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Merge remote-tracking branch 'jpfeifer/mode_compute'

time-shift
Sébastien Villemot 2013-11-05 16:20:04 +01:00
parent 27f525e1e4 0d669a73cb
commit 8a6e23845b
5 changed files with 212 additions and 2 deletions
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21
doc/dynare.texi
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@ -4389,6 +4389,7 @@ the total number of Metropolis draws available. Default:
The fraction of initially generated parameter vectors to be dropped as a burnin before using posterior simulations. Default: @code{0.5}
@item mh_jscale = @var{DOUBLE}
@anchor{mh_jscale}
The scale to be used for the jumping distribution in
Metropolis-Hastings algorithm. The default value is rarely
satisfactory. This option must be tuned to obtain, ideally, an
@ -4485,6 +4486,26 @@ value of that function as the posterior mode.
@noindent
Default value is @code{4}.
@item MCMC_jumping_covariance = hessian|prior_variance|identity_matrix|@var{filename}
Tells Dynare which covariance to use for the proposal density of the MCMC sampler. @code{MCMC_jumping_covariance} can be one of the following:
@table @code
@item hessian
Uses the Hessian matrix computed at the mode.
@item prior_variance
Uses the prior variances. No infinite prior variances are allowed in this case.
@item identity_matrix
Uses an identity matrix.
@item filename
Loads an arbitrary user-specified covariance matrix from @code{filename.mat}. The covariance matrix must be saved in a variable named @code{jumping_covariance}, must be square, positive definite, and have the same dimension as the number of estimated parameters.
@end table
@noindent
Note that the covariance matrices are stil scaled with @ref{mh_jscale}. Default value is @code{hessian}.
@item mode_check
Tells Dynare to plot the posterior density for values around the
computed mode for each estimated parameter in turn. This is helpful to

33
matlab/dynare_estimation_1.m
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@ -609,8 +609,8 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
error(['dynare_estimation:: mode_compute = ' int2str(options_.mode_compute) ' option is unknown!'])
end
end
if ~isequal(options_.mode_compute,6)
if options_.cova_compute == 1
if ~isequal(options_.mode_compute,6) %always already computes covariance matrix
if options_.cova_compute == 1 %user did not request covariance not to be computed
if options_.analytic_derivation && strcmp(func2str(objective_function),'dsge_likelihood'),
ana_deriv = options_.analytic_derivation;
options_.analytic_derivation = 2;
@ -636,6 +636,35 @@ if options_.cova_compute == 0
hh = [];%NaN(length(xparam1),length(xparam1));
end
switch options_.MCMC_jumping_covariance
case 'hessian' %Baseline
%do nothing and use hessian from mode_compute
case 'prior_variance' %Use prior variance
if any(isinf(bayestopt_.p2))
error('Infinite prior variances detected. You cannot use the prior variances as the proposal density, if some variances are Inf.')
else
hh = diag(1./(bayestopt_.p2.^2));
end
case 'identity_matrix' %Use identity
hh = eye(nx);
otherwise %user specified matrix in file
try
load(options_.MCMC_jumping_covariance,'jumping_covariance')
hh=jumping_covariance;
catch
error(['No matrix named ''jumping_covariance'' could be found in ',options_.MCMC_jumping_covariance,'.mat'])
end
[nrow, ncol]=size(hh);
if ~isequal(nrow,ncol) && ~isequal(nrow,nx) %check if square and right size
error(['jumping_covariance matrix must be square and have ',num2str(nx),' rows and columns'])
end
try %check for positive definiteness
chol(hh);
catch
error(['Specified jumping_covariance is not positive definite'])
end
end
if ~options_.mh_posterior_mode_estimation && options_.cova_compute
try
chol(hh);

1
matlab/global_initialization.m
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@ -381,6 +381,7 @@ options_.mh_nblck = 2;
options_.mh_recover = 0;
options_.mh_replic = 20000;
options_.recursive_estimation_restart = 0;
options_.MCMC_jumping_covariance='hessian';
options_.mode_compute = 4;
options_.mode_file = '';

86
tests/estimation/fs2000_MCMC_jumping_covariance.mod Normal file
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@ -0,0 +1,86 @@
// See fs2000.mod in the examples/ directory for details on the model
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;
initval;
k = 6;
m = mst;
P = 2.25;
c = 0.45;
e = 1;
W = 4;
R = 1.02;
d = 0.85;
n = 0.19;
l = 0.86;
y = 0.6;
gy_obs = exp(gam);
gp_obs = exp(-gam);
dA = exp(gam);
end;
shocks;
var e_a; stderr 0.014;
var e_m; stderr 0.005;
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, 0.223;
psi, beta_pdf, 0.65, 0.05;
del, beta_pdf, 0.01, 0.005;
stderr e_a, inv_gamma_pdf, 0.035449, .1;
stderr e_m, inv_gamma_pdf, 0.008862, .1;
end;
varobs gp_obs gy_obs;
options_.solve_tolf = 1e-12;
options_.mode_compute=4;
options_.plot_priors=0;
options_.MCMC_jumping_covariance='hessian';
estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=1000,mh_nblocks=1,mh_jscale=0.8);
load fs2000_MCMC_jumping_covariance_mode hh;
jumping_covariance=diag(diag(hh));
save test_matrix jumping_covariance;
options_.MCMC_jumping_covariance='prior_variance';
estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=1000,mh_nblocks=1,mh_jscale=0.01);
options_.MCMC_jumping_covariance='identity_matrix';
estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=1000,mh_nblocks=1,mh_jscale=0.0001);
options_.MCMC_jumping_covariance='test_matrix';
estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=1000,mh_nblocks=1,mh_jscale=0.8);

73
tests/estimation/fs2000_MCMC_jumping_covariance_steadystate.m Normal file
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@ -0,0 +1,73 @@
% computes the steady state of fs2000 analyticaly
% largely inspired by the program of F. Schorfheide
% Copyright (C) 2004-2010 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/>.
function [ys,check] = fs2000_steadystate(ys,exe)
global M_
alp = M_.params(1);
bet = M_.params(2);
gam = M_.params(3);
mst = M_.params(4);
rho = M_.params(5);
psi = M_.params(6);
del = M_.params(7);
check = 0;
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;
ys =[
m
P
c
e
W
R
k
d
n
l
gy_obs
gp_obs
y
dA ];