diff --git a/doc/dynare.texi b/doc/dynare.texi
index 6a553ff45e36510bca80b6568f4426cc2f4cbe5b..a48c6a5ab6f4d660ca6771fac48a657aae67610d 100644
--- a/doc/dynare.texi
+++ b/doc/dynare.texi
@@ -2189,7 +2189,7 @@ possible types of simulations in stochastic mode:
@itemize
@item
-@ref{stoch_simul}, if the @code{periods} options is specified
+@ref{stoch_simul}, if the @code{periods} option is specified
@item
@ref{forecast} as the initial point at which the forecasts are computed
@@ -2419,7 +2419,7 @@ Moreover, as only states enter the recursive policy functions, all values specif
@itemize
@item
-in @ref{stoch_simul}, if the @code{periods} options is specified. Note that this only affects the starting point for the simulation, but not for the impulse response functions.
+in @ref{stoch_simul}, if the @code{periods} option is specified. Note that this only affects the starting point for the simulation, but not for the impulse response functions.
@item
in @ref{forecast} as the initial point at which the forecasts are computed
@@ -3925,6 +3925,11 @@ Tolerance for the suppression of small terms in the display of decision rules. R
smaller than @code{dr_display_tol} are not displayed.
Default value: @code{1e-6}.
+@item contemporaneous_correlation
+@anchor{contemporaneous_correlation}
+Saves the contemporaneous correlation between the endogenous variables in @code{oo_.contemporaneous_correlation}.
+Requires the @code{nocorr}-option not to be set.
+
@end table
@outputhead
@@ -3942,6 +3947,8 @@ If options @code{irf} is different from zero, sets @code{oo_.irfs}
the global workspace (this latter way of accessing the IRFs is
deprecated and will disappear in a future version).
+If the option @code{contemporaneous_correlation} is different from 0, sets
+@code{oo_.contemporaneous_correlation}, which is described below.
@customhead{Example 1}
@@ -3971,14 +3978,15 @@ response functions on 60 periods for variables @code{y} and @code{k}.
@defvr {MATLAB/Octave variable} oo_.mean
After a run of @code{stoch_simul}, contains the mean of the endogenous
variables. Contains theoretical mean if the @code{periods} option is
-not present, and empirical mean otherwise. The variables are arranged
+not present, and simulated mean otherwise. The variables are arranged
in declaration order.
@end defvr
@defvr {MATLAB/Octave variable} oo_.var
After a run of @code{stoch_simul}, contains the variance-covariance of
the endogenous variables. Contains theoretical variance if the
-@code{periods} option is not present (or an approximation thereof for @code{order=2}), and empirical variance
+@code{periods} option is not present (or an approximation thereof for @code{order=2}),
+and simulated variance
otherwise. The variables are arranged in declaration order.
@end defvr
@@ -3990,7 +3998,7 @@ number of the matrix in the cell array corresponds to the order of
autocorrelation. The option @code{ar} specifies the number of
autocorrelation matrices available. Contains theoretical
autocorrelations if the @code{periods} option is not present (or an approximation thereof for @code{order=2}), and
-empirical autocorrelations otherwise. The field is only created if stationary variables are present.
+simulated autocorrelations otherwise. The field is only created if stationary variables are present.
The element @code{oo_.autocorr@{i@}(k,l)} is equal to the correlation
between @math{y^k_t} and @math{y^l_{t-i}}, where @math{y^k}
@@ -4024,13 +4032,18 @@ If a second order approximation has been requested, contains the
vector of the mean correction terms.
@end table
-In case of @code{order=2}, the theoretical second moments are a second order accurate approximation of the true second moments, see @code{conditional_variance_decomposition}.
+In case of @code{order=2}, the theoretical second moments are a second order
+accurate approximation of the true second moments, see @code{conditional_variance_decomposition}.
@end defvr
@anchor{oo_.variance_decomposition}
@defvr {MATLAB/Octave variable} oo_.variance_decomposition
-After a run of @code{stoch_simul} when requesting theoretical moments (@code{periods=0}), contains a matrix with the result of the unconditional variance decomposition (i.e. at horizon infinity). The first dimension corresponds to the endogenous variables (in the order of declaration) and the second dimension corresponds to exogenous variables (in the order of declaration). Numbers are in percent and sum up to 100 across columns.
+After a run of @code{stoch_simul} when requesting theoretical moments (@code{periods=0}),
+contains a matrix with the result of the unconditional variance decomposition (i.e. at horizon infinity).
+The first dimension corresponds to the endogenous variables (in the order of declaration) and
+the second dimension corresponds to exogenous variables (in the order of declaration).
+Numbers are in percent and sum up to 100 across columns.
@end defvr
@anchor{oo_.conditional_variance_decomposition}
@@ -4044,6 +4057,15 @@ the order of declaration), the third dimension corresponds to
exogenous variables (in the order of declaration).
@end defvr
+@anchor{oo_.contemporaneous_correlation}
+@defvr {MATLAB/Octave variable} oo_.contemporaneous_correlation
+After a run of @code{stoch_simul} with the
+@code{contemporaneous_correlation} option, contains theoretical contemporaneous correlations if the
+@code{periods} option is not present (or an approximation thereof for @code{order=2}),
+and simulated contemporaneous correlations otherwise. The variables are arranged in declaration order.
+@end defvr
+
+
@defvr {MATLAB/Octave variable} oo_.irfs
After a run of @code{stoch_simul} with option @code{irf} different
from zero, contains the impulse responses, with the following naming
@@ -5380,6 +5402,10 @@ variables. Results are stored in
@code{oo_.PosteriorTheoreticalMoments} (@pxref{oo_.PosteriorTheoreticalMoments}). The number of lags in the autocorrelation function is
controlled by the @code{ar} option.
+@item contemporaneous_correlation
+@xref{contemporaneous_correlation}. Results are stored in @code{oo_.PosteriorTheoreticalMoments}.
+Note that the @code{nocorr}-option has no effect.
+
@item conditional_variance_decomposition = @var{INTEGER}
See below.
@@ -5924,6 +5950,9 @@ where @var{THEORETICAL_MOMENT} is one of the following:
@item covariance
Variance-covariance of endogenous variables
+@item contemporaneous_correlation
+Contemporaneous correlation of endogenous variables when the @ref{contemporaneous_correlation} option is specified.
+
@item correlation
Auto- and cross-correlation of endogenous variables. Fields are vectors with correlations from 1 up to order @code{options_.ar}
diff --git a/matlab/check_posterior_analysis_data.m b/matlab/check_posterior_analysis_data.m
index 243b2d48da4a4653f7c12e04b0a6c6eb2b578d81..b1d25b3d131dd2b35f0665098a2ddacaf4c92218 100644
--- a/matlab/check_posterior_analysis_data.m
+++ b/matlab/check_posterior_analysis_data.m
@@ -1,6 +1,23 @@
function [info,description] = check_posterior_analysis_data(type,M_)
+% function [info,description] = check_posterior_analysis_data(type,M_)
+% Checks the status of posterior analysis and in particular if files need to be
+% created or updated; called by posterior_analysis.m
+%
+% Inputs:
+% type [string] name of the posterior moment considered
+% M_ [structure] Dynare model structure
+%
+% Outputs:
+% info [scalar] return code
+% info = 1; % select_posterior_draws has to be called first.
+% info = 2; % _posterior_draws files have to be updated.
+% info = 3; % Ok! posterior draws files are up to date ;
+% info = 4; % posterior draws have to be processed.
+% info = 5; % posterior data files have to be updated.
+% info = 6; % Ok (nothing to do ;-)
+% description [string] Message corresponding to info
-% Copyright (C) 2008-2013 Dynare Team
+% Copyright (C) 2008-2015 Dynare Team
%
% This file is part of Dynare.
%
diff --git a/matlab/check_prior_analysis_data.m b/matlab/check_prior_analysis_data.m
index cb4e2913c665960201cd2c93c33a5cc4a0eb4081..8d74ba943c655625901c879d3e3d0d3c3472993e 100644
--- a/matlab/check_prior_analysis_data.m
+++ b/matlab/check_prior_analysis_data.m
@@ -1,4 +1,23 @@
function [info,description] = check_prior_analysis_data(type,M_)
+% function [info,description] = check_prior_analysis_data(type,M_)
+% Checks the status of prior analysis and in particular if files need to be
+% created or updated; called by prior_analysis.m
+%
+% Inputs:
+% type [string] name of the posterior moment considered
+% M_ [structure] Dynare model structure
+%
+% Outputs:
+% info [scalar] return code
+% info = 1; % prior_sampler has to be called first.
+% info = 2; % _prior_draws files have to be updated.
+% info = 3; % Ok! prior draws files are up to date ;
+% info = 4; % prior draws have to be processed.
+% info = 5; % prior data files have to be updated.
+% info = 6; % Ok (nothing to do ;-)
+% description [string] Message corresponding to info
+
+
% Copyright (C) 2009-2011 Dynare Team
%
% This file is part of Dynare.
@@ -28,7 +47,6 @@ if ~exist([ M_.dname '/prior/draws'],'dir')
end
prior_draws_info = dir([ M_.dname '/prior/draws/prior_draws*.mat']);
-name_of_the_last_prior_draw_file = prior_draws_info(end).name;
date_of_the_last_prior_draw_file = prior_draws_info(end).datenum;
%% Get informations about _posterior_draws files.
@@ -39,7 +57,6 @@ if isempty(prior_draws_info)
end
return
else
- number_of_last_prior_draws_file = length(prior_draws_info);
date_of_the_prior_definition = get_date_of_a_file([ M_.dname '/prior/definition.mat']);
if date_of_the_prior_definition>date_of_the_last_prior_draw_file
info = 2;
diff --git a/matlab/compute_moments_varendo.m b/matlab/compute_moments_varendo.m
index 8a294606f9d15ec9fe24f40f70bca8c53b831cac..75952c78ade5f703ed367dfa8a322cbdb6fe3167 100644
--- a/matlab/compute_moments_varendo.m
+++ b/matlab/compute_moments_varendo.m
@@ -33,6 +33,9 @@ function oo_ = compute_moments_varendo(type,options_,M_,oo_,var_list_)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
+
+fprintf('Estimation::compute_moments_varendo: I''m computing endogenous moments (this may take a while)... ');
+
if strcmpi(type,'posterior')
posterior = 1;
if nargin==4
@@ -47,13 +50,11 @@ elseif strcmpi(type,'prior')
end
end
else
- disp('compute_moments_varendo:: Unknown type!')
- error()
+ error('compute_moments_varendo:: Unknown type!')
end
NumberOfEndogenousVariables = rows(var_list_);
NumberOfExogenousVariables = M_.exo_nbr;
-list_of_exogenous_variables = M_.exo_names;
NumberOfLags = options_.ar;
NoDecomposition = options_.nodecomposition;
if isfield(options_,'conditional_variance_decomposition')
@@ -128,3 +129,5 @@ if M_.exo_nbr > 1
end
end
end
+
+fprintf(' Done!\n');
diff --git a/matlab/covariance_mc_analysis.m b/matlab/covariance_mc_analysis.m
index d98992f0eb9b17ebe72fac678c57f7da94ccc6cd..59cc635f2bd72f01b45fc6490d0942befbecf6d5 100644
--- a/matlab/covariance_mc_analysis.m
+++ b/matlab/covariance_mc_analysis.m
@@ -1,4 +1,4 @@
-function oo_ = covariance_mc_analysis(NumberOfSimulations,type,dname,fname,vartan,nvar,var1,var2,mh_conf_sig,oo_)
+function oo_ = covariance_mc_analysis(NumberOfSimulations,type,dname,fname,vartan,nvar,var1,var2,mh_conf_sig,oo_,options_)
% This function analyses the (posterior or prior) distribution of the
% endogenous variables' covariance matrix.
%
@@ -14,6 +14,7 @@ function oo_ = covariance_mc_analysis(NumberOfSimulations,type,dname,fname,varta
% mh_conf_sig [double] 2 by 1 vector with upper
% and lower bound of HPD intervals
% oo_ [structure] Dynare structure where the results are saved.
+% options_ [structure] Dynare options structure
%
% OUTPUTS
% oo_ [structure] Dynare structure where the results are saved.
@@ -86,10 +87,21 @@ end
ListOfFiles = dir([ PATH fname '_' TYPE '2ndOrderMoments*.mat']);
i1 = 1; tmp = zeros(NumberOfSimulations,1);
+if options_.contemporaneous_correlation
+ tmp_corr_mat = zeros(NumberOfSimulations,1);
+ cov_pos=symmetric_matrix_index(indx1,indx2,nvar);
+ var_pos_1=symmetric_matrix_index(indx1,indx1,nvar);
+ var_pos_2=symmetric_matrix_index(indx2,indx2,nvar);
+end
for file = 1:length(ListOfFiles)
load([ PATH ListOfFiles(file).name ]);
i2 = i1 + rows(Covariance_matrix) - 1;
tmp(i1:i2) = Covariance_matrix(:,symmetric_matrix_index(indx1,indx2,nvar));
+ if options_.contemporaneous_correlation
+ temp=Covariance_matrix(:,cov_pos)./(sqrt(Covariance_matrix(:,var_pos_1)).*sqrt(Covariance_matrix(:,var_pos_2)));
+ temp(Covariance_matrix(:,cov_pos)==0)=0; %filter out 0 correlations that would result in 0/0
+ tmp_corr_mat(i1:i2)=temp;
+ end
i1 = i2+1;
end
name = [var1 '.' var2];
@@ -111,4 +123,16 @@ else
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.covariance.HPDsup.' name ' = NaN;']);
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.covariance.deciles.' name ' = NaN;']);
eval(['oo_.' TYPE 'TheoreticalMoments.dsge.covariance.density.' name ' = NaN;']);
-end
\ No newline at end of file
+end
+
+if options_.contemporaneous_correlation
+ [p_mean, p_median, p_var, hpd_interval, p_deciles, density] = ...
+ posterior_moments(tmp_corr_mat,1,mh_conf_sig);
+ eval(['oo_.' TYPE 'TheoreticalMoments.dsge.contemporeaneous_correlation.Mean.' name ' = p_mean;']);
+ eval(['oo_.' TYPE 'TheoreticalMoments.dsge.contemporeaneous_correlation.Median.' name ' = p_median;']);
+ eval(['oo_.' TYPE 'TheoreticalMoments.dsge.contemporeaneous_correlation.Variance.' name ' = p_var;']);
+ eval(['oo_.' TYPE 'TheoreticalMoments.dsge.contemporeaneous_correlation.HPDinf.' name ' = hpd_interval(1);']);
+ eval(['oo_.' TYPE 'TheoreticalMoments.dsge.contemporeaneous_correlation.HPDsup.' name ' = hpd_interval(2);']);
+ eval(['oo_.' TYPE 'TheoreticalMoments.dsge.contemporeaneous_correlation.deciles.' name ' = p_deciles;']);
+ eval(['oo_.' TYPE 'TheoreticalMoments.dsge.contemporeaneous_correlation.density.' name ' = density;']);
+end
diff --git a/matlab/disp_moments.m b/matlab/disp_moments.m
index 478936e9659af94efb2337e66e94b8ec3859e1d0..cd7ebf2b9caebd444baf2e5a062b3871e8d3aef3 100644
--- a/matlab/disp_moments.m
+++ b/matlab/disp_moments.m
@@ -69,6 +69,9 @@ end
if options_.nocorr == 0
corr = (y'*y/size(y,1))./(s'*s);
+ if options_.contemporaneous_correlation
+ oo_.contemporaneous_correlation = corr;
+ end
if options_.noprint == 0
title = 'CORRELATION OF SIMULATED VARIABLES';
if options_.hp_filter
diff --git a/matlab/disp_th_moments.m b/matlab/disp_th_moments.m
index 68a24d03e36091bd48f74a1ea8377f38f8d97d71..b6edede955427dfb4918a5e7b31c51dce1956e52 100644
--- a/matlab/disp_th_moments.m
+++ b/matlab/disp_th_moments.m
@@ -117,6 +117,9 @@ end
if options_.nocorr == 0 && size(stationary_vars, 1) > 0
corr = oo_.gamma_y{1}(i1,i1)./(sd(i1)*sd(i1)');
+ if options_.contemporaneous_correlation
+ oo_.contemporaneous_correlation = corr;
+ end
if ~options_.noprint,
skipline()
if options_.order == 2
diff --git a/matlab/global_initialization.m b/matlab/global_initialization.m
index c42edf4167d8506a312ba43d54b36c1655279fa3..44694a40ab10801fda858cf37db84e57f4cb49d7 100644
--- a/matlab/global_initialization.m
+++ b/matlab/global_initialization.m
@@ -480,6 +480,7 @@ end
options_.filter_covariance = 0;
options_.filter_decomposition = 0;
options_.selected_variables_only = 0;
+options_.contemporaneous_correlation = 0;
options_.initialize_estimated_parameters_with_the_prior_mode = 0;
options_.estimation_dll = 0;
% Misc
diff --git a/matlab/metropolis_hastings_initialization.m b/matlab/metropolis_hastings_initialization.m
index c8e04f3bdb135a7603abc7ad30143e9d8448bdf5..27e8f4c7454504dfe708f944fd88995e352aa9fe 100644
--- a/matlab/metropolis_hastings_initialization.m
+++ b/matlab/metropolis_hastings_initialization.m
@@ -87,7 +87,7 @@ if ~options_.load_mh_file && ~options_.mh_recover
if nblck > 1
disp('Estimation::mcmc: Multiple chains mode.')
else
- disp('Etimation::mcmc: One Chain mode.')
+ disp('Estimation::mcmc: One Chain mode.')
end
% Delete old mh files if any...
files = dir([BaseName '_mh*_blck*.mat']);
diff --git a/matlab/posterior_analysis.m b/matlab/posterior_analysis.m
index 0382f923fa93a7cc84696ad42be0f771fff99c39..5209cb3f8c69c63d3e1f504a4ad1b467e1c753d9 100644
--- a/matlab/posterior_analysis.m
+++ b/matlab/posterior_analysis.m
@@ -55,7 +55,7 @@ switch type
dsge_simulated_theoretical_covariance(SampleSize,M_,options_,oo_,'posterior');
end
oo_ = covariance_mc_analysis(SampleSize,'posterior',M_.dname,M_.fname,...
- vartan,nvar,arg1,arg2,options_.mh_conf_sig,oo_);
+ vartan,nvar,arg1,arg2,options_.mh_conf_sig,oo_,options_);
case 'decomposition'
if nargin==narg1
[nvar,vartan,NumberOfFiles] = ...
diff --git a/matlab/prior_analysis.m b/matlab/prior_analysis.m
index 69c1ca7d985f3a4c53d3f2dd340cefd1c6c0aad1..b942a509fa98065d42924f7067576dabc92760db 100644
--- a/matlab/prior_analysis.m
+++ b/matlab/prior_analysis.m
@@ -56,7 +56,7 @@ switch type
dsge_simulated_theoretical_covariance(SampleSize,M_,options_,oo_,'prior');
end
oo_ = covariance_mc_analysis(SampleSize,'prior',M_.dname,M_.fname,...
- vartan,nvar,arg1,arg2,options_.mh_conf_sig,oo_);
+ vartan,nvar,arg1,arg2,options_.mh_conf_sig,oo_,options_);
case 'decomposition'
if nargin==narg1
[nvar,vartan,NumberOfFiles] = ...
diff --git a/matlab/symmetric_matrix_index.m b/matlab/symmetric_matrix_index.m
index 0c926d6b172f7e8255bb0831b31752d90884a1c6..73cf4a50b0a6025e0e3b3ccd58ab09d003d7ce7c 100644
--- a/matlab/symmetric_matrix_index.m
+++ b/matlab/symmetric_matrix_index.m
@@ -1,6 +1,17 @@
function k = symmetric_matrix_index(i,j,n)
+% function k = symmetric_matrix_index(i,j,n)
+% Returns index number of variable combination (i,j) in vech(A) where A is
+% an symmetric n by n matrix and vech creates row vector by stacking rows
+% of A on and above the diagonal
+%
+% Inputs:
+% i [scalar] index of first variable
+% j [scalar] index of second variable
+% n [scalar] number of variables
+% Outputs:
+% k [scalar] index of variable combination in vech(A)
-% Copyright (C) 2007-2009 Dynare Team
+% Copyright (C) 2007-2015 Dynare Team
%
% This file is part of Dynare.
%
diff --git a/preprocessor/DynareBison.yy b/preprocessor/DynareBison.yy
index 789286082badd65e88233908af70c4f61dd4895c..392d3d3efe60a54ccc2b9b23a191f6a3df0c1343 100644
--- a/preprocessor/DynareBison.yy
+++ b/preprocessor/DynareBison.yy
@@ -106,7 +106,7 @@ class ParsingDriver;
%token LYAPUNOV_FIXED_POINT_TOL LYAPUNOV_DOUBLING_TOL LYAPUNOV_SQUARE_ROOT_SOLVER_TOL LOG_DEFLATOR LOG_TREND_VAR LOG_GROWTH_FACTOR MARKOWITZ MARGINAL_DENSITY MAX MAXIT
%token MFS MH_CONF_SIG MH_DROP MH_INIT_SCALE MH_JSCALE MH_MODE MH_NBLOCKS MH_REPLIC MH_RECOVER POSTERIOR_MAX_SUBSAMPLE_DRAWS MIN MINIMAL_SOLVING_PERIODS
%token MODE_CHECK MODE_CHECK_NEIGHBOURHOOD_SIZE MODE_CHECK_SYMMETRIC_PLOTS MODE_CHECK_NUMBER_OF_POINTS MODE_COMPUTE MODE_FILE MODEL MODEL_COMPARISON MODEL_INFO MSHOCKS ABS SIGN
-%token MODEL_DIAGNOSTICS MODIFIEDHARMONICMEAN MOMENTS_VARENDO DIFFUSE_FILTER SUB_DRAWS TAPER_STEPS GEWEKE_INTERVAL MCMC_JUMPING_COVARIANCE MOMENT_CALIBRATION
+%token MODEL_DIAGNOSTICS MODIFIEDHARMONICMEAN MOMENTS_VARENDO CONTEMPORANEOUS_CORRELATION DIFFUSE_FILTER SUB_DRAWS TAPER_STEPS GEWEKE_INTERVAL MCMC_JUMPING_COVARIANCE MOMENT_CALIBRATION
%token NUMBER_OF_PARTICLES RESAMPLING SYSTEMATIC GENERIC RESAMPLING_THRESHOLD RESAMPLING_METHOD KITAGAWA STRATIFIED SMOOTH
%token FILTER_ALGORITHM PROPOSAL_APPROXIMATION CUBATURE UNSCENTED MONTECARLO DISTRIBUTION_APPROXIMATION
%token <string_val> NAME
@@ -1068,6 +1068,7 @@ stoch_simul_primary_options : o_dr_algo
| o_drop
| o_ar
| o_nocorr
+ | o_contemporaneous_correlation
| o_nofunctions
| o_nomoments
| o_nograph
@@ -1672,6 +1673,7 @@ estimation_options : o_datafile
| o_forecast
| o_smoother
| o_moments_varendo
+ | o_contemporaneous_correlation
| o_filtered_vars
| o_kalman_algo
| o_kalman_tol
@@ -2706,6 +2708,7 @@ o_tex : TEX { driver.option_num("TeX", "1"); };
o_forecast : FORECAST EQUAL INT_NUMBER { driver.option_num("forecast", $3); };
o_smoother : SMOOTHER { driver.option_num("smoother", "1"); };
o_moments_varendo : MOMENTS_VARENDO { driver.option_num("moments_varendo", "1"); };
+o_contemporaneous_correlation : CONTEMPORANEOUS_CORRELATION { driver.option_num("contemporaneous_correlation", "1"); };
o_filtered_vars : FILTERED_VARS { driver.option_num("filtered_vars", "1"); };
o_relative_irf : RELATIVE_IRF { driver.option_num("relative_irf", "1"); };
o_kalman_algo : KALMAN_ALGO EQUAL INT_NUMBER { driver.option_num("kalman_algo", $3); };
diff --git a/preprocessor/DynareFlex.ll b/preprocessor/DynareFlex.ll
index 81bcf01225c60fc67e0585efccbc03f7ea51dadf..ce022df74363857411b69d4ed0d970c9ff54df84 100644
--- a/preprocessor/DynareFlex.ll
+++ b/preprocessor/DynareFlex.ll
@@ -298,6 +298,7 @@ DATE -?[0-9]+([YyAa]|[Mm]([1-9]|1[0-2])|[Qq][1-4]|[Ww]([1-9]{1}|[1-4][0-9]|5[0-2
<DYNARE_STATEMENT>dsge_var {return token::DSGE_VAR;}
<DYNARE_STATEMENT>dsge_varlag {return token::DSGE_VARLAG;}
<DYNARE_STATEMENT>moments_varendo {return token::MOMENTS_VARENDO;}
+<DYNARE_STATEMENT>contemporaneous_correlation {return token::CONTEMPORANEOUS_CORRELATION;}
<DYNARE_STATEMENT>posterior_max_subsample_draws {return token::POSTERIOR_MAX_SUBSAMPLE_DRAWS;}
<DYNARE_STATEMENT>filtered_vars {return token::FILTERED_VARS;}
<DYNARE_STATEMENT>filter_step_ahead {return token::FILTER_STEP_AHEAD;}
diff --git a/tests/TeX/fs2000_corr_ME.mod b/tests/TeX/fs2000_corr_ME.mod
index f8bbe3c4ad422d4fe37edef74a2de4d361adec43..bb19e5fef4175c9217ee0e3e215c3606a115a6d9 100644
--- a/tests/TeX/fs2000_corr_ME.mod
+++ b/tests/TeX/fs2000_corr_ME.mod
@@ -123,7 +123,7 @@ end;
steady;
-stoch_simul(order=1,irf=20,graph_format=eps);
+stoch_simul(order=1,irf=20,graph_format=eps,contemporaneous_correlation);
write_latex_original_model;
write_latex_static_model;
@@ -143,7 +143,7 @@ stderr gy_obs, 1;
corr gp_obs, gy_obs,0;
end;
-estimation(order=1,datafile='../fs2000/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;
+estimation(order=1,datafile='../fs2000/fsdat_simul',mode_check,smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20,contemporaneous_correlation) m P c e W R k d y gy_obs;
@@ -159,7 +159,7 @@ stderr gp_obs, inv_gamma_pdf, 0.001, inf;
//corr gp_obs, gy_obs,normal_pdf, 0, 0.2;
end;
-estimation(mode_compute=9,order=1,datafile='../fs2000/fsdat_simul',mode_check,smoother,filter_decomposition,mh_replic=2002, mh_nblocks=2, mh_jscale=0.8,forecast = 8,bayesian_irf,filtered_vars,filter_step_ahead=[1,3],irf=20,moments_varendo) m P c e W R k d y;
+estimation(mode_compute=9,order=1,datafile='../fs2000/fsdat_simul',mode_check,smoother,filter_decomposition,mh_replic=2002, mh_nblocks=2, mh_jscale=0.8,forecast = 8,bayesian_irf,filtered_vars,filter_step_ahead=[1,3],irf=20,moments_varendo,contemporaneous_correlation) m P c e W R k d y;
shock_decomposition y W R;
collect_LaTeX_Files(M_);