Changed the name of DsgeLikelihood (-> dsge_likelihood).

matlab/DsgeLikelihood.m → matlab/dsge_likelihood.m

-function [fval,exit_flag,ys,trend_coeff,info,Model,DynareOptions,BayesInfo,DynareResults,DLIK,AHess] = DsgeLikelihood(xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults,derivatives_info)
+function [fval,exit_flag,ys,trend_coeff,info,Model,DynareOptions,BayesInfo,DynareResults,DLIK,AHess] = dsge_likelihood(xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults,derivatives_info)
 % Evaluates the posterior kernel of a dsge model.
 
 %@info:
 %! @deftypefn {Function File} {[@var{fval},@var{exit_flag},@var{ys},@var{trend_coeff},@var{info},@var{Model},@var{DynareOptions},@var{BayesInfo},@var{DynareResults},@var{DLIK},@var{AHess}] =} DsgeLikelihood (@var{xparam1},@var{DynareDataset},@var{DynareOptions},@var{Model},@var{EstimatedParameters},@var{BayesInfo},@var{DynareResults},@var{derivatives_flag})
-%! @anchor{DsgeLikelihood}
+%! @anchor{dsge_likelihood}
 %! @sp 1
 %! Evaluates the posterior kernel of a dsge model.
 %! @sp 2
@@ -428,13 +428,13 @@ switch DynareOptions.lik_init
         [err,Pstar] = kalman_steady_state(transpose(T),R*Q*transpose(R),transpose(build_selection_matrix(Z,np,length(Z))),H);
     end
     if err
-        disp(['DsgeLikelihood:: I am not able to solve the Riccati equation, so I switch to lik_init=1!']);
+        disp(['dsge_likelihood:: I am not able to solve the Riccati equation, so I switch to lik_init=1!']);
         DynareOptions.lik_init = 1;
         Pstar = lyapunov_symm(T,R*Q*R',DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold);
     end
     Pinf  = [];
   otherwise
-    error('DsgeLikelihood:: Unknown initialization approach for the Kalman filter!')
+    error('dsge_likelihood:: Unknown initialization approach for the Kalman filter!')
 end
 
 if analytic_derivation

matlab/dynare_estimation_1.m

@@ -32,7 +32,7 @@ function dynare_estimation_1(var_list_,dname)
 global M_ options_ oo_ estim_params_ bayestopt_ dataset_
 
 if ~options_.dsge_var
-    objective_function = str2func('DsgeLikelihood');
+    objective_function = str2func('dsge_likelihood');
 else
     objective_function = str2func('DsgeVarLikelihood');
 end
@@ -248,7 +248,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
                 fval = feval(objective_function,xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
                 options_.mh_jscale = Scale;
                 mouvement = max(max(abs(PostVar-OldPostVar)));
-                fval = DsgeLikelihood(xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+                fval = dsge_likelihood(xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
                 disp(['Change in the covariance matrix = ' num2str(mouvement) '.'])
                 disp(['Mode improvement = ' num2str(abs(OldMode-fval))])
                 OldMode = fval;

matlab/dynare_resolve.m

@@ -41,7 +41,7 @@ function [A,B,ys,info,Model,DynareOptions,DynareResults] = dynare_resolve(Model,
 %! @sp 2
 %! @strong{This function is called by:}
 %! @sp 1
-%! @ref{DsgeLikelihood}, @ref{DsgeLikelihood_hh}, @ref{DsgeVarLikelihood}, @ref{dsge_posterior_kernel}, @ref{DsgeSmoother}, @ref{dynare_sensitivity}, @ref{gsa/thet2tau}, @ref{gsa/stab_map}, @ref{identification_analysis}, @ref{imcforecast}, @ref{thet2tau}
+%! @ref{dsge_likelihood}, @ref{DsgeLikelihood_hh}, @ref{DsgeVarLikelihood}, @ref{dsge_posterior_kernel}, @ref{DsgeSmoother}, @ref{dynare_sensitivity}, @ref{gsa/thet2tau}, @ref{gsa/stab_map}, @ref{identification_analysis}, @ref{imcforecast}, @ref{thet2tau}
 %! @sp 2
 %! @strong{This function calls:}
 %! @sp 1

matlab/evaluate_likelihood.m

@@ -85,6 +85,6 @@ end
 pshape_original   = bayestopt_.pshape;
 bayestopt_.pshape = Inf(size(bayestopt_.pshape));
 
-llik = -DsgeLikelihood(parameters,dataset,options_,M_,estim_params_,bayestopt_,oo_);
+llik = -dsge_likelihood(parameters,dataset,options_,M_,estim_params_,bayestopt_,oo_);
 
 bayestopt_.pshape = pshape_original;
\ No newline at end of file

matlab/identification_analysis.m

@@ -135,7 +135,7 @@ if info(1)==0,
             data_info.data=oo_.endo_simul(options_.varobs_id,100+1:end);
             %                         datax=data;
             derivatives_info.no_DLIK=1;
-            [fval,cost_flag,ys,trend_coeff,info,M_,options_,bayestopt_,oo_,DLIK,AHess] = DsgeLikelihood(params',data_info,options_,M_,estim_params_,bayestopt_,oo_,derivatives_info);
+            [fval,cost_flag,ys,trend_coeff,info,M_,options_,bayestopt_,oo_,DLIK,AHess] = dsge_likelihood(params',data_info,options_,M_,estim_params_,bayestopt_,oo_,derivatives_info);
 %                 fval = DsgeLikelihood(xparam1,data_info,options_,M_,estim_params_,bayestopt_,oo_);
             AHess=-AHess;
             ide_hess.AHess= AHess;

matlab/initial_estimation_checks.m

@@ -41,7 +41,7 @@ end
 if DynareOptions.dsge_var
     [fval,cost_flag,info] = DsgeVarLikelihood(xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
 else
-    [fval,cost_flag,ys,trend_coeff,info] = DsgeLikelihood(xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+    [fval,cost_flag,ys,trend_coeff,info] = dsge_likelihood(xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
 end
 
 if info(1) > 0

matlab/kalman/likelihood/kalman_filter.m

@@ -2,7 +2,7 @@ function [LIK, likk, a, P] = kalman_filter(Y,start,last,a,P,kalman_tol,riccati_t
 % Computes the likelihood of a stationnary state space model.
 
 %@info:
-%! @deftypefn {Function File} {[@var{LIK},@var{likk},@var{a},@var{P} ] =} DsgeLikelihood (@var{Y}, @var{start}, @var{last}, @var{a}, @var{P}, @var{kalman_tol}, @var{riccati_tol},@var{presample},@var{T},@var{Q},@var{R},@var{H},@var{Z},@var{mm},@var{pp},@var{rr},@var{Zflag},@var{diffuse_periods})
+%! @deftypefn {Function File} {[@var{LIK},@var{likk},@var{a},@var{P} ] =} kalman_filter (@var{Y}, @var{start}, @var{last}, @var{a}, @var{P}, @var{kalman_tol}, @var{riccati_tol},@var{presample},@var{T},@var{Q},@var{R},@var{H},@var{Z},@var{mm},@var{pp},@var{rr},@var{Zflag},@var{diffuse_periods})
 %! @anchor{kalman_filter}
 %! @sp 1
 %! Computes the likelihood of a stationary state space model, given initial condition for the states (mean and variance).
@@ -63,7 +63,7 @@ function [LIK, likk, a, P] = kalman_filter(Y,start,last,a,P,kalman_tol,riccati_t
 %! @sp 2
 %! @strong{This function is called by:}
 %! @sp 1
-%! @ref{DsgeLikelihood}
+%! @ref{dsge_likelihood}
 %! @sp 2
 %! @strong{This function calls:}
 %! @sp 1

matlab/kalman/likelihood/kalman_filter_ss.m

@@ -2,7 +2,7 @@ function [LIK, likk, a] = kalman_filter_ss(Y,start,last,a,T,K,iF,dF,Z,pp,Zflag)
 % Computes the likelihood of a stationnary state space model (steady state kalman filter).
 
 %@info:
-%! @deftypefn {Function File} {[@var{LIK},@var{likk},@var{a},@var{P} ] =} DsgeLikelihood (@var{Y}, @var{start}, @var{last}, @var{a}, @var{P}, @var{kalman_tol}, @var{riccati_tol},@var{presample},@var{T},@var{Q},@var{R},@var{H},@var{Z},@var{mm},@var{pp},@var{rr},@var{Zflag},@var{diffuse_periods})
+%! @deftypefn {Function File} {[@var{LIK},@var{likk},@var{a},@var{P} ] =} kalman_filter_ss (@var{Y}, @var{start}, @var{last}, @var{a}, @var{P}, @var{kalman_tol}, @var{riccati_tol},@var{presample},@var{T},@var{Q},@var{R},@var{H},@var{Z},@var{mm},@var{pp},@var{rr},@var{Zflag},@var{diffuse_periods})
 %! @anchor{kalman_filter}
 %! @sp 1
 %! Computes the likelihood of a stationary state space model, given initial condition for the states (mean), the steady state kalman gain and the steady state inveverted covariance matrix of the prediction errors.

matlab/kalman/likelihood/univariate_kalman_filter.m

@@ -69,7 +69,7 @@ function [LIK, likk,a,P] = univariate_kalman_filter(data_index,number_of_observa
 %! @sp 2
 %! @strong{This function is called by:}
 %! @sp 1
-%! @ref{DsgeLikelihood}
+%! @ref{dsge_likelihood}
 %! @sp 2
 %! @strong{This function calls:}
 %! @sp 1

matlab/kalman/likelihood/univariate_kalman_filter_d.m

@@ -73,7 +73,7 @@ function [dLIK, dlikk, a, Pstar, llik] = univariate_kalman_filter_d(data_index,
 %! @sp 2
 %! @strong{This function is called by:}
 %! @sp 1
-%! @ref{DsgeLikelihood}, @ref{DsgeLikelihood_hh}
+%! @ref{dsge_likelihood}, @ref{DsgeLikelihood_hh}
 %! @sp 2
 %! @strong{This function calls:}
 %! @sp 1

matlab/print_info.m

@@ -73,7 +73,7 @@ if ~noprint
         error('one (many) parameter(s) do(es) not satisfy the upper bound');
       case 43
         error('Covariance matrix of shocks is not positive definite')
-      case 44 %DsgeLikelihood_hh / DsgeLikelihood
+      case 44 %DsgeLikelihood_hh / dsge_likelihood
         error('');
       case 51
         error('You are estimating a DSGE-VAR model, but the value of the dsge prior weight is too low!')