Finish implementation of mcmc_jumping_covariance option

Closes #112, #514

doc/dynare.texi

@@ -4486,8 +4486,8 @@ 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:
+@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 
@@ -4499,12 +4499,12 @@ 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.
+@item @var{FILENAME}
+Loads an arbitrary user-specified covariance matrix from @code{@var{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}.
+Note that the covariance matrices are still scaled with @ref{mh_jscale}. Default value is @code{hessian}.
 
 @item mode_check
 Tells Dynare to plot the posterior density for values around the

preprocessor/DynareBison.yy

@@ -113,7 +113,7 @@ 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_DROP MH_INIT_SCALE MH_JSCALE MH_MODE MH_NBLOCKS MH_REPLIC MH_RECOVER 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
+%token MODEL_DIAGNOSTICS MODIFIEDHARMONICMEAN MOMENTS_VARENDO DIFFUSE_FILTER SUB_DRAWS TAPER_STEPS GEWEKE_INTERVAL MCMC_JUMPING_COVARIANCE
 %token <string_val> NAME
 %token NAN_CONSTANT NO_STATIC NOBS NOCONSTANT NODISPLAY NOCORR NODIAGNOSTIC NOFUNCTIONS
 %token NOGRAPH NOMOMENTS NOPRINT NORMAL_PDF SAVE_DRAWS
@@ -151,7 +151,7 @@ class ParsingDriver;
 %token VLISTLOG VLISTPER
 %token RESTRICTION RESTRICTION_FNAME CROSS_RESTRICTIONS NLAGS CONTEMP_REDUCED_FORM REAL_PSEUDO_FORECAST
 %token DUMMY_OBS NSTATES INDXSCALESSTATES NO_BAYESIAN_PRIOR SPECIFICATION SIMS_ZHA
-%token <string_val> ALPHA BETA ABAND NINV CMS NCMS CNUM GAMMA INV_GAMMA INV_GAMMA1 INV_GAMMA2 NORMAL UNIFORM EPS PDF FIG DR NONE PRIOR
+%token <string_val> ALPHA BETA ABAND NINV CMS NCMS CNUM GAMMA INV_GAMMA INV_GAMMA1 INV_GAMMA2 NORMAL UNIFORM EPS PDF FIG DR NONE PRIOR PRIOR_VARIANCE HESSIAN IDENTITY_MATRIX
 %token GSIG2_LMDM Q_DIAG FLAT_PRIOR NCSK NSTD
 %token INDXPARR INDXOVR INDXAP APBAND INDXIMF IMFBAND INDXFORE FOREBAND INDXGFOREHAT INDXGIMFHAT
 %token INDXESTIMA INDXGDLS EQ_MS FILTER_COVARIANCE FILTER_DECOMPOSITION
@@ -1558,6 +1558,7 @@ estimation_options : o_datafile
                    | o_qz_zero_threshold
                    | o_taper_steps
                    | o_geweke_interval
+                   | o_mcmc_jumping_covariance
                    ;
 
 list_optim_option : QUOTED_STRING COMMA QUOTED_STRING
@@ -2677,6 +2678,14 @@ o_discretionary_tol: DISCRETIONARY_TOL EQUAL non_negative_number { driver.option
 o_analytic_derivation : ANALYTIC_DERIVATION { driver.option_num("analytic_derivation", "1"); }
 o_endogenous_prior : ENDOGENOUS_PRIOR { driver.option_num("endogenous_prior", "1"); }
 o_use_univariate_filters_if_singularity_is_detected : USE_UNIVARIATE_FILTERS_IF_SINGULARITY_IS_DETECTED EQUAL INT_NUMBER { driver.option_num("use_univariate_filters_if_singularity_is_detected", $3); }
+o_mcmc_jumping_covariance : MCMC_JUMPING_COVARIANCE EQUAL HESSIAN
+                            { driver.option_str("MCMC_jumping_covariance", $3); }                                  | MCMC_JUMPING_COVARIANCE EQUAL PRIOR_VARIANCE
+                            { driver.option_str("MCMC_jumping_covariance", $3); }
+                          | MCMC_JUMPING_COVARIANCE EQUAL IDENTITY_MATRIX
+                            { driver.option_str("MCMC_jumping_covariance", $3); }
+                          | MCMC_JUMPING_COVARIANCE EQUAL filename
+                            { driver.option_str("MCMC_jumping_covariance", $3); }
+                          ;
 
 range : symbol ':' symbol
         {

preprocessor/DynareFlex.ll

@@ -439,6 +439,19 @@ string eofbuff;
 <DYNARE_STATEMENT>random_parameter_convergence_criterion {return token::RANDOM_PARAMETER_CONVERGENCE_CRITERION;}
 <DYNARE_STATEMENT>tolf {return token::TOLF;}
 <DYNARE_STATEMENT>instruments {return token::INSTRUMENTS;}
+<DYNARE_STATEMENT>hessian  {
+  yylval->string_val = new string(yytext);
+  return token::HESSIAN;
+}
+<DYNARE_STATEMENT>prior_variance  {
+  yylval->string_val = new string(yytext);
+  return token::PRIOR_VARIANCE;
+}
+<DYNARE_STATEMENT>identity_matrix  {
+  yylval->string_val = new string(yytext);
+  return token::IDENTITY_MATRIX;
+}
+<DYNARE_STATEMENT>mcmc_jumping_covariance {return token::MCMC_JUMPING_COVARIANCE;}
 
  /* These four (var, varexo, varexo_det, parameters) are for change_type */
 <DYNARE_STATEMENT>var { return token::VAR; }

tests/Makefile.am

@@ -3,6 +3,7 @@ MODFILES = \
 	estimation/fs2000_mc4_mf.mod \
 	estimation/fs2000_mc6.mod \
 	estimation/fs2000_mc6_mf.mod \
+	estimation/fs2000_MCMC_jumping_covariance.mod \
 	gsa/ls2003.mod \
 	ramst.mod \
 	ramst_a.mod \
@@ -309,7 +310,8 @@ EXTRA_DIST = \
 	third_order/comparison_policy_functions_dynare_mathematica.m \
 	third_order/policyfunctions.mat \
 	shock_decomposition/example1_calib_shock_decomp_data.mat \
-	shock_decomposition/fsdat_simul.m
+	shock_decomposition/fsdat_simul.m \
+	estimation/fs2000_MCMC_jumping_covariance_steadystate.m
 
 TARGETS =
 
@@ -417,3 +419,5 @@ clean-local:
 		ms-sbvar/*.dat \
 		ms-sbvar/tmv_rr_sr \
 		ms-sbvar/tmv_rr_tr
+
+	rm -f estimation/test_matrix.mat

tests/estimation/fs2000_MCMC_jumping_covariance.mod

@@ -73,14 +73,13 @@ 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);
+estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=1000,mh_nblocks=1,mh_jscale=0.8,mcmc_jumping_covariance=hessian);
+
 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);
+save test_matrix.mat jumping_covariance;
+
+estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=1000,mh_nblocks=1,mh_jscale=0.01,mcmc_jumping_covariance=prior_variance);
+estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=1000,mh_nblocks=1,mh_jscale=0.0001,mcmc_jumping_covariance=identity_matrix);
+
+estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=1000,mh_nblocks=1,mh_jscale=0.8,mcmc_jumping_covariance='test_matrix');

tests/estimation/fs2000_MCMC_jumping_covariance_steadystate.m

@@ -18,7 +18,7 @@
 % 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)
+function [ys,check] = fs2000_MCMC_jumping_covariance_steadystate(ys,exe)
   global M_
   
   alp = M_.params(1);