134 lines
3.8 KiB
Modula-2
134 lines
3.8 KiB
Modula-2
/*
|
|
* 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 © 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 <https://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;
|
|
|
|
steady;
|
|
|
|
|
|
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;
|
|
|
|
estimation(order=1,datafile=fsdat_simul,mode_check,smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20,tex) m P c e W R k d y gy_obs;
|
|
|
|
|
|
|
|
estimated_params(overwrite);
|
|
//alp, beta_pdf, 0.356, 0.02;
|
|
gam, normal_pdf, 0.0085, 0.003;
|
|
//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;
|
|
corr e_m, e_a, normal_pdf, 0, 0.2;
|
|
stderr gp_obs, inv_gamma_pdf, 0.001, inf;
|
|
//stderr gy_obs, inv_gamma_pdf, 0.001, inf;
|
|
//corr gp_obs, gy_obs,normal_pdf, 0, 0.2;
|
|
end;
|
|
|
|
estimation(mode_compute=5,order=1,datafile=fsdat_simul,mode_check,smoother,filter_decomposition,mh_replic=2000, mh_nblocks=1, mh_jscale=0.8,forecast = 8,bayesian_irf,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y;
|
|
shock_decomposition y W R;
|
|
//identification(advanced=1,max_dim_cova_group=3,prior_mc=250);
|