Add unit tests for Kalman filter calls from Matlab
Johannes Pfeifer
Johannes Pfeifer
parent
b0bbab68f3
commit
1dfb6ea327
|
|
@ -166,6 +166,10 @@ MODFILES = \
|
|||
kalman_filter_smoother/fs2000_smoother_only.mod \
|
||||
kalman_filter_smoother/check_variable_dimensions/fs2000.mod \
|
||||
kalman_filter_smoother/check_variable_dimensions/fs2000_ML.mod \
|
||||
kalman/likelihood_from_dynare/fs2000_corr_ME.mod \
|
||||
kalman/likelihood_from_dynare/fs2000_corr_ME_missing.mod \
|
||||
kalman/likelihood_from_dynare/fs2000_uncorr_ME.mod \
|
||||
kalman/likelihood_from_dynare/fs2000_uncorr_ME_missing.mod \
|
||||
second_order/burnside_1.mod \
|
||||
second_order/ds1.mod \
|
||||
second_order/ds2.mod \
|
||||
|
|
@ -397,6 +401,9 @@ EXTRA_DIST = \
|
|||
ms-sbvar/archive-files/specification_2v2c.dat \
|
||||
recursive/data_ca1.m \
|
||||
kalman_filter_smoother/fsdat_simul.m \
|
||||
kalman/likelihood_from_dynare/fsdat_simul_corr_ME_missing.m \
|
||||
kalman/likelihood_from_dynare/fsdat_simul_uncorr_ME.m \
|
||||
kalman/likelihood_from_dynare/fsdat_simul_uncorr_ME_missing.m \
|
||||
identification/kim/kim2_steadystate.m \
|
||||
identification/as2007/as2007_steadystate.m \
|
||||
estimation/fsdat_simul.m \
|
||||
|
|
|
|||
|
|
@ -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;
|
||||
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;
|
||||
|
||||
options_.TeX=1;
|
||||
options_.debug=1;
|
||||
|
||||
%%default
|
||||
options_.lik_init=1;
|
||||
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;
|
||||
fval_algo_0=oo_.likelihood_at_initial_parameters;
|
||||
%%Multivariate Kalman Filter
|
||||
options_.lik_init=1;
|
||||
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;
|
||||
fval_algo_1=oo_.likelihood_at_initial_parameters;
|
||||
%%Univariate Kalman Filter
|
||||
options_.lik_init=1;
|
||||
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;
|
||||
fval_algo_3=oo_.likelihood_at_initial_parameters;
|
||||
%%Diffuse Multivariate Kalman Filter
|
||||
options_.lik_init=1;
|
||||
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;
|
||||
fval_algo_2=oo_.likelihood_at_initial_parameters;
|
||||
%%Diffuse univariate Kalman Filter
|
||||
options_.lik_init=1;
|
||||
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;
|
||||
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;
|
||||
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;
|
||||
|
||||
options_.TeX=1;
|
||||
options_.debug=1;
|
||||
|
||||
%%default
|
||||
options_.lik_init=1;
|
||||
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;
|
||||
fval_algo_0=oo_.likelihood_at_initial_parameters;
|
||||
%%Multivariate Kalman Filter
|
||||
options_.lik_init=1;
|
||||
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;
|
||||
fval_algo_1=oo_.likelihood_at_initial_parameters;
|
||||
%%Univariate Kalman Filter
|
||||
options_.lik_init=1;
|
||||
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;
|
||||
fval_algo_3=oo_.likelihood_at_initial_parameters;
|
||||
%%Diffuse Multivariate Kalman Filter
|
||||
options_.lik_init=1;
|
||||
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;
|
||||
fval_algo_2=oo_.likelihood_at_initial_parameters;
|
||||
%%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;
|
||||
|
|
@ -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
|
||||
1.0107431
|
||||
0.99518695
|
||||
1.0114048
|
||||
0.99376019
|
||||
1.0023369
|
||||
0.98783327
|
||||
1.0051727
|
||||
1.0100462
|
||||
0.98607387
|
||||
1.0000064
|
||||
0.99692442
|
||||
1.012225
|
||||
0.99574078
|
||||
0.98642833
|
||||
0.99008207
|
||||
1.0197359
|
||||
1.0112849
|
||||
0.98711069
|
||||
0.99402748
|
||||
1.0242141
|
||||
1.0135349
|
||||
0.99842505
|
||||
1.0130714
|
||||
0.99887044
|
||||
1.0059058
|
||||
1.0185998
|
||||
1.0073314
|
||||
0.98687706
|
||||
1.0084551
|
||||
0.97698964
|
||||
0.99482714
|
||||
1.0015302
|
||||
1.0105331
|
||||
1.0261767
|
||||
1.0232822
|
||||
1.0084176
|
||||
0.99785167
|
||||
0.99619733
|
||||
1.0055223
|
||||
1.0076326
|
||||
0.99205461
|
||||
1.0030587
|
||||
1.0137012
|
||||
1.0145878
|
||||
1.0190297
|
||||
1.0000681
|
||||
1.0153894
|
||||
1.0140649
|
||||
1.0007236
|
||||
0.97961463
|
||||
1.0125257
|
||||
1.0169503
|
||||
NaN
|
||||
1.0221185
|
||||
|
||||
];
|
||||
|
||||
gp_obs =[
|
||||
1.0079715
|
||||
1.0115853
|
||||
1.0167502
|
||||
1.0068957
|
||||
1.0138189
|
||||
1.0258364
|
||||
1.0243817
|
||||
1.017373
|
||||
1.0020171
|
||||
1.0003742
|
||||
1.0008974
|
||||
1.0104804
|
||||
1.0116393
|
||||
1.0114294
|
||||
0.99932124
|
||||
0.99461459
|
||||
NaN
|
||||
1.0051446
|
||||
1.020639
|
||||
1.0051964
|
||||
1.0093042
|
||||
1.007068
|
||||
1.01086
|
||||
0.99590086
|
||||
1.0014883
|
||||
1.0117332
|
||||
0.9990095
|
||||
1.0108284
|
||||
1.0103672
|
||||
1.0036722
|
||||
1.0005124
|
||||
1.0190331
|
||||
1.0130978
|
||||
1.007842
|
||||
1.0285436
|
||||
1.0322054
|
||||
1.0213403
|
||||
1.0246486
|
||||
1.0419306
|
||||
1.0258867
|
||||
1.0156316
|
||||
0.99818589
|
||||
0.9894107
|
||||
1.0127584
|
||||
1.0146882
|
||||
1.0136529
|
||||
1.0340107
|
||||
1.0343652
|
||||
1.02971
|
||||
1.0077932
|
||||
1.0198114
|
||||
1.013971
|
||||
1.0061083
|
||||
1.0089573
|
||||
1.0037926
|
||||
1.0082071
|
||||
0.99498155
|
||||
0.99735772
|
||||
0.98765026
|
||||
1.006465
|
||||
1.0196088
|
||||
1.0053233
|
||||
1.0119974
|
||||
1.0188066
|
||||
1.0029302
|
||||
1.0183459
|
||||
1.0034218
|
||||
1.0158799
|
||||
0.98824798
|
||||
1.0274357
|
||||
1.0168832
|
||||
1.0180641
|
||||
1.0294657
|
||||
0.98864091
|
||||
1.0358326
|
||||
0.99889969
|
||||
1.0178322
|
||||
0.99813566
|
||||
1.0073549
|
||||
1.0215985
|
||||
1.0084245
|
||||
1.0080939
|
||||
1.0157021
|
||||
1.0075815
|
||||
1.0032633
|
||||
1.0117871
|
||||
1.0209276
|
||||
1.0077569
|
||||
0.99680958
|
||||
1.0120266
|
||||
1.0017625
|
||||
1.0138811
|
||||
1.0198358
|
||||
1.0059629
|
||||
1.0115416
|
||||
1.0319473
|
||||
1.0167074
|
||||
1.0116111
|
||||
1.0048627
|
||||
1.0217622
|
||||
1.0125221
|
||||
1.0142045
|
||||
0.99792469
|
||||
0.99823971
|
||||
0.99561547
|
||||
0.99850373
|
||||
0.9898464
|
||||
1.0030963
|
||||
1.0051373
|
||||
1.0004213
|
||||
1.0144117
|
||||
0.97185592
|
||||
0.9959518
|
||||
1.0073529
|
||||
1.0051603
|
||||
0.98642572
|
||||
0.99433423
|
||||
1.0112131
|
||||
1.0007695
|
||||
1.0176867
|
||||
1.0134363
|
||||
0.99926191
|
||||
0.99879835
|
||||
0.99878754
|
||||
1.0331374
|
||||
1.0077797
|
||||
1.0127221
|
||||
1.0047393
|
||||
1.0074106
|
||||
0.99784213
|
||||
1.0056495
|
||||
1.0057708
|
||||
0.98817494
|
||||
0.98742176
|
||||
0.99930555
|
||||
1.0000687
|
||||
1.0129754
|
||||
1.009529
|
||||
1.0226731
|
||||
1.0149534
|
||||
1.0164295
|
||||
1.0239469
|
||||
1.0293458
|
||||
1.026199
|
||||
1.0197525
|
||||
1.0126818
|
||||
1.0054473
|
||||
1.0254423
|
||||
1.0069461
|
||||
1.0153135
|
||||
1.0337515
|
||||
1.0178187
|
||||
1.0240469
|
||||
1.0079489
|
||||
1.0186953
|
||||
1.0008628
|
||||
1.0113799
|
||||
1.0140118
|
||||
1.0168007
|
||||
1.011441
|
||||
0.98422774
|
||||
0.98909729
|
||||
1.0157859
|
||||
1.0151586
|
||||
0.99756232
|
||||
0.99497777
|
||||
1.0102841
|
||||
1.0221659
|
||||
0.9937759
|
||||
0.99877193
|
||||
1.0079433
|
||||
0.99667692
|
||||
1.0095959
|
||||
1.0128804
|
||||
1.0156949
|
||||
1.0111951
|
||||
1.0228887
|
||||
1.0122083
|
||||
1.0190197
|
||||
1.0074927
|
||||
1.0268096
|
||||
0.99689352
|
||||
0.98948474
|
||||
1.0024938
|
||||
1.0105543
|
||||
1.014116
|
||||
1.0141217
|
||||
1.0056504
|
||||
1.0101026
|
||||
1.0105069
|
||||
0.99619053
|
||||
1.0059439
|
||||
0.99449473
|
||||
0.99482458
|
||||
1.0037702
|
||||
1.0068087
|
||||
0.99575975
|
||||
1.0030815
|
||||
1.0334014
|
||||
0.99879386
|
||||
0.99625634
|
||||
NaN
|
||||
0.99233844
|
||||
|
||||
];
|
||||
|
||||
|
|
@ -0,0 +1,406 @@
|
|||
gy_obs = [
|
||||
1.0089434
|
||||
0.97436837
|
||||
1.0078602
|
||||
0.99728812
|
||||
1.0469033
|
||||
0.98514927
|
||||
1.0130718
|
||||
1.0127905
|
||||
1.0012276
|
||||
1.0207597
|
||||
1.0128382
|
||||
1.0117555
|
||||
1.0093849
|
||||
1.0130848
|
||||
1.0076878
|
||||
1.0151714
|
||||
0.99010787
|
||||
0.9651508
|
||||
1.0075909
|
||||
1.0211352
|
||||
1.0016703
|
||||
1.0067838
|
||||
0.99211778
|
||||
1.0006245
|
||||
1.0164224
|
||||
0.99240467
|
||||
0.98841006
|
||||
1.0021161
|
||||
0.99328172
|
||||
0.99975511
|
||||
0.9894502
|
||||
1.0095403
|
||||
1.0227135
|
||||
0.98474395
|
||||
0.98842474
|
||||
0.99510226
|
||||
1.0003444
|
||||
0.99419424
|
||||
0.98459156
|
||||
1.0006202
|
||||
1.0205175
|
||||
1.0046155
|
||||
0.99261284
|
||||
1.0137533
|
||||
1.0062878
|
||||
0.9882274
|
||||
1.0106694
|
||||
0.99471339
|
||||
1.002965
|
||||
0.99799306
|
||||
1.0134657
|
||||
1.0039836
|
||||
1.0066297
|
||||
1.0084027
|
||||
1.0245597
|
||||
0.9763106
|
||||
1.02033
|
||||
1.0147916
|
||||
1.0219756
|
||||
1.0008057
|
||||
1.0390485
|
||||
1.0210931
|
||||
0.99734292
|
||||
1.0171595
|
||||
1.0130723
|
||||
0.99892094
|
||||
0.98279425
|
||||
1.0066122
|
||||
0.98575479
|
||||
1.0078347
|
||||
1.0036275
|
||||
0.98116612
|
||||
0.99293813
|
||||
0.98842048
|
||||
0.97690963
|
||||
1.0093542
|
||||
1.0027533
|
||||
1.0156453
|
||||
0.99313547
|
||||
1.0004421
|
||||
0.99954572
|
||||
0.98736251
|
||||
1.0238741
|
||||
0.98768174
|
||||
1.000261
|
||||
0.98722229
|
||||
0.98398124
|
||||
1.0072403
|
||||
1.0009303
|
||||
0.99628068
|
||||
0.98538731
|
||||
0.99218841
|
||||
1.009118
|
||||
0.98810044
|
||||
1.0112788
|
||||
1.0004868
|
||||
0.99890858
|
||||
1.0029751
|
||||
1.0219324
|
||||
1.000043
|
||||
1.0058196
|
||||
1.014664
|
||||
1.0044
|
||||
1.0067619
|
||||
1.0008676
|
||||
0.99532428
|
||||
0.99224953
|
||||
0.99444046
|
||||
1.0003366
|
||||
1.0221002
|
||||
0.98855273
|
||||
1.0187089
|
||||
0.98416472
|
||||
1.0006988
|
||||
0.99933767
|
||||
1.0084427
|
||||
0.9910711
|
||||
0.99630044
|
||||
1.0041385
|
||||
0.99578819
|
||||
0.98859148
|
||||
1.0071189
|
||||
1.0057602
|
||||
1.0006798
|
||||
1.0040692
|
||||
0.99357917
|
||||
1.0055212
|
||||
0.99826781
|
||||
1.0294402
|
||||
1.0306182
|
||||
1.0163397
|
||||
0.99544135
|
||||
1.0089258
|
||||
1.0091866
|
||||
1.0031688
|
||||
1.0065311
|
||||
1.0162032
|
||||
1.006835
|
||||
0.98588242
|
||||
1.0031649
|
||||
1.0143694
|
||||
1.0071297
|
||||
1.0151235
|
||||
0.9950707
|
||||
1.0190895
|
||||
1.0036681
|
||||
1.0153039
|
||||
1.0031942
|
||||
0.97146165
|
||||
0.97647363
|
||||
1.0040287
|
||||
1.0074315
|
||||
1.0139851
|
||||
1.0084592
|
||||
1.013138
|
||||
1.0145484
|
||||
1.0008416
|
||||
0.97670707
|
||||
0.98714692
|
||||
1.0106897
|
||||
0.99046031
|
||||
1.0047648
|
||||
1.0064955
|
||||
0.99129614
|
||||
0.97537098
|
||||
0.99551383
|
||||
1.0000844
|
||||
1.0024149
|
||||
1.0294317
|
||||
0.97109038
|
||||
0.98520755
|
||||
1.0043495
|
||||
0.99858097
|
||||
0.99675168
|
||||
1.0112858
|
||||
1.0139032
|
||||
0.99391287
|
||||
1.026372
|
||||
1.003729
|
||||
1.0047125
|
||||
0.99687991
|
||||
1.0218475
|
||||
1.0125423
|
||||
1.0004841
|
||||
0.9992396
|
||||
0.98416027
|
||||
0.99245649
|
||||
1.007447
|
||||
0.99887608
|
||||
0.97908706
|
||||
1.0330613
|
||||
0.99897339
|
||||
0.9891648
|
||||
1.0148316
|
||||
0.99214239
|
||||
0.99630795
|
||||
0.99629966
|
||||
0.99137359
|
||||
0.9809149
|
||||
1.0008659
|
||||
];
|
||||
|
||||
gp_obs = [
|
||||
1.0193403
|
||||
1.0345762
|
||||
1.0011701
|
||||
1.0147224
|
||||
1.008392
|
||||
1.0488327
|
||||
1.0153551
|
||||
1.0099775
|
||||
1.0260561
|
||||
1.0172218
|
||||
1.0014374
|
||||
1.0184572
|
||||
1.0179988
|
||||
1.0060339
|
||||
1.0019537
|
||||
0.99179578
|
||||
1.004346
|
||||
1.0345153
|
||||
1.0004432
|
||||
0.98327074
|
||||
1.0007585
|
||||
1.0034378
|
||||
1.010532
|
||||
1.0121367
|
||||
1.0097161
|
||||
1.0166682
|
||||
1.0089513
|
||||
1.0194821
|
||||
1.0192704
|
||||
1.0220258
|
||||
1.020915
|
||||
1.0176156
|
||||
1.0040707
|
||||
1.0157694
|
||||
1.0357484
|
||||
1.0256259
|
||||
1.0240583
|
||||
1.0095152
|
||||
1.0241605
|
||||
1.0115295
|
||||
1.003636
|
||||
1.0222399
|
||||
1.0250969
|
||||
1.0068969
|
||||
1.0009829
|
||||
1.0166179
|
||||
1.0252018
|
||||
1.0211178
|
||||
0.99867851
|
||||
0.99594002
|
||||
0.9908135
|
||||
0.99762919
|
||||
0.99616309
|
||||
1.0058679
|
||||
0.99323315
|
||||
1.0132879
|
||||
0.98718922
|
||||
0.99739822
|
||||
0.97858593
|
||||
0.99128769
|
||||
0.98624299
|
||||
0.98447966
|
||||
1.0013312
|
||||
0.99189504
|
||||
0.98032699
|
||||
0.99332035
|
||||
1.0129565
|
||||
1.0007785
|
||||
1.0218292
|
||||
1.0030419
|
||||
1.0044453
|
||||
1.0156181
|
||||
1.0040112
|
||||
1.0081137
|
||||
1.0261598
|
||||
1.0053686
|
||||
1.0024674
|
||||
0.99883223
|
||||
1.0224791
|
||||
1.0074723
|
||||
1.0037807
|
||||
1.0348866
|
||||
1.0053664
|
||||
1.0140072
|
||||
1.017359
|
||||
1.0013916
|
||||
1.017887
|
||||
1.008987
|
||||
1.011771
|
||||
1.0201455
|
||||
1.0249464
|
||||
1.0159166
|
||||
1.0162718
|
||||
1.0312397
|
||||
1.0108745
|
||||
1.0132205
|
||||
1.0142484
|
||||
1.0178907
|
||||
1.0065039
|
||||
1.0190304
|
||||
1.0034406
|
||||
1.0053556
|
||||
1.012823
|
||||
1.0009983
|
||||
1.0073148
|
||||
1.0247254
|
||||
1.0140215
|
||||
1.0053603
|
||||
1.006169
|
||||
0.994725
|
||||
1.026685
|
||||
1.0012279
|
||||
1.0160733
|
||||
1.0119851
|
||||
1.0148392
|
||||
0.99760076
|
||||
1.0070377
|
||||
1.0066215
|
||||
0.98130614
|
||||
1.0127043
|
||||
1.0203824
|
||||
1.0067477
|
||||
0.99510728
|
||||
1.0188472
|
||||
1.0100108
|
||||
1.0146874
|
||||
1.0118012
|
||||
1.0111904
|
||||
0.97759194
|
||||
0.99081872
|
||||
0.98425915
|
||||
1.0026496
|
||||
0.98587189
|
||||
0.98648329
|
||||
1.0035766
|
||||
1.0094743
|
||||
0.99460644
|
||||
0.9953724
|
||||
1.0194434
|
||||
1.0065039
|
||||
1.0056522
|
||||
1.0160367
|
||||
1.006524
|
||||
1.0092492
|
||||
0.9864426
|
||||
0.98723638
|
||||
0.9994522
|
||||
1.0026778
|
||||
1.025553
|
||||
1.0030477
|
||||
0.99411719
|
||||
1.0045087
|
||||
0.99375289
|
||||
1.0017609
|
||||
1.0039766
|
||||
0.99976299
|
||||
1.0155671
|
||||
1.0192975
|
||||
1.0135507
|
||||
1.0099869
|
||||
1.0125994
|
||||
1.0050808
|
||||
1.0088531
|
||||
1.0135256
|
||||
1.0322097
|
||||
1.0065808
|
||||
0.99857525
|
||||
1.0008792
|
||||
0.9997691
|
||||
1.02875
|
||||
1.0177818
|
||||
1.0150152
|
||||
1.026416
|
||||
1.0209804
|
||||
1.010633
|
||||
1.009636
|
||||
1.0028257
|
||||
0.9896666
|
||||
1.0094002
|
||||
0.99958414
|
||||
1.0077797
|
||||
0.98933606
|
||||
1.0014885
|
||||
0.99875283
|
||||
1.005051
|
||||
1.016385
|
||||
1.0116282
|
||||
0.99774103
|
||||
1.0101802
|
||||
1.0281101
|
||||
1.0024654
|
||||
1.0174549
|
||||
1.0342738
|
||||
1.0160514
|
||||
1.0238797
|
||||
1.015626
|
||||
1.0075839
|
||||
1.0132055
|
||||
1.0250766
|
||||
1.0175022
|
||||
];
|
||||
|
||||
|
|
@ -0,0 +1,406 @@
|
|||
gy_obs = [
|
||||
1.0089434
|
||||
0.97436837
|
||||
1.0078602
|
||||
0.99728812
|
||||
1.0469033
|
||||
NaN
|
||||
1.0130718
|
||||
1.0127905
|
||||
1.0012276
|
||||
1.0207597
|
||||
1.0128382
|
||||
1.0117555
|
||||
1.0093849
|
||||
1.0130848
|
||||
1.0076878
|
||||
1.0151714
|
||||
0.99010787
|
||||
0.9651508
|
||||
1.0075909
|
||||
1.0211352
|
||||
1.0016703
|
||||
1.0067838
|
||||
0.99211778
|
||||
1.0006245
|
||||
1.0164224
|
||||
0.99240467
|
||||
0.98841006
|
||||
1.0021161
|
||||
0.99328172
|
||||
0.99975511
|
||||
0.9894502
|
||||
1.0095403
|
||||
1.0227135
|
||||
0.98474395
|
||||
0.98842474
|
||||
0.99510226
|
||||
1.0003444
|
||||
0.99419424
|
||||
0.98459156
|
||||
1.0006202
|
||||
1.0205175
|
||||
1.0046155
|
||||
0.99261284
|
||||
1.0137533
|
||||
1.0062878
|
||||
0.9882274
|
||||
1.0106694
|
||||
0.99471339
|
||||
1.002965
|
||||
0.99799306
|
||||
1.0134657
|
||||
1.0039836
|
||||
1.0066297
|
||||
1.0084027
|
||||
1.0245597
|
||||
0.9763106
|
||||
1.02033
|
||||
1.0147916
|
||||
1.0219756
|
||||
1.0008057
|
||||
1.0390485
|
||||
1.0210931
|
||||
0.99734292
|
||||
1.0171595
|
||||
1.0130723
|
||||
0.99892094
|
||||
0.98279425
|
||||
1.0066122
|
||||
0.98575479
|
||||
1.0078347
|
||||
1.0036275
|
||||
0.98116612
|
||||
0.99293813
|
||||
0.98842048
|
||||
0.97690963
|
||||
1.0093542
|
||||
1.0027533
|
||||
1.0156453
|
||||
0.99313547
|
||||
1.0004421
|
||||
0.99954572
|
||||
0.98736251
|
||||
1.0238741
|
||||
0.98768174
|
||||
1.000261
|
||||
0.98722229
|
||||
0.98398124
|
||||
1.0072403
|
||||
1.0009303
|
||||
0.99628068
|
||||
0.98538731
|
||||
0.99218841
|
||||
1.009118
|
||||
0.98810044
|
||||
1.0112788
|
||||
1.0004868
|
||||
0.99890858
|
||||
1.0029751
|
||||
1.0219324
|
||||
1.000043
|
||||
1.0058196
|
||||
1.014664
|
||||
1.0044
|
||||
1.0067619
|
||||
1.0008676
|
||||
0.99532428
|
||||
0.99224953
|
||||
0.99444046
|
||||
1.0003366
|
||||
1.0221002
|
||||
0.98855273
|
||||
1.0187089
|
||||
0.98416472
|
||||
1.0006988
|
||||
0.99933767
|
||||
1.0084427
|
||||
0.9910711
|
||||
0.99630044
|
||||
1.0041385
|
||||
0.99578819
|
||||
0.98859148
|
||||
1.0071189
|
||||
1.0057602
|
||||
1.0006798
|
||||
1.0040692
|
||||
0.99357917
|
||||
1.0055212
|
||||
0.99826781
|
||||
1.0294402
|
||||
1.0306182
|
||||
1.0163397
|
||||
0.99544135
|
||||
1.0089258
|
||||
1.0091866
|
||||
1.0031688
|
||||
1.0065311
|
||||
1.0162032
|
||||
1.006835
|
||||
0.98588242
|
||||
1.0031649
|
||||
1.0143694
|
||||
1.0071297
|
||||
1.0151235
|
||||
0.9950707
|
||||
1.0190895
|
||||
1.0036681
|
||||
1.0153039
|
||||
1.0031942
|
||||
0.97146165
|
||||
0.97647363
|
||||
1.0040287
|
||||
1.0074315
|
||||
1.0139851
|
||||
1.0084592
|
||||
1.013138
|
||||
1.0145484
|
||||
1.0008416
|
||||
0.97670707
|
||||
0.98714692
|
||||
1.0106897
|
||||
0.99046031
|
||||
1.0047648
|
||||
1.0064955
|
||||
0.99129614
|
||||
0.97537098
|
||||
0.99551383
|
||||
1.0000844
|
||||
1.0024149
|
||||
1.0294317
|
||||
0.97109038
|
||||
0.98520755
|
||||
1.0043495
|
||||
0.99858097
|
||||
0.99675168
|
||||
1.0112858
|
||||
1.0139032
|
||||
0.99391287
|
||||
1.026372
|
||||
1.003729
|
||||
1.0047125
|
||||
0.99687991
|
||||
1.0218475
|
||||
1.0125423
|
||||
1.0004841
|
||||
0.9992396
|
||||
0.98416027
|
||||
0.99245649
|
||||
1.007447
|
||||
0.99887608
|
||||
0.97908706
|
||||
1.0330613
|
||||
0.99897339
|
||||
0.9891648
|
||||
1.0148316
|
||||
0.99214239
|
||||
0.99630795
|
||||
0.99629966
|
||||
0.99137359
|
||||
NaN
|
||||
1.0008659
|
||||
];
|
||||
|
||||
gp_obs = [
|
||||
1.0193403
|
||||
1.0345762
|
||||
1.0011701
|
||||
1.0147224
|
||||
1.008392
|
||||
1.0488327
|
||||
1.0153551
|
||||
1.0099775
|
||||
1.0260561
|
||||
1.0172218
|
||||
1.0014374
|
||||
1.0184572
|
||||
1.0179988
|
||||
1.0060339
|
||||
1.0019537
|
||||
0.99179578
|
||||
1.004346
|
||||
1.0345153
|
||||
1.0004432
|
||||
0.98327074
|
||||
1.0007585
|
||||
1.0034378
|
||||
1.010532
|
||||
1.0121367
|
||||
1.0097161
|
||||
1.0166682
|
||||
1.0089513
|
||||
1.0194821
|
||||
1.0192704
|
||||
1.0220258
|
||||
1.020915
|
||||
1.0176156
|
||||
1.0040707
|
||||
1.0157694
|
||||
1.0357484
|
||||
1.0256259
|
||||
1.0240583
|
||||
1.0095152
|
||||
1.0241605
|
||||
1.0115295
|
||||
1.003636
|
||||
1.0222399
|
||||
1.0250969
|
||||
1.0068969
|
||||
1.0009829
|
||||
1.0166179
|
||||
1.0252018
|
||||
1.0211178
|
||||
0.99867851
|
||||
0.99594002
|
||||
0.9908135
|
||||
0.99762919
|
||||
0.99616309
|
||||
1.0058679
|
||||
0.99323315
|
||||
1.0132879
|
||||
0.98718922
|
||||
0.99739822
|
||||
0.97858593
|
||||
0.99128769
|
||||
0.98624299
|
||||
0.98447966
|
||||
1.0013312
|
||||
0.99189504
|
||||
0.98032699
|
||||
0.99332035
|
||||
1.0129565
|
||||
1.0007785
|
||||
1.0218292
|
||||
1.0030419
|
||||
1.0044453
|
||||
1.0156181
|
||||
1.0040112
|
||||
1.0081137
|
||||
1.0261598
|
||||
1.0053686
|
||||
1.0024674
|
||||
0.99883223
|
||||
1.0224791
|
||||
1.0074723
|
||||
1.0037807
|
||||
1.0348866
|
||||
1.0053664
|
||||
1.0140072
|
||||
1.017359
|
||||
1.0013916
|
||||
1.017887
|
||||
1.008987
|
||||
1.011771
|
||||
1.0201455
|
||||
1.0249464
|
||||
1.0159166
|
||||
1.0162718
|
||||
1.0312397
|
||||
1.0108745
|
||||
1.0132205
|
||||
1.0142484
|
||||
1.0178907
|
||||
1.0065039
|
||||
1.0190304
|
||||
1.0034406
|
||||
1.0053556
|
||||
1.012823
|
||||
1.0009983
|
||||
1.0073148
|
||||
1.0247254
|
||||
1.0140215
|
||||
1.0053603
|
||||
1.006169
|
||||
0.994725
|
||||
1.026685
|
||||
1.0012279
|
||||
1.0160733
|
||||
1.0119851
|
||||
1.0148392
|
||||
0.99760076
|
||||
1.0070377
|
||||
1.0066215
|
||||
0.98130614
|
||||
1.0127043
|
||||
1.0203824
|
||||
1.0067477
|
||||
0.99510728
|
||||
1.0188472
|
||||
1.0100108
|
||||
1.0146874
|
||||
1.0118012
|
||||
1.0111904
|
||||
0.97759194
|
||||
0.99081872
|
||||
0.98425915
|
||||
1.0026496
|
||||
0.98587189
|
||||
0.98648329
|
||||
1.0035766
|
||||
1.0094743
|
||||
0.99460644
|
||||
0.9953724
|
||||
1.0194434
|
||||
1.0065039
|
||||
1.0056522
|
||||
1.0160367
|
||||
1.006524
|
||||
1.0092492
|
||||
0.9864426
|
||||
0.98723638
|
||||
0.9994522
|
||||
1.0026778
|
||||
1.025553
|
||||
1.0030477
|
||||
NaN
|
||||
1.0045087
|
||||
0.99375289
|
||||
1.0017609
|
||||
1.0039766
|
||||
0.99976299
|
||||
1.0155671
|
||||
1.0192975
|
||||
1.0135507
|
||||
1.0099869
|
||||
1.0125994
|
||||
1.0050808
|
||||
1.0088531
|
||||
1.0135256
|
||||
1.0322097
|
||||
1.0065808
|
||||
0.99857525
|
||||
1.0008792
|
||||
0.9997691
|
||||
1.02875
|
||||
1.0177818
|
||||
1.0150152
|
||||
1.026416
|
||||
1.0209804
|
||||
1.010633
|
||||
1.009636
|
||||
1.0028257
|
||||
0.9896666
|
||||
1.0094002
|
||||
0.99958414
|
||||
1.0077797
|
||||
0.98933606
|
||||
1.0014885
|
||||
0.99875283
|
||||
1.005051
|
||||
1.016385
|
||||
1.0116282
|
||||
0.99774103
|
||||
1.0101802
|
||||
1.0281101
|
||||
1.0024654
|
||||
1.0174549
|
||||
1.0342738
|
||||
1.0160514
|
||||
1.0238797
|
||||
1.015626
|
||||
1.0075839
|
||||
1.0132055
|
||||
NaN
|
||||
1.0175022
|
||||
];
|
||||
|
||||
Loading…
Reference in New Issue