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Merge commit '34fb9f1c62c63e7e4ab41656a7690167449e596f'

time-shift
Johannes Pfeifer 2013-03-21 21:22:20 +01:00
parent 3bbdbf8387 34fb9f1c62
commit 82c6ff3b1b
14 changed files with 212 additions and 52 deletions
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8
doc/dynare.texi
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@ -2158,7 +2158,7 @@ Specifies the correlation of two variables.
In an estimation context, it is also possible to specify variances and
covariances on endogenous variables: in that case, these values are
interpreted as the calibration of the measurement errors on these
variables.
variables. This requires the @code{var_obs}-command to be specified before the @code{shocks}-block.
Here is an example:
@ -3747,14 +3747,14 @@ alternatives:
@item stderr @var{VARIABLE_NAME}
Indicates that the standard error of the exogenous variable
@var{VARIABLE_NAME}, or of the observation error associated with
@var{VARIABLE_NAME}, or of the observation error/measurement errors associated with
endogenous observed variable @var{VARIABLE_NAME}, is to be estimated
@item corr @var{VARIABLE_NAME1}, @var{VARIABLE_NAME2}
Indicates that the correlation between the exogenous variables
@var{VARIABLE_NAME1} and @var{VARIABLE_NAME2}, or the correlation of
the observation errors associated with endogenous observed variables
@var{VARIABLE_NAME1} and @var{VARIABLE_NAME2}, is to be estimated
the observation errors/measurement errors associated with endogenous observed variables
@var{VARIABLE_NAME1} and @var{VARIABLE_NAME2}, is to be estimated. Note that correlations set by previous @code{shocks}-blocks or @code{estimation}-commands are kept at their value set prior to estimation if they are not estimated again subsequently. Thus, the treatment is the same as in the case of deep parameters set during model calibration and not estimated.
@item @var{PARAMETER_NAME}
The name of a model parameter to be estimated

10
matlab/GetPosteriorParametersStatistics.m
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@ -15,7 +15,7 @@ function oo_ = GetPosteriorParametersStatistics(estim_params_, M_, options_, bay
% SPECIAL REQUIREMENTS
% None.
% Copyright (C) 2006-2012 Dynare Team
% Copyright (C) 2006-2013 Dynare Team
%
% This file is part of Dynare.
%
@ -163,7 +163,7 @@ end
if nvn
type = 'measurement_errors_std';
if TeX
fid = TeXBegin(OutputDirectoryName,M_.fname,3,'standard deviation of measurement errors')
fid = TeXBegin(OutputDirectoryName,M_.fname,3,'standard deviation of measurement errors');
end
disp(' ')
disp('standard deviation of measurement errors')
@ -174,17 +174,17 @@ if nvn
Draws = GetAllPosteriorDraws(ip,FirstMhFile,FirstLine,TotalNumberOfMhFiles,NumberOfDraws);
[post_mean, post_median, post_var, hpd_interval, post_deciles, density] = ...
posterior_moments(Draws,1,options_.mh_conf_sig);
name = deblank(options_.varobs(estim_params_.var_endo(i,1),:));
name = deblank(options_.varobs(estim_params_.nvn_observable_correspondence(i,1),:));
oo_ = Filloo(oo_,name,type,post_mean,hpd_interval,post_median,post_var,post_deciles,density);
else
try
name = deblank(options_.varobs(estim_params_.var_endo(i,1),:));
name = deblank(options_.varobs(estim_params_.nvn_observable_correspondence(i,1),:));
[post_mean,hpd_interval,post_var] = Extractoo(oo_,name,type);
catch
Draws = GetAllPosteriorDraws(ip,FirstMhFile,FirstLine,TotalNumberOfMhFiles,NumberOfDraws);
[post_mean, post_median, post_var, hpd_interval, post_deciles, density] = ...
posterior_moments(Draws,1,options_.mh_conf_sig);
name = deblank(options_.varobs(estim_params_.var_endo(i,1),:));
name = deblank(options_.varobs(estim_params_.nvn_observable_correspondence(i,1),:));
oo_ = Filloo(oo_,name,type,post_mean,hpd_interval,post_median,post_var,post_deciles,density);
end
end

4
matlab/PlotPosteriorDistributions.m
View File

@ -16,7 +16,7 @@ function oo_ = PlotPosteriorDistributions(estim_params_, M_, options_, bayestopt
% SPECIAL REQUIREMENTS
% none
% Copyright (C) 2005-2012 Dynare Team
% Copyright (C) 2005-2013 Dynare Team
%
% This file is part of Dynare.
%
@ -89,7 +89,7 @@ for i=1:npar
eval(['pmod = oo_.posterior_mode.shocks_std.' name ';'])
end
elseif i <= nvx+nvn
name = deblank(options_.varobs(estim_params_.var_endo(i-nvx,1),:));
name = deblank(options_.varobs(estim_params_.nvn_observable_correspondence(i-nvx,1),:));
eval(['x1 = oo_.posterior_density.measurement_errors_std.' name '(:,1);'])
eval(['f1 = oo_.posterior_density.measurement_errors_std.' name '(:,2);'])
eval(['oo_.prior_density.mearsurement_errors_std.' name '(:,1) = x2;'])

6
matlab/dyn_forecast.m
View File

@ -87,8 +87,10 @@ switch task
i_var_obs = [ i_var_obs; tmp];
trend_coeffs = [trend_coeffs; oo_.Smoother.TrendCoeffs(i)];
end
end
trend = trend_coeffs*(gend+(1-M_.maximum_lag:horizon));
end
if ~isempty(trend_coeffs)
trend = trend_coeffs*(gend+(1-M_.maximum_lag:horizon));
end
end
global bayestopt_
if isfield(bayestopt_,'mean_varobs')

31
matlab/dynare_estimation_1.m
View File

@ -77,6 +77,24 @@ if ~isequal(estim_params_.ncx,nnz(tril(M_.Sigma_e,-1)))
end
end
M_.H_is_diagonal = 1;
if estim_params_.ncn || ~isequal(nnz(M_.H),length(M_.H))
M_.H_is_diagonal = 0;
end
% Set the correlation matrix of measurement errors if necessary.
if ~isequal(estim_params_.ncn,nnz(tril(M_.H,-1)))
M_.Correlation_matrix_ME = diag(1./sqrt(diag(M_.H)))*M_.H*diag(1./sqrt(diag(M_.H)));
% Remove NaNs appearing because of variances calibrated to zero.
if any(isnan(M_.Correlation_matrix_ME))
zero_variance_idx = find(~diag(M_.H));
for i=1:length(zero_variance_idx)
M_.Correlation_matrix_ME(zero_variance_idx(i),:) = 0;
M_.Correlation_matrix_ME(:,zero_variance_idx(i)) = 0;
end
end
end
data = dataset_.data;
rawdata = dataset_.rawdata;
data_index = dataset_.missing.aindex;
@ -554,7 +572,7 @@ if any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
disp(tit1)
ip = nvx+1;
for i=1:nvn
name = deblank(options_.varobs(estim_params_.var_endo(i,1),:));
name = deblank(options_.varobs(estim_params_.nvn_observable_correspondence(i,1),:));
disp(sprintf('%-*s %7.3f %8.4f %7.4f %7.4f %4s %6.4f', ...
header_width,name,bayestopt_.p1(ip), ...
xparam1(ip),stdh(ip),tstath(ip), ...
@ -653,7 +671,7 @@ elseif ~any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
disp(tit1)
ip = nvx+1;
for i=1:nvn
name = deblank(options_.varobs(estim_params_.var_endo(i,1),:));
name = deblank(options_.varobs(estim_params_.nvn_observable_correspondence(i,1),:));
disp(sprintf('%-*s %8.4f %7.4f %7.4f',header_width,name,xparam1(ip),stdh(ip),tstath(ip)))
eval(['oo_.mle_mode.measurement_errors_std.' name ' = xparam1(ip);']);
eval(['oo_.mle_std.measurement_errors_std.' name ' = stdh(ip);']);
@ -799,7 +817,7 @@ if any(bayestopt_.pshape > 0) && options_.TeX %% Bayesian estimation (posterior
fprintf(fidTeX,'\\hline \\hline \\endlastfoot \n');
ip = nvx+1;
for i=1:nvn
idx = strmatch(options_.varobs(estim_params_.var_endo(i,1),:),M_.endo_names);
idx = strmatch(options_.varobs(estim_params_.nvn_observable_correspondence(i,1),:),M_.endo_names);
fprintf(fidTeX,'$%s$ & %4s & %7.3f & %6.4f & %8.4f & %7.4f \\\\ \n',...
deblank(M_.endo_names_tex(idx,:)), ...
deblank(pnames(bayestopt_.pshape(ip)+1,:)), ...
@ -1100,7 +1118,8 @@ if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.psha
hh = dyn_figure(options_,'Name','Smoothed observation errors');
NAMES = [];
if options_.TeX, TeXNAMES = []; end
for i=1:min(nstar,number_of_plots_to_draw-(nbplt-1)*nstar)
nstar0=min(nstar,number_of_plots_to_draw-(nbplt-1)*nstar);
for i=1:nstar0
k = (plt-1)*nstar+i;
subplot(nr,nc,i);
plot([1 gend],[0 0],'-r','linewidth',.5)
@ -1131,7 +1150,7 @@ if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.psha
dyn_saveas(hh,[M_.fname '_SmoothedObservationErrors' int2str(plt)],options_);
if options_.TeX
fprintf(fidTeX,'\\begin{figure}[H]\n');
for jj = 1:nstar
for jj = 1:nstar0
fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(jj,:)),deblank(TeXNAMES(jj,:)));
end
fprintf(fidTeX,'\\centering \n');
@ -1168,7 +1187,7 @@ if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.psha
for i=1:nstar0,
k = (plt-1)*nstar+i;
subplot(nr,nc,i);
plot(1:gend,yf(k,:),'--r','linewidth',1)
plot(1:gend,yf(k,:),'-r','linewidth',1)
hold on
plot(1:gend,rawdata(:,k),'--k','linewidth',1)
hold off

8
matlab/get_posterior_parameters.m
View File

@ -12,7 +12,7 @@ function xparam = get_posterior_parameters(type)
% SPECIAL REQUIREMENTS
% None.
% Copyright (C) 2006-2009 Dynare Team
% Copyright (C) 2006-2013 Dynare Team
%
% This file is part of Dynare.
%
@ -49,7 +49,7 @@ for i=1:nvx
end
for i=1:nvn
k1 = estim_params_.var_endo(i,1);
k1 = estim_params_.nvn_observable_correspondence(i,1);
name1 = deblank(options_.varobs(k1,:));
xparam(m) = eval(['oo_.posterior_' type '.measurement_errors_std.' name1]);
m = m+1;
@ -67,8 +67,8 @@ for i=1:ncx
end
for i=1:ncn
k1 = estim_params_.corrn(i,1);
k2 = estim_params_.corrn(i,2);
k1 = estim_params_.corrn_observable_correspondence(i,1);
k2 = estim_params_.corrn_observable_correspondence(i,2);
name1 = deblank(options_.varobs(k1,:));
name2 = deblank(options_.varobs(k2,:));
xparam(m) = eval(['oo_.posterior_' type '.measurement_errors_corr.' name1 '_' name2]);

8
matlab/get_the_name.m
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@ -40,7 +40,7 @@ function [nam,texnam] = get_the_name(k,TeX,M_,estim_params_,options_)
%! @end deftypefn
%@eod:
% Copyright (C) 2004-2011 Dynare Team
% Copyright (C) 2004-2013 Dynare Team
%
% This file is part of Dynare.
%
@ -73,11 +73,11 @@ if k <= nvx
texnam = ['$ SE_{' tname '} $'];
end
elseif k <= (nvx+nvn)
vname = deblank(options_.varobs(estim_params_.var_endo(k-estim_params_.nvx,1),:));
vname = deblank(options_.varobs(estim_params_.nvn_observable_correspondence(k-estim_params_.nvx,1),:));
nam=['SE_EOBS_',vname];
if TeX
tname = deblank(M_.endo_names_tex(estim_params_.var_endo(k-estim_params_.nvx,1),:));
texnam = ['$ SE_{' tname '} $'];
texnam = ['$ EOBS SE_{' tname '} $'];
end
elseif k <= (nvx+nvn+ncx)
jj = k - (nvx+nvn);
@ -97,7 +97,7 @@ elseif k <= (nvx+nvn+ncx+ncn)
nam=['CC_EOBS_' vname];
if TeX
tname = [deblank(M_.endo_names_tex(k1,:)) ',' deblank(M_.endo_names_tex(k2,:))];
texnam =['$ CC_{' tname '} $'];
texnam =['$ EOBS CC_{' tname '} $'];
end
else
jj = k - (nvx+nvn+ncx+ncn);

1
matlab/global_initialization.m
View File

@ -422,6 +422,7 @@ oo_.exo_det_simul = [];
M_.params = [];
M_.endo_histval = [];
M_.Correlation_matrix = [];
M_.Correlation_matrix_ME = [];
% homotopy
options_.homotopy_mode = 0;

10
matlab/prior_posterior_statistics.m
View File

@ -19,7 +19,7 @@ function prior_posterior_statistics(type,dataset)
% See the comments random_walk_metropolis_hastings.m funtion.
% Copyright (C) 2005-2012 Dynare Team
% Copyright (C) 2005-2013 Dynare Team
%
% This file is part of Dynare.
%
@ -298,20 +298,20 @@ end
if options_.filtered_vars
pm3(endo_nbr,gend,ifil(1),B,'Updated Variables',...
'',varlist,'tit_tex',M_.endo_names,...
'',varlist,M_.endo_names_tex,M_.endo_names,...
varlist,'UpdatedVariables',DirectoryName, ...
'_update');
pm3(endo_nbr,gend+1,ifil(4),B,'One step ahead forecast',...
'',varlist,'tit_tex',M_.endo_names,...
'',varlist,M_.endo_names_tex,M_.endo_names,...
varlist,'FilteredVariables',DirectoryName,'_filter_step_ahead');
end
if options_.forecast
pm3(endo_nbr,horizon+maxlag,ifil(6),B,'Forecasted variables (mean)',...
'',varlist,'tit_tex',M_.endo_names,...
'',varlist,M_.endo_names_tex,M_.endo_names,...
varlist,'MeanForecast',DirectoryName,'_forc_mean');
pm3(endo_nbr,horizon+maxlag,ifil(6),B,'Forecasted variables (point)',...
'',varlist,'tit_tex',M_.endo_names,...
'',varlist,M_.endo_names_tex,M_.endo_names,...
varlist,'PointForecast',DirectoryName,'_forc_point');
end

2
matlab/prior_posterior_statistics_core.m
View File

@ -140,7 +140,7 @@ if run_smoother
end
end
if nvn
stock_error = NaN(endo_nbr,gend,MAX_nerro);
stock_error = NaN(size(varobs,1),gend,MAX_nerro);
end
if naK
stock_filter_step_ahead =NaN(length(options_.filter_step_ahead),endo_nbr,gend+max(options_.filter_step_ahead),MAX_naK);

4
matlab/row_header_width.m
View File

@ -13,7 +13,7 @@ function w=row_header_width(M_,estim_params_,bayestopt_)
% SPECIAL REQUIREMENTS
% None.
% Copyright (C) 2006-2009 Dynare Team
% Copyright (C) 2006-2013 Dynare Team
%
% This file is part of Dynare.
%
@ -64,7 +64,7 @@ if ncx
end
end
if ncn
for i=1:nvn
for i=1:ncn
k1 = estim_params_.corrn(i,1);
k2 = estim_params_.corrn(i,2);
w = max(w,length(deblank(M_.endo_names(k1,:)))...

18
matlab/set_all_parameters.m
View File

@ -71,9 +71,8 @@ offset = nvx;
% setting measument error variance
if nvn
var_endo = estim_params.var_endo;
for i=1:nvn
k = var_endo(i,1);
k = estim_params.nvn_observable_correspondence(i,1);
H(k,k) = xparam1(i+offset)^2;
end
end
@ -83,7 +82,7 @@ offset = nvx+nvn;
% setting shocks covariances
if ~isempty(M.Correlation_matrix)
Sigma_e = diag(sqrt(diag(Sigma_e)))*M.Correlation_matrix*diag(sqrt(diag(Sigma_e)));
Sigma_e = diag(sqrt(diag(Sigma_e)))*M.Correlation_matrix*diag(sqrt(diag(Sigma_e))); % use of old correlation matrix is correct due to the diagonal structure and later only using the hence correctly updated diagonal entries of Sigma_e
end
if ncx
corrx = estim_params.corrx;
@ -100,11 +99,16 @@ end
% update offset
offset = nvx+nvn+ncx;
% setting measurement error covariances
if ~isempty(M.Correlation_matrix_ME)
H = diag(sqrt(diag(H)))*M.Correlation_matrix_ME*diag(sqrt(diag(H)));
end
if ncn
corrn = estim_params.corrn;
corrn_observable_correspondence = estim_params.corrn_observable_correspondence;
for i=1:ncn
k1 = corr(i,1);
k2 = corr(i,2);
k1 = corrn_observable_correspondence(i,1);
k2 = corrn_observable_correspondence(i,2);
M.Correlation_matrix_ME(k1,k2) = xparam1(i+offset);
M.Correlation_matrix_ME(k2,k1) = M.Correlation_matrix_ME(k1,k2);
H(k1,k2) = xparam1(i+offset)*sqrt(H(k1,k1)*H(k2,k2));
H(k2,k1) = H(k1,k2);
end
@ -122,6 +126,6 @@ end
if nvx || ncx
M.Sigma_e = Sigma_e;
end
if nvn
if nvn || ncn
M.H = H;
end

29
matlab/set_prior.m
View File

@ -18,7 +18,7 @@ function [xparam1, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params
% SPECIAL REQUIREMENTS
% None
% Copyright (C) 2003-2011 Dynare Team
% Copyright (C) 2003-2013 Dynare Team
%
% This file is part of Dynare.
%
@ -41,11 +41,11 @@ ncx = size(estim_params_.corrx,1);
ncn = size(estim_params_.corrn,1);
np = size(estim_params_.param_vals,1);
estim_params_.nvx = nvx;
estim_params_.nvn = nvn;
estim_params_.ncx = ncx;
estim_params_.ncn = ncn;
estim_params_.np = np;
estim_params_.nvx = nvx; %exogenous shock variances
estim_params_.nvn = nvn; %endogenous variances, i.e. measurement error
estim_params_.ncx = ncx; %exogenous shock correlations
estim_params_.ncn = ncn; % correlation between endogenous variables, i.e. measurement error.
estim_params_.np = np; % other parameters of the model
xparam1 = [];
ub = [];
@ -74,6 +74,7 @@ if nvx
bayestopt_.name = cellstr(M_.exo_names(estim_params_.var_exo(:,1),:));
end
if nvn
estim_params_.nvn_observable_correspondence=NaN(nvn,1); % stores number of corresponding observable
if isequal(M_.H,0)
nvarobs = size(options_.varobs,1);
M_.H = zeros(nvarobs,nvarobs);
@ -83,7 +84,7 @@ if nvn
if isempty(obsi_)
error(['The variable ' deblank(M_.endo_names(estim_params_.var_endo(i,1),:)) ' has to be declared as observable since you assume a measurement error on it.'])
end
estim_params_.var_endo(i,1) = obsi_;
estim_params_.nvn_observable_correspondence(i,1)=obsi_;
end
xparam1 = [xparam1; estim_params_.var_endo(:,2)];
ub = [ub; estim_params_.var_endo(:,4)];
@ -94,7 +95,7 @@ if nvn
bayestopt_.p3 = [ bayestopt_.p3; estim_params_.var_endo(:,8)];
bayestopt_.p4 = [ bayestopt_.p4; estim_params_.var_endo(:,9)];
bayestopt_.jscale = [ bayestopt_.jscale; estim_params_.var_endo(:,10)];
bayestopt_.name = [ bayestopt_.name; cellstr(options_.varobs(estim_params_.var_endo(:,1),:))];
bayestopt_.name = [ bayestopt_.name; cellstr(options_.varobs(estim_params_.nvn_observable_correspondence,:))];
end
if ncx
xparam1 = [xparam1; estim_params_.corrx(:,3)];
@ -111,6 +112,7 @@ if ncx
repmat(', ',ncx,1) , deblank(M_.exo_names(estim_params_.corrx(:,2),:))])];
end
if ncn
estim_params_.corrn_observable_correspondence=NaN(ncn,2);
if isequal(M_.H,0)
nvarobs = size(options_.varobs,1);
M_.H = zeros(nvarobs,nvarobs);
@ -125,8 +127,15 @@ if ncn
bayestopt_.p4 = [ bayestopt_.p4; estim_params_.corrn(:,10)];
bayestopt_.jscale = [ bayestopt_.jscale; estim_params_.corrn(:,11)];
bayestopt_.name = [bayestopt_.name; cellstr([repmat('corr ',ncn,1) ...
deblank(M_.exo_names(estim_params_.corrn(:,1),:)) ...
repmat(', ',ncn,1) , deblank(M_.exo_names(estim_params_.corrn(:,2),:))])];
deblank(M_.endo_names(estim_params_.corrn(:,1),:)) ...
repmat(', ',ncn,1) , deblank(M_.endo_names(estim_params_.corrn(:,2),:))])];
for i=1:ncn
k1 = estim_params_.corrn(i,1);
k2 = estim_params_.corrn(i,2);
obsi1 = strmatch(deblank(M_.endo_names(k1,:)),deblank(options_.varobs),'exact'); %find correspondence to varobs to construct H in set_all_paramters
obsi2 = strmatch(deblank(M_.endo_names(k2,:)),deblank(options_.varobs),'exact');
estim_params_.corrn_observable_correspondence(i,:)=[obsi1,obsi2]; %save correspondence
end
end
if np
xparam1 = [xparam1; estim_params_.param_vals(:,2)];

125
tests/measurement_errors/fs2000_corr_me_ml_mcmc/fs2000_corr_ME.mod Normal file
View File

@ -0,0 +1,125 @@
/*
* 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;
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;
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;
estimation(mode_compute=9,order=1,datafile=fsdat_simul,mode_check,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;
estimated_params;
//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=0,mode_file=fs2000_corr_ME_mh_mode,order=1,datafile=fsdat_simul,mode_check,smoother,filter_decomposition,mh_replic=2000, mh_nblocks=2, 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);