Add unit test for correctness of posterior moments
parent
b333f6bf2b
commit
32b9853277
|
@ -2,6 +2,7 @@ MODFILES = \
|
|||
walsh.mod \
|
||||
optimizers/fs2000_6.mod \
|
||||
moments/example1_hp_test.mod \
|
||||
moments/fs2000_post_moments.mod \
|
||||
lmmcp/rbcii.mod \
|
||||
ep/rbc_mc.mod \
|
||||
estimation/TaRB/fs2000_tarb.mod \
|
||||
|
|
|
@ -0,0 +1,190 @@
|
|||
/*
|
||||
* This file replicates the estimation of the cash in advance model (termed M1
|
||||
* in the paper) described in Frank Schorfheide (2000): "Loss function-based
|
||||
* evaluation of DSGE models", Journal of Applied Econometrics, 15(6), 645-670.
|
||||
*
|
||||
* The data are in file "fsdat_simul.m", and have been artificially generated.
|
||||
* They are therefore different from the original dataset used by Schorfheide.
|
||||
*
|
||||
* The prior distribution follows the one originally specified in Schorfheide's
|
||||
* paper, except for parameter rho. In the paper, the elicited beta prior for rho
|
||||
* implies an asymptote and corresponding prior mode at 0. It is generally
|
||||
* recommended to avoid this extreme type of prior. Some optimizers, for instance
|
||||
* mode_compute=12 (Mathworks' particleswarm algorithm) may find a posterior mode
|
||||
* with rho equal to zero. We lowered the value of the prior standard deviation
|
||||
* (changing .223 to .100) to remove the asymptote.
|
||||
*
|
||||
* 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 originally written by Michel Juillard. Please note that the
|
||||
* following copyright notice only applies to this Dynare implementation of the
|
||||
* model.
|
||||
*/
|
||||
|
||||
/*
|
||||
* Copyright (C) 2004-2017 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;
|
||||
|
||||
alp = 0.33;
|
||||
bet = 0.99;
|
||||
gam = 0.003;
|
||||
mst = 1.011;
|
||||
rho = 0.7;
|
||||
psi = 0.787;
|
||||
del = 0.02;
|
||||
|
||||
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;
|
||||
|
||||
shocks;
|
||||
var e_a; stderr 0.014;
|
||||
var e_m; stderr 0.005;
|
||||
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;
|
||||
|
||||
steady;
|
||||
|
||||
check;
|
||||
|
||||
estimated_params;
|
||||
alp, beta_pdf, 0.356, 0.02;
|
||||
bet, beta_pdf, 0.993, 0.002;
|
||||
gam, normal_pdf, 0.0085, 0.003;
|
||||
mst, normal_pdf, 1.0002, 0.007;
|
||||
rho, beta_pdf, 0.129, 0.100;
|
||||
psi, beta_pdf, 0.65, 0.05;
|
||||
del, beta_pdf, 0.01, 0.005;
|
||||
stderr e_a, inv_gamma_pdf, 0.035449, inf;
|
||||
stderr e_m, inv_gamma_pdf, 0.008862, inf;
|
||||
end;
|
||||
|
||||
varobs gp_obs gy_obs;
|
||||
|
||||
estimation(order=1,mode_compute=5, datafile='../fs2000/fsdat_simul.m', nobs=192, loglinear, mh_replic=20, mh_nblocks=1, mh_jscale=0.8,moments_varendo,
|
||||
conditional_variance_decomposition=[2,2000],consider_all_endogenous,sub_draws=2);
|
||||
|
||||
stoch_simul(order=1,conditional_variance_decomposition=[2,2000],noprint,nograph);
|
||||
par=load([M_.fname filesep 'metropolis' filesep M_.fname '_posterior_draws1']);
|
||||
|
||||
for par_iter=1:size(par.pdraws,1)
|
||||
M_=set_parameters_locally(M_,par.pdraws{par_iter,1});
|
||||
info=stoch_simul(var_list_);
|
||||
correlation(:,:,par_iter)=cell2mat(oo_.autocorr);
|
||||
covariance(:,:,par_iter)=oo_.var;
|
||||
conditional_variance_decomposition(:,:,:,par_iter)=oo_.conditional_variance_decomposition;
|
||||
variance_decomposition(:,:,par_iter)=oo_.variance_decomposition;
|
||||
end
|
||||
|
||||
correlation=mean(correlation,3);
|
||||
nvars=size(M_.endo_names(1:M_.orig_endo_nbr,:),1);
|
||||
for var_iter_1=1:nvars
|
||||
for var_iter_2=1:nvars
|
||||
if max(abs(correlation(var_iter_1,var_iter_2:nvars:end)'-oo_.PosteriorTheoreticalMoments.dsge.correlation.Mean.(deblank(M_.endo_names{var_iter_1,:})).(deblank(M_.endo_names{var_iter_2,:}))))>1e-8
|
||||
error('Correlations do not match')
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
covariance=mean(covariance,3);
|
||||
nvars=size(M_.endo_names(1:M_.orig_endo_nbr,:),1);
|
||||
for var_iter_1=1:nvars
|
||||
for var_iter_2=var_iter_1:nvars
|
||||
if max(abs(covariance(var_iter_1,var_iter_2)-oo_.PosteriorTheoreticalMoments.dsge.covariance.Mean.(deblank(M_.endo_names{var_iter_1,:})).(deblank(M_.endo_names{var_iter_2,:}))))>1e-8
|
||||
error('Covariances do not match')
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
variance_decomposition=mean(variance_decomposition,3);
|
||||
nvars=size(M_.endo_names(1:M_.orig_endo_nbr,:),1);
|
||||
for var_iter_1=1:nvars
|
||||
for shock_iter=1:M_.exo_nbr
|
||||
if max(abs(variance_decomposition(var_iter_1,shock_iter)/100-oo_.PosteriorTheoreticalMoments.dsge.VarianceDecomposition.Mean.(deblank(M_.endo_names{var_iter_1,:})).(deblank(M_.exo_names{shock_iter,:}))))>1e-8
|
||||
error('Variance decomposition does not match')
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
conditional_variance_decomposition=mean(conditional_variance_decomposition,4);
|
||||
nvars=size(M_.endo_names(1:M_.orig_endo_nbr,:),1);
|
||||
horizon_size=size(conditional_variance_decomposition,3);
|
||||
for var_iter_1=1:nvars
|
||||
for shock_iter=1:M_.exo_nbr
|
||||
for horizon_iter=1:horizon_size
|
||||
if max(abs(conditional_variance_decomposition(var_iter_1,horizon_iter,shock_iter)-oo_.PosteriorTheoreticalMoments.dsge.ConditionalVarianceDecomposition.Mean.(deblank(M_.endo_names{var_iter_1,:})).(deblank(M_.exo_names{shock_iter,:}))(horizon_iter)))>1e-8
|
||||
error('Conditional Variance decomposition does not match')
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
/*
|
||||
* The following lines were used to generate the data file. If you want to
|
||||
* generate another random data file, comment the "estimation" line and uncomment
|
||||
* the following lines.
|
||||
*/
|
||||
|
||||
//stoch_simul(periods=200, order=1);
|
||||
//datatomfile('fsdat_simul', char('gy_obs', 'gp_obs'));
|
Loading…
Reference in New Issue