diff --git a/doc/dynare.texi b/doc/dynare.texi index 6a553ff45..08cc205d5 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 +{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/compute_moments_varendo.m b/matlab/compute_moments_varendo.m index cf91019c9..75952c78a 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 . + +fprintf('Estimation::compute_moments_varendo: I''m computing endogenous moments (this may take a while)... '); + if strcmpi(type,'posterior') posterior = 1; if nargin==4 @@ -126,3 +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 d98992f0e..59cc635f2 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 478936e96..cd7ebf2b9 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 68a24d03e..b6edede95 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 c42edf416..44694a40a 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/posterior_analysis.m b/matlab/posterior_analysis.m index 0382f923f..5209cb3f8 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 69c1ca7d9..b942a509f 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/preprocessor/DynareBison.yy b/preprocessor/DynareBison.yy index 789286082..392d3d3ef 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 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 81bcf0122..ce022df74 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 dsge_var {return token::DSGE_VAR;} dsge_varlag {return token::DSGE_VARLAG;} moments_varendo {return token::MOMENTS_VARENDO;} +contemporaneous_correlation {return token::CONTEMPORANEOUS_CORRELATION;} posterior_max_subsample_draws {return token::POSTERIOR_MAX_SUBSAMPLE_DRAWS;} filtered_vars {return token::FILTERED_VARS;} 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 f8bbe3c4a..bb19e5fef 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_);