||||
|
||||
|
||||
if max(max(abs(SmoothedMeasurementErrors-repmat(SmoothedMeasurementErrors(:,:,1),1,1,4))))>1e-8
|
||||
error('SmoothedMeasurementErrors do not match')
|
||||
end
|
||||
|
||||
if max(max(abs(SmoothedShocks-repmat(SmoothedShocks(:,:,1),1,1,4))))>1e-8
|
||||
error('SmoothedShocks do not match')
|
||||
end
|
||||
|
||||
if max(max(abs(SmoothedVariables-repmat(SmoothedVariables(:,:,1),1,1,4))))>1e-8
|
||||
error('SmoothedVariables do not match')
|
||||
end
|
||||
|
||||
if max(abs([fval_algo_1 fval_algo_2,fval_algo_3,fval_algo_4]-fval_algo_1))>1e-6
|
||||
error('Likelihoods do not match')
|
||||
end
|
||||
@#include "fs2000_estimation_check.inc"
|
||||
|
|
@ -1,102 +1,4 @@
|
|||
/*
|
||||
* This file is based on the cash in advance model described
|
||||
* Frank Schorfheide (2000): "Loss function-based evaluation of DSGE models",
|
||||
* Journal of Applied Econometrics, 15(6), 645-670.
|
||||
*
|
||||
* The equations are taken from J. Nason and T. Cogley (1994): "Testing the
|
||||
* implications of long-run neutrality for monetary business cycle models",
|
||||
* Journal of Applied Econometrics, 9, S37-S70.
|
||||
* Note that there is an initial minus sign missing in equation (A1), p. S63.
|
||||
*
|
||||
* This implementation was written by Michel Juillard. Please note that the
|
||||
* following copyright notice only applies to this Dynare implementation of the
|
||||
* model.
|
||||
*/
|
||||
|
||||
/*
|
||||
* Copyright (C) 2004-2013 Dynare Team
|
||||
*
|
||||
* This file is part of Dynare.
|
||||
*
|
||||
* Dynare is free software: you can redistribute it and/or modify
|
||||
* it under the terms of the GNU General Public License as published by
|
||||
* the Free Software Foundation, either version 3 of the License, or
|
||||
* (at your option) any later version.
|
||||
*
|
||||
* Dynare is distributed in the hope that it will be useful,
|
||||
* but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
* GNU General Public License for more details.
|
||||
*
|
||||
* You should have received a copy of the GNU General Public License
|
||||
* along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||
*/
|
||||
|
||||
var m P c e W R k d n l gy_obs gp_obs y dA;
|
||||
varexo e_a e_m;
|
||||
|
||||
parameters alp bet gam mst rho psi del theta;
|
||||
|
||||
alp = 0.33;
|
||||
bet = 0.99;
|
||||
gam = 0.003;
|
||||
mst = 1.011;
|
||||
rho = 0.7;
|
||||
psi = 0.787;
|
||||
del = 0.02;
|
||||
theta=0;
|
||||
|
||||
model;
|
||||
dA = exp(gam+e_a);
|
||||
log(m) = (1-rho)*log(mst) + rho*log(m(-1))+e_m;
|
||||
-P/(c(+1)*P(+1)*m)+bet*P(+1)*(alp*exp(-alp*(gam+log(e(+1))))*k^(alp-1)*n(+1)^(1-alp)+(1-del)*exp(-(gam+log(e(+1)))))/(c(+2)*P(+2)*m(+1))=0;
|
||||
W = l/n;
|
||||
-(psi/(1-psi))*(c*P/(1-n))+l/n = 0;
|
||||
R = P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(-alp)/W;
|
||||
1/(c*P)-bet*P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)/(m*l*c(+1)*P(+1)) = 0;
|
||||
c+k = exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)+(1-del)*exp(-(gam+e_a))*k(-1);
|
||||
P*c = m;
|
||||
m-1+d = l;
|
||||
e = exp(e_a);
|
||||
y = k(-1)^alp*n^(1-alp)*exp(-alp*(gam+e_a));
|
||||
gy_obs = dA*y/y(-1);
|
||||
gp_obs = (P/P(-1))*m(-1)/dA;
|
||||
end;
|
||||
|
||||
steady_state_model;
|
||||
dA = exp(gam);
|
||||
gst = 1/dA;
|
||||
m = mst;
|
||||
khst = ( (1-gst*bet*(1-del)) / (alp*gst^alp*bet) )^(1/(alp-1));
|
||||
xist = ( ((khst*gst)^alp - (1-gst*(1-del))*khst)/mst )^(-1);
|
||||
nust = psi*mst^2/( (1-alp)*(1-psi)*bet*gst^alp*khst^alp );
|
||||
n = xist/(nust+xist);
|
||||
P = xist + nust;
|
||||
k = khst*n;
|
||||
|
||||
l = psi*mst*n/( (1-psi)*(1-n) );
|
||||
c = mst/P;
|
||||
d = l - mst + 1;
|
||||
y = k^alp*n^(1-alp)*gst^alp;
|
||||
R = mst/bet;
|
||||
W = l/n;
|
||||
ist = y-c;
|
||||
q = 1 - d;
|
||||
|
||||
e = 1;
|
||||
|
||||
gp_obs = m/dA;
|
||||
gy_obs = dA;
|
||||
end;
|
||||
|
||||
varobs gp_obs gy_obs;
|
||||
|
||||
shocks;
|
||||
var e_a; stderr 0.014;
|
||||
var e_m; stderr 0.005;
|
||||
end;
|
||||
|
||||
steady;
|
||||
@#include "fs2000_model.inc"
|
||||
|
||||
estimated_params;
|
||||
alp, 0.356;
|
||||
|
|
@ -110,55 +12,7 @@ stderr gy_obs, 1;
|
|||
//corr gp_obs, gy_obs,0;
|
||||
end;
|
||||
|
||||
options_.TeX=1;
|
||||
options_.debug=1;
|
||||
@#define mode_file_name="fs2000_uncorr_ME_missing_mode"
|
||||
@#define data_file_name="fsdat_simul_uncorr_ME_missing"
|
||||
|
||||
%%default
|
||||
estimation(kalman_algo=0,mode_compute=4,order=1,datafile=fsdat_simul_uncorr_ME_missing,smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;
|
||||
fval_algo_0=oo_.likelihood_at_initial_parameters;
|
||||
%%Multivariate Kalman Filter
|
||||
options_.lik_init=1;
|
||||
estimation(kalman_algo=1,mode_file=fs2000_uncorr_ME_missing_mode,mode_compute=0,order=1,datafile=fsdat_simul_uncorr_ME_missing,smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;
|
||||
fval_algo_1=oo_.likelihood_at_initial_parameters;
|
||||
SmoothedMeasurementErrors(:,:,1)=cell2mat(struct2cell(oo_.SmoothedMeasurementErrors));
|
||||
SmoothedShocks(:,:,1)=cell2mat(struct2cell(oo_.SmoothedShocks));
|
||||
SmoothedVariables(:,:,1)=cell2mat(struct2cell(oo_.SmoothedVariables));
|
||||
%%Univariate Kalman Filter
|
||||
options_.lik_init=1;
|
||||
estimation(kalman_algo=3,mode_file=fs2000_uncorr_ME_missing_mode,mode_compute=0,order=1,datafile=fsdat_simul_uncorr_ME_missing,smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;
|
||||
fval_algo_3=oo_.likelihood_at_initial_parameters;
|
||||
SmoothedMeasurementErrors(:,:,3)=cell2mat(struct2cell(oo_.SmoothedMeasurementErrors));
|
||||
SmoothedShocks(:,:,3)=cell2mat(struct2cell(oo_.SmoothedShocks));
|
||||
SmoothedVariables(:,:,3)=cell2mat(struct2cell(oo_.SmoothedVariables));
|
||||
|
||||
%%Diffuse Multivariate Kalman Filter
|
||||
options_.lik_init=1;
|
||||
estimation(kalman_algo=2,mode_file=fs2000_uncorr_ME_missing_mode,mode_compute=0,order=1,datafile=fsdat_simul_uncorr_ME_missing,smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;
|
||||
fval_algo_2=oo_.likelihood_at_initial_parameters;
|
||||
SmoothedMeasurementErrors(:,:,2)=cell2mat(struct2cell(oo_.SmoothedMeasurementErrors));
|
||||
SmoothedShocks(:,:,2)=cell2mat(struct2cell(oo_.SmoothedShocks));
|
||||
SmoothedVariables(:,:,2)=cell2mat(struct2cell(oo_.SmoothedVariables));
|
||||
|
||||
%%Diffuse univariate Kalman Filter
|
||||
options_.lik_init=1;
|
||||
estimation(kalman_algo=4,mode_file=fs2000_uncorr_ME_missing_mode,mode_compute=0,order=1,datafile=fsdat_simul_uncorr_ME_missing,smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;
|
||||
fval_algo_4=oo_.likelihood_at_initial_parameters;
|
||||
SmoothedMeasurementErrors(:,:,4)=cell2mat(struct2cell(oo_.SmoothedMeasurementErrors));
|
||||
SmoothedShocks(:,:,4)=cell2mat(struct2cell(oo_.SmoothedShocks));
|
||||
SmoothedVariables(:,:,4)=cell2mat(struct2cell(oo_.SmoothedVariables));
|
||||
|
||||
if max(max(abs(SmoothedMeasurementErrors-repmat(SmoothedMeasurementErrors(:,:,1),1,1,4))))>1e-8
|
||||
error('SmoothedMeasurementErrors do not match')
|
||||
end
|
||||
|
||||
if max(max(abs(SmoothedShocks-repmat(SmoothedShocks(:,:,1),1,1,4))))>1e-8
|
||||
error('SmoothedShocks do not match')
|
||||
end
|
||||
|
||||
if max(max(abs(SmoothedVariables-repmat(SmoothedVariables(:,:,1),1,1,4))))>1e-8
|
||||
error('SmoothedVariables do not match')
|
||||
end
|
||||
|
||||
if max(abs([fval_algo_2,fval_algo_3,fval_algo_4]-fval_algo_1))>1e-6
|
||||
error('Likelihoods do not match')
|
||||
end
|
||||
@#include "fs2000_estimation_check.inc"
|
||||
|
|
@ -1,408 +0,0 @@
|
|||
% Dataset generated by fs2000_uncorr_ME.m.
|
||||
% 14-Jun-2016 17:56:00
|
||||
gy_obs = [
|
||||
1.0109609
|
||||
0.97642646
|
||||
0.96315626
|
||||
0.95745501
|
||||
0.97985018
|
||||
0.98698404
|
||||
NaN
|
||||
0.97758449
|
||||
1.0372816
|
||||
1.0458945
|
||||
1.0332673
|
||||
0.97502186
|
||||
0.94303873
|
||||
0.90973739
|
||||
0.99226447
|
||||
1.1009692
|
||||
1.0053564
|
||||
0.94758433
|
||||
0.9567912
|
||||
0.87110654
|
||||
1.0446766
|
||||
1.0111115
|
||||
0.86655595
|
||||
1.0215822
|
||||
1.0472621
|
||||
0.908667
|
||||
1.1514238
|
||||
0.89857157
|
||||
1.060294
|
||||
1.013061
|
||||
0.96731591
|
||||
0.9138868
|
||||
0.98929905
|
||||
0.97839349
|
||||
0.85327882
|
||||
0.89567237
|
||||
1.0052162
|
||||
0.98056641
|
||||
0.92769265
|
||||
1.1388486
|
||||
1.0496267
|
||||
0.98826525
|
||||
0.97595184
|
||||
1.0090495
|
||||
0.89012437
|
||||
1.0911123
|
||||
0.99972573
|
||||
1.0037704
|
||||
0.93309032
|
||||
1.0777157
|
||||
1.0195358
|
||||
0.93789726
|
||||
0.93761622
|
||||
0.99192629
|
||||
0.99058658
|
||||
0.96107042
|
||||
0.97133837
|
||||
0.89782855
|
||||
0.89300512
|
||||
1.1139143
|
||||
1.0940156
|
||||
1.1159643
|
||||
0.98698884
|
||||
1.0290253
|
||||
0.96175065
|
||||
0.90738687
|
||||
0.99696118
|
||||
0.97020529
|
||||
1.1340389
|
||||
1.0148178
|
||||
1.0718094
|
||||
1.0246439
|
||||
1.0853133
|
||||
0.9376242
|
||||
0.98567328
|
||||
1.0127188
|
||||
0.89367724
|
||||
1.1355668
|
||||
0.96168471
|
||||
1.0507627
|
||||
0.97586638
|
||||
0.99626073
|
||||
1.0162793
|
||||
1.0616655
|
||||
1.0678168
|
||||
0.97154114
|
||||
0.99647212
|
||||
1.0420533
|
||||
1.0217119
|
||||
1.1918036
|
||||
0.92459188
|
||||
1.0202248
|
||||
1.1838137
|
||||
0.98797992
|
||||
1.000314
|
||||
0.92258543
|
||||
0.98650265
|
||||
1.0416953
|
||||
1.0685879
|
||||
1.0723427
|
||||
1.0187821
|
||||
1.0227766
|
||||
1.0229143
|
||||
0.98194776
|
||||
0.99438303
|
||||
1.0064659
|
||||
0.88075679
|
||||
1.2524675
|
||||
0.98155486
|
||||
0.94454137
|
||||
0.91557661
|
||||
0.94205973
|
||||
0.95937251
|
||||
1.0861095
|
||||
0.95876852
|
||||
1.1358827
|
||||
0.97599938
|
||||
0.98529744
|
||||
1.1516775
|
||||
1.2405386
|
||||
0.88366791
|
||||
0.85121878
|
||||
0.9844267
|
||||
0.96547309
|
||||
0.96460182
|
||||
1.0839974
|
||||
1.1668071
|
||||
1.0287994
|
||||
1.0180404
|
||||
1.0132839
|
||||
1.0273246
|
||||
1.0382224
|
||||
1.0030073
|
||||
0.96332671
|
||||
0.99994006
|
||||
0.99648052
|
||||
1.0565757
|
||||
1.0387682
|
||||
0.90149605
|
||||
1.0537697
|
||||
1.1070891
|
||||
0.99672816
|
||||
1.0670392
|
||||
0.9810588
|
||||
1.0392032
|
||||
1.0431335
|
||||
0.91577584
|
||||
1.027802
|
||||
0.96260436
|
||||
0.85928141
|
||||
0.96465077
|
||||
1.0163289
|
||||
0.98117748
|
||||
1.0331503
|
||||
1.0033069
|
||||
0.90657866
|
||||
1.0963327
|
||||
0.84171175
|
||||
1.0262137
|
||||
1.1035421
|
||||
1.0091114
|
||||
1.0007519
|
||||
1.2000436
|
||||
1.0487303
|
||||
0.97031189
|
||||
1.0637329
|
||||
1.0283992
|
||||
1.0893456
|
||||
1.0592114
|
||||
0.97353829
|
||||
1.0326355
|
||||
1.0745714
|
||||
0.99375808
|
||||
1.1411619
|
||||
1.0828585
|
||||
0.83939772
|
||||
0.87515849
|
||||
0.96132978
|
||||
1.0772758
|
||||
1.0185846
|
||||
0.99000922
|
||||
0.96689881
|
||||
1.1259479
|
||||
0.92506257
|
||||
1.0039843
|
||||
0.93819156
|
||||
1.0443276
|
||||
1.0782669
|
||||
0.90842532
|
||||
1.0840948
|
||||
0.9993092
|
||||
1.0253587
|
||||
1.058007
|
||||
1.069575
|
||||
0.85747319
|
||||
1.0449107
|
||||
0.95307215
|
||||
0.96357006
|
||||
NaN
|
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@ -1,408 +0,0 @@
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% 14-Jun-2016 17:56:00
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];
|
||||
|
||||
|
|
@ -1,408 +0,0 @@
|
|||
% Dataset generated by fs2000_uncorr_ME.m.
|
||||
% 14-Jun-2016 17:56:00
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||||
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NaN
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1.1244273
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];
|
||||
|
||||
Loading…
Reference in New Issue