MichelJuillard
commit
80253835a7
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@ -352,7 +352,7 @@ if (kalman_algo == 2) || (kalman_algo == 4)
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Q = blkdiag(Q,H);
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R = blkdiag(R,eye(pp));
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Pstar = blkdiag(Pstar,H);
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Pinf = blckdiag(Pinf,zeros(pp));
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Pinf = blkdiag(Pinf,zeros(pp));
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H = zeros(pp,1);
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mmm = mm+pp;
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end
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@ -395,7 +395,7 @@ switch DynareOptions.lik_init
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if kalman_algo == 0
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kalman_algo = 3;
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elseif ~((kalman_algo == 3) || (kalman_algo == 4))
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error(['diffuse filter: options_.kalman_algo can only be equal ' ...
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error(['The model requires Diffuse filter, but you specified a different Kalman filter. You must set options_.kalman_algo ' ...
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'to 0 (default), 3 or 4'])
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end
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[Z,T,R,QT,Pstar,Pinf] = schur_statespace_transformation(Z,T,R,Q,DynareOptions.qz_criterium);
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@ -436,7 +436,7 @@ switch DynareOptions.lik_init
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Q = blkdiag(Q,H);
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R = blkdiag(R,eye(pp));
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Pstar = blkdiag(Pstar,H);
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Pinf = blckdiag(Pinf,zeros(pp));
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Pinf = blkdiag(Pinf,zeros(pp));
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H1 = zeros(pp,1);
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mmm = mm+pp;
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end
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@ -726,7 +726,7 @@ if (kalman_algo==2) || (kalman_algo==4)
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Q = blkdiag(Q,H);
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R = blkdiag(R,eye(pp));
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Pstar = blkdiag(Pstar,H);
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Pinf = blckdiag(Pinf,zeros(pp));
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Pinf = blkdiag(Pinf,zeros(pp));
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H1 = zeros(pp,1);
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mmm = mm+pp;
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end
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@ -291,9 +291,9 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
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end
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parameter_names = bayestopt_.name;
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if options_.cova_compute || options_.mode_compute==5 || options_.mode_compute==6
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save([M_.fname '_mode.mat'],'xparam1','hh','parameter_names');
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save([M_.fname '_mode.mat'],'xparam1','hh','parameter_names','fval');
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else
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save([M_.fname '_mode.mat'],'xparam1','parameter_names');
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save([M_.fname '_mode.mat'],'xparam1','parameter_names','fval');
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end
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end
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@ -122,6 +122,9 @@ else
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b=0;
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fval = 0;
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end
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if DynareOptions.debug
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DynareResults.likelihood_at_initial_parameters=fval;
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end
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DynareOptions.analytic_derivation=ana_deriv;
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if DynareOptions.dsge_var || strcmp(func2str(objective_function),'non_linear_dsge_likelihood')
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@ -169,6 +169,10 @@ MODFILES = \
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kalman_filter_smoother/fs2000_smoother_only.mod \
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kalman_filter_smoother/check_variable_dimensions/fs2000.mod \
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kalman_filter_smoother/check_variable_dimensions/fs2000_ML.mod \
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kalman/likelihood_from_dynare/fs2000_corr_ME.mod \
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kalman/likelihood_from_dynare/fs2000_corr_ME_missing.mod \
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kalman/likelihood_from_dynare/fs2000_uncorr_ME.mod \
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kalman/likelihood_from_dynare/fs2000_uncorr_ME_missing.mod \
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second_order/burnside_1.mod \
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second_order/ds1.mod \
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second_order/ds2.mod \
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@ -403,6 +407,9 @@ 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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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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@ -0,0 +1,137 @@
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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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estimated_params;
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alp, 0.356;
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gam, 0.0085;
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del, 0.01;
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stderr e_a, 0.035449;
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stderr e_m, 0.008862;
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corr e_m, e_a, 0;
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stderr gp_obs, 1;
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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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%%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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%%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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%%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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%%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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@ -0,0 +1,137 @@
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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
|
||||
* 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.
|
||||
*
|
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* 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/>.
|
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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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estimated_params;
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alp, 0.356;
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gam, 0.0085;
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del, 0.01;
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stderr e_a, 0.035449;
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stderr e_m, 0.008862;
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corr e_m, e_a, 0;
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stderr gp_obs, 1;
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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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%%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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%%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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%%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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||||
%%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_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_4=oo_.likelihood_at_initial_parameters;
|
||||
|
|
@ -0,0 +1,137 @@
|
|||
/*
|
||||
* 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);
|
||||
//datatomfile('fsdat_simul_uncorr_ME', char('gy_obs', 'gp_obs'));
|
||||
|
||||
estimated_params;
|
||||
alp, 0.356;
|
||||
gam, 0.0085;
|
||||
del, 0.01;
|
||||
stderr e_a, 0.035449;
|
||||
stderr e_m, 0.008862;
|
||||
corr e_m, e_a, 0;
|
||||
stderr gp_obs, 1;
|
||||
stderr gy_obs, 1;
|
||||
//corr gp_obs, gy_obs,0;
|
||||
end;
|
||||
|
||||
options_.TeX=1;
|
||||
options_.debug=1;
|
||||
|
||||
%%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;
|
||||
%%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;
|
||||
%%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;
|
||||
%%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;
|
||||
|
|
@ -0,0 +1,137 @@
|
|||
/*
|
||||
* 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);
|
||||
//datatomfile('fsdat_simul_uncorr_ME', char('gy_obs', 'gp_obs'));
|
||||
|
||||
estimated_params;
|
||||
alp, 0.356;
|
||||
gam, 0.0085;
|
||||
del, 0.01;
|
||||
stderr e_a, 0.035449;
|
||||
stderr e_m, 0.008862;
|
||||
corr e_m, e_a, 0;
|
||||
stderr gp_obs, 1;
|
||||
stderr gy_obs, 1;
|
||||
//corr gp_obs, gy_obs,0;
|
||||
end;
|
||||
|
||||
options_.TeX=1;
|
||||
options_.debug=1;
|
||||
|
||||
%%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;
|
||||
%%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;
|
||||
%%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;
|
||||
%%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;
|
||||
|
|
@ -0,0 +1,416 @@
|
|||
% Generated data, used by fs2000.mod
|
||||
|
||||
gy_obs =[
|
||||
1.0030045
|
||||
1.0002599
|
||||
0.99104664
|
||||
1.0321162
|
||||
1.0223545
|
||||
1.0043614
|
||||
NaN
|
||||
1.0092127
|
||||
1.0357197
|
||||
1.0150827
|
||||
1.0051548
|
||||
0.98465775
|
||||
0.99132132
|
||||
0.99904153
|
||||
1.0044641
|
||||
1.0179198
|
||||
1.0113462
|
||||
0.99409421
|
||||
0.99904293
|
||||
1.0448336
|
||||
0.99932433
|
||||
1.0057004
|
||||
0.99619787
|
||||
1.0267504
|
||||
1.0077645
|
||||
1.0058026
|
||||
1.0025891
|
||||
0.9939097
|
||||
0.99604693
|
||||
0.99908569
|
||||
1.0151094
|
||||
0.99348134
|
||||
1.0039124
|
||||
1.0145805
|
||||
0.99800868
|
||||
0.98578138
|
||||
1.0065771
|
||||
0.99843919
|
||||
0.97979062
|
||||
0.98413351
|
||||
0.96468174
|
||||
1.0273857
|
||||
1.0225211
|
||||
0.99958667
|
||||
1.0111157
|
||||
1.0099585
|
||||
0.99480311
|
||||
1.0079265
|
||||
0.98924573
|
||||
1.0070613
|
||||
1.0075706
|
||||
0.9937151
|
||||
1.0224711
|
||||
1.0018891
|
||||
0.99051863
|
||||
1.0042944
|
||||
1.0184055
|
||||
0.99419508
|
||||
0.99756624
|
||||
1.0015983
|
||||
0.9845772
|
||||
1.0004407
|
||||
1.0116237
|
||||
0.9861885
|
||||
1.0073094
|
||||
0.99273355
|
||||
1.0013224
|
||||
0.99777979
|
||||
1.0301686
|
||||
0.96809556
|
||||
0.99917088
|
||||
0.99949253
|
||||
0.96590004
|
||||
1.0083938
|
||||
0.96662298
|
||||
1.0221454
|
||||
1.0069792
|
||||
1.0343996
|
||||
1.0066531
|
||||
1.0072525
|
||||
0.99743563
|
||||
0.99723703
|
||||
1.000372
|
||||
0.99013917
|
||||
1.0095223
|
||||
0.98864268
|
||||
0.98092242
|
||||
0.98886488
|
||||
1.0030341
|
||||
1.01894
|
||||
0.99155059
|
||||
0.99533235
|
||||
0.99734316
|
||||
1.0047356
|
||||
1.0082737
|
||||
0.98425116
|
||||
0.99949212
|
||||
1.0055899
|
||||
1.0065075
|
||||
0.99385069
|
||||
0.98867975
|
||||
0.99804843
|
||||
1.0184038
|
||||
0.99301902
|
||||
1.0177222
|
||||
1.0051924
|
||||
1.0187852
|
||||
1.0098985
|
||||
1.0097172
|
||||
1.0145811
|
||||
0.98721038
|
||||
1.0361722
|
||||
1.0105821
|
||||
0.99469309
|
||||
0.98626785
|
||||
1.013871
|
||||
0.99858924
|
||||
0.99302637
|
||||
1.0042186
|
||||
0.99623745
|
||||
0.98545708
|
||||
1.0225435
|
||||
1.0011861
|
||||
1.0130321
|
||||
0.97861347
|
||||
1.0228193
|
||||
0.99627435
|
||||
1.0272779
|
||||
1.0075172
|
||||
1.0096762
|
||||
1.0129306
|
||||
0.99966549
|
||||
1.0262882
|
||||
1.0026914
|
||||
1.0061475
|
||||
1.009523
|
||||
1.0036127
|
||||
0.99762992
|
||||
0.99092634
|
||||
1.0058469
|
||||
0.99887292
|
||||
1.0060653
|
||||
0.98673557
|
||||
0.98895709
|
||||
0.99111967
|
||||
0.990118
|
||||
0.99788054
|
||||
0.97054709
|
||||
1.0099157
|
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1.0175022
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];
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|
|
@ -0,0 +1,406 @@
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gy_obs = [
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1.0045087
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1.0238797
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1.0132055
|
||||
NaN
|
||||
1.0175022
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];
|
||||
|
||||
Loading…
Reference in New Issue