diff --git a/matlab/PosteriorIRF_core1.m b/matlab/PosteriorIRF_core1.m
index bc2b3160afe3bb2d4fd14ad98e2cd532c0dfc74f..37f98209bad6c628fb3cb78d73a00fbb789639b9 100644
--- a/matlab/PosteriorIRF_core1.m
+++ b/matlab/PosteriorIRF_core1.m
@@ -194,7 +194,7 @@ while fpar<B
end
if MAX_nirfs_dsgevar
IRUN = IRUN+1;
- [fval,junk1,junk2,cost_flag,info,PHI,SIGMAu,iXX] = dsge_var_likelihood(deep',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
+ [fval,info,junk1,junk2,junk3,junk3,junk4,PHI,SIGMAu,iXX] = dsge_var_likelihood(deep',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
dsge_prior_weight = M_.params(strmatch('dsge_prior_weight',M_.param_names));
DSGE_PRIOR_WEIGHT = floor(dataset_.nobs*(1+dsge_prior_weight));
SIGMA_inv_upper_chol = chol(inv(SIGMAu*dataset_.nobs*(dsge_prior_weight+1)));
diff --git a/matlab/TaRB_optimizer_wrapper.m b/matlab/TaRB_optimizer_wrapper.m
index 9c7ed4cccbdde7ea5079275d9d6d08737ebc7044..4d29add34a3c4777b98097124e03bfa62d2ea32a 100644
--- a/matlab/TaRB_optimizer_wrapper.m
+++ b/matlab/TaRB_optimizer_wrapper.m
@@ -1,5 +1,5 @@
-function [fval,DLIK,Hess,exit_flag] = TaRB_optimizer_wrapper(optpar,par_vector,parameterindices,TargetFun,varargin)
-% function [fval,DLIK,Hess,exit_flag] = TaRB_optimizer_wrapper(optpar,par_vector,parameterindices,TargetFun,varargin)
+function [fval,info,exit_flag,DLIK,Hess,SteadyState,trend_coeff] = TaRB_optimizer_wrapper(optpar,par_vector,parameterindices,TargetFun,varargin)
+% function [fval,info,exit_flag,DLIK,Hess,SteadyState,trend_coeff] = TaRB_optimizer_wrapper(optpar,par_vector,parameterindices,TargetFun,varargin)
% Wrapper function for target function used in TaRB algorithm; reassembles
% full parameter vector before calling target function
%
@@ -14,11 +14,14 @@ function [fval,DLIK,Hess,exit_flag] = TaRB_optimizer_wrapper(optpar,par_vector,p
%
% OUTPUTS
% o fval [scalar] value of (minus) the likelihood.
+% o info [double] (p*2) error code vector
+% o exit_flag [scalar] equal to zero if the routine return with a penalty (one otherwise).
% o DLIK [double] (p*1) score vector of the likelihood.
% o Hess [double] (p*p) asymptotic Hessian matrix.
-% o exit_flag [scalar] equal to zero if the routine return with a penalty (one otherwise).
+% o SteadyState [double] Vector of doubles, steady state level for the endogenous variables.
+% o trend_coeff [double] Matrix of doubles, coefficients of the deterministic trend in the measurement equation
%
-% Copyright (C) 2015 Dynare Team
+% Copyright (C) 2015-16 Dynare Team
%
% This file is part of Dynare.
%
@@ -36,5 +39,5 @@ function [fval,DLIK,Hess,exit_flag] = TaRB_optimizer_wrapper(optpar,par_vector,p
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
par_vector(parameterindices,:)=optpar; %reassemble parameter
-[fval,DLIK,Hess,exit_flag] = feval(TargetFun,par_vector,varargin{:}); %call target function
+[fval,info,exit_flag,DLIK,Hess,SteadyState,trend_coeff] = feval(TargetFun,par_vector,varargin{:}); %call target function
diff --git a/matlab/dsge_likelihood.m b/matlab/dsge_likelihood.m
index 5b8c6f2828c8a2ecfba403a336d2a627a1ce201c..d5dfaefdf37284f892dc18a1047b1215c9fad72e 100644
--- a/matlab/dsge_likelihood.m
+++ b/matlab/dsge_likelihood.m
@@ -1,4 +1,4 @@
-function [fval,DLIK,Hess,exit_flag,SteadyState,trend_coeff,info,Model,DynareOptions,BayesInfo,DynareResults] = dsge_likelihood(xparam1,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults,derivatives_info)
+function [fval,info,exit_flag,DLIK,Hess,SteadyState,trend_coeff,Model,DynareOptions,BayesInfo,DynareResults] = dsge_likelihood(xparam1,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults,derivatives_info)
% Evaluates the posterior kernel of a dsge model using the specified
% kalman_algo; the resulting posterior includes the 2*pi constant of the
% likelihood function
@@ -35,14 +35,8 @@ function [fval,DLIK,Hess,exit_flag,SteadyState,trend_coeff,info,Model,DynareOpti
%! @table @ @var
%! @item fval
%! Double scalar, value of (minus) the likelihood.
-%! @item exit_flag
-%! Integer scalar, equal to zero if the routine return with a penalty (one otherwise).
-%! @item ys
-%! Vector of doubles, steady state level for the endogenous variables.
-%! @item trend_coeff
-%! Matrix of doubles, coefficients of the deterministic trend in the measurement equation.
%! @item info
-%! Integer scalar, error code.
+%! Double vector, second entry stores penalty, first entry the error code.
%! @table @ @code
%! @item info==0
%! No error.
@@ -91,6 +85,16 @@ function [fval,DLIK,Hess,exit_flag,SteadyState,trend_coeff,info,Model,DynareOpti
%! @item info==48
%! Posterior kernel is a complex valued number (logged prior density is complex).
%! @end table
+%! @item exit_flag
+%! Integer scalar, equal to zero if the routine return with a penalty (one otherwise).
+%! @item DLIK
+%! Vector of doubles, score of the likelihood.
+%! @item AHess
+%! Matrix of doubles, asymptotic hessian matrix.
+%! @item SteadyState
+%! Vector of doubles, steady state level for the endogenous variables.
+%! @item trend_coeff
+%! Matrix of doubles, coefficients of the deterministic trend in the measurement equation.
%! @item Model
%! Matlab's structure describing the model (initialized by dynare, see @ref{M_}).
%! @item DynareOptions
@@ -99,10 +103,6 @@ function [fval,DLIK,Hess,exit_flag,SteadyState,trend_coeff,info,Model,DynareOpti
%! Matlab's structure describing the priors (initialized by dynare, see @ref{bayesopt_}).
%! @item DynareResults
%! Matlab's structure gathering the results (initialized by dynare, see @ref{oo_}).
-%! @item DLIK
-%! Vector of doubles, score of the likelihood.
-%! @item AHess
-%! Matrix of doubles, asymptotic hessian matrix.
%! @end table
%! @sp 2
%! @strong{This function is called by:}
diff --git a/matlab/dsge_var_likelihood.m b/matlab/dsge_var_likelihood.m
index 87d09edb215adbe306a1f39953a4c03e5f2ecb6d..1317f7e6c8b20924aff6824b51915f7617908578 100644
--- a/matlab/dsge_var_likelihood.m
+++ b/matlab/dsge_var_likelihood.m
@@ -1,4 +1,4 @@
-function [fval,grad,hess,exit_flag,info,PHI,SIGMAu,iXX,prior] = dsge_var_likelihood(xparam1,DynareDataset,DynareInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults)
+function [fval,info,exit_flag,grad,hess,SteadyState,trend_coeff,PHI,SIGMAu,iXX,prior] = dsge_var_likelihood(xparam1,DynareDataset,DynareInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults)
% Evaluates the posterior kernel of the bvar-dsge model.
%
% INPUTS
@@ -7,8 +7,16 @@ function [fval,grad,hess,exit_flag,info,PHI,SIGMAu,iXX,prior] = dsge_var_likelih
%
% OUTPUTS
% o fval [double] Value of the posterior kernel at xparam1.
-% o cost_flag [integer] Zero if the function returns a penalty, one otherwise.
% o info [integer] Vector of informations about the penalty.
+% o exit_flag [integer] Zero if the function returns a penalty, one otherwise.
+% o grad [double] place holder for gradient of the likelihood
+% currently not supported by dsge_var
+% o hess [double] place holder for hessian matrix of the likelihood
+% currently not supported by dsge_var
+% o SteadyState [double] Steady state vector possibly recomputed
+% by call to dynare_resolve()
+% o trend_coeff [double] place holder for trend coefficients,
+% currently not supported by dsge_var
% o PHI [double] Stacked BVAR-DSGE autoregressive matrices (at the mode associated to xparam1).
% o SIGMAu [double] Covariance matrix of the BVAR-DSGE (at the mode associated to xparam1).
% o iXX [double] inv(X'X).
@@ -45,6 +53,7 @@ PHI = [];
SIGMAu = [];
iXX = [];
prior = [];
+trend_coeff=[];
% Initialization of of the index for parameter dsge_prior_weight in Model.params.
if isempty(dsge_prior_weight_idx)
@@ -271,7 +280,7 @@ if imag(fval)~=0
return
end
-if (nargout == 8)
+if (nargout == 10)
if isinf(dsge_prior_weight)
iXX = iGXX;
else
@@ -279,7 +288,7 @@ if (nargout == 8)
end
end
-if (nargout==9)
+if (nargout==11)
if isinf(dsge_prior_weight)
iXX = iGXX;
else
diff --git a/matlab/dynare_estimation_1.m b/matlab/dynare_estimation_1.m
index a2be89596bae10e3bfc7700f47b5aa47f756fd94..6e71390b9dd78a0eb30ae616bd61ecfc05fae832 100644
--- a/matlab/dynare_estimation_1.m
+++ b/matlab/dynare_estimation_1.m
@@ -236,7 +236,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
if options_.analytic_derivation && strcmp(func2str(objective_function),'dsge_likelihood'),
ana_deriv_old = options_.analytic_derivation;
options_.analytic_derivation = 2;
- [junk1, junk2, hh] = feval(objective_function,xparam1, ...
+ [junk1, junk2,junk3, junk4, hh] = feval(objective_function,xparam1, ...
dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
options_.analytic_derivation = ana_deriv_old;
elseif ~isnumeric(options_.mode_compute) || ~(isequal(options_.mode_compute,5) && newratflag~=1),
diff --git a/matlab/identification_analysis.m b/matlab/identification_analysis.m
index 53fc6ad33257a1f47c0ed264e5ebe85d96dcc7ec..3e05e16391db63fc0287153c4ff47ee889c6a1ae 100644
--- a/matlab/identification_analysis.m
+++ b/matlab/identification_analysis.m
@@ -156,7 +156,7 @@ if info(1)==0,
dataset_ = dseries(oo_.endo_simul(options_.varobs_id,100+1:end)',dates('1Q1'), options_.varobs);
derivatives_info.no_DLIK=1;
bounds = prior_bounds(bayestopt_, options_.prior_trunc);
- [fval,DLIK,AHess,cost_flag,ys,trend_coeff,info,M_,options_,bayestopt_,oo_] = dsge_likelihood(params',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_,derivatives_info);
+ [fval,info,cost_flag,DLIK,AHess,ys,trend_coeff,M_,options_,bayestopt_,oo_] = dsge_likelihood(params',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_,derivatives_info);
% fval = DsgeLikelihood(xparam1,data_info,options_,M_,estim_params_,bayestopt_,oo_);
options_.analytic_derivation = analytic_derivation;
AHess=-AHess;
diff --git a/matlab/initial_estimation_checks.m b/matlab/initial_estimation_checks.m
index 84adbde0cd02bddd45c403f81620c72d861c8a53..3e6430a8206650b73f8646198dd9085cf3f2dd05 100644
--- a/matlab/initial_estimation_checks.m
+++ b/matlab/initial_estimation_checks.m
@@ -116,9 +116,9 @@ ana_deriv = DynareOptions.analytic_derivation;
DynareOptions.analytic_derivation=0;
if ~isequal(DynareOptions.mode_compute,11) || ...
(isequal(DynareOptions.mode_compute,11) && isequal(DynareOptions.order,1))
- [fval,junk1,junk2,a,b,c,d] = feval(objective_function,xparam1,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults);
+ [fval,info] = feval(objective_function,xparam1,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults);
else
- b=0;
+ info=0;
fval = 0;
end
if DynareOptions.debug
@@ -126,12 +126,6 @@ if DynareOptions.debug
end
DynareOptions.analytic_derivation=ana_deriv;
-if DynareOptions.dsge_var || strcmp(func2str(objective_function),'non_linear_dsge_likelihood')
- info = b;
-else
- info = d;
-end
-
% if DynareOptions.mode_compute==5
% if ~strcmp(func2str(objective_function),'dsge_likelihood')
% error('Options mode_compute=5 is not compatible with non linear filters or Dsge-VAR models!')
diff --git a/matlab/mode_check.m b/matlab/mode_check.m
index 40b675739bab202dc8b09656b75c6acf4235b0dc..7a282af2e0e4d562aacc62692eef0784aa342c7a 100644
--- a/matlab/mode_check.m
+++ b/matlab/mode_check.m
@@ -140,7 +140,7 @@ for plt = 1:nbplt,
end
for i=1:length(z)
xx(kk) = z(i);
- [fval, junk1, junk2, exit_flag] = feval(fun,xx,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults);
+ [fval, info, exit_flag] = feval(fun,xx,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults);
if exit_flag
y(i,1) = fval;
else
diff --git a/matlab/non_linear_dsge_likelihood.m b/matlab/non_linear_dsge_likelihood.m
index 94c46eee170bbaec5b81474e53c98d667ea2a2d7..35685ed2a47540f703303244573f60543af7f4c6 100644
--- a/matlab/non_linear_dsge_likelihood.m
+++ b/matlab/non_linear_dsge_likelihood.m
@@ -1,4 +1,4 @@
-function [fval,ys,trend_coeff,exit_flag,info,Model,DynareOptions,BayesInfo,DynareResults] = non_linear_dsge_likelihood(xparam1,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults)
+function [fval,info,exit_flag,DLIK,Hess,ys,trend_coeff,Model,DynareOptions,BayesInfo,DynareResults] = non_linear_dsge_likelihood(xparam1,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults)
% Evaluates the posterior kernel of a dsge model using a non linear filter.
%@info:
@@ -31,14 +31,8 @@ function [fval,ys,trend_coeff,exit_flag,info,Model,DynareOptions,BayesInfo,Dynar
%! @table @ @var
%! @item fval
%! Double scalar, value of (minus) the likelihood.
-%! @item exit_flag
-%! Integer scalar, equal to zero if the routine return with a penalty (one otherwise).
-%! @item ys
-%! Vector of doubles, steady state level for the endogenous variables.
-%! @item trend_coeffs
-%! Matrix of doubles, coefficients of the deterministic trend in the measurement equation.
%! @item info
-%! Integer scalar, error code.
+%! Double vector, second entry stores penalty, first entry the error code.
%! @table @ @code
%! @item info==0
%! No error.
@@ -81,6 +75,18 @@ function [fval,ys,trend_coeff,exit_flag,info,Model,DynareOptions,BayesInfo,Dynar
%! @item info==45
%! Likelihood is a complex valued number.
%! @end table
+%! @item exit_flag
+%! Integer scalar, equal to zero if the routine return with a penalty (one otherwise).
+%! @item DLIK
+%! Vector of doubles, placeholder for score of the likelihood, currently
+%! not supported by non_linear_dsge_likelihood
+%! @item AHess
+%! Matrix of doubles, placeholder for asymptotic hessian matrix, currently
+%! not supported by non_linear_dsge_likelihood
+%! @item ys
+%! Vector of doubles, steady state level for the endogenous variables.
+%! @item trend_coeffs
+%! Matrix of doubles, coefficients of the deterministic trend in the measurement equation.
%! @item Model
%! Matlab's structure describing the model (initialized by dynare, see @ref{M_}).
%! @item DynareOptions
@@ -132,6 +138,8 @@ fval = [];
ys = [];
trend_coeff = [];
exit_flag = 1;
+DLIK = [];
+Hess = [];
% Issue an error if loglinear option is used.
if DynareOptions.loglinear
diff --git a/matlab/osr1.m b/matlab/osr1.m
index d5070cdbb787f2a15be8ffda557e40628669abf9..51fd5475fa1737132febdb286db6728c4b5891a0 100644
--- a/matlab/osr1.m
+++ b/matlab/osr1.m
@@ -77,7 +77,7 @@ inv_order_var = oo_.dr.inv_order_var;
%extract unique entries of covariance
i_var=unique(i_var);
%% do initial checks
-[loss,vx,info,exit_flag]=osr_obj(t0,i_params,inv_order_var(i_var),weights(i_var,i_var));
+[loss,info,exit_flag,vx]=osr_obj(t0,i_params,inv_order_var(i_var),weights(i_var,i_var));
if info~=0
print_info(info, options_.noprint, options_);
else
diff --git a/matlab/osr_obj.m b/matlab/osr_obj.m
index 378d2b06ed0b50362aa14018eb3d07c3c86f741e..7c3e7035312a51cb724a863390de6dc7ff50f672 100644
--- a/matlab/osr_obj.m
+++ b/matlab/osr_obj.m
@@ -1,4 +1,4 @@
-function [loss,vx,info,exit_flag]=osr_obj(x,i_params,i_var,weights)
+function [loss,info,exit_flag,vx,junk]=osr_obj(x,i_params,i_var,weights)
% objective function for optimal simple rules (OSR)
% INPUTS
% x vector values of the parameters
@@ -9,13 +9,15 @@ function [loss,vx,info,exit_flag]=osr_obj(x,i_params,i_var,weights)
%
% OUTPUTS
% loss scalar loss function returned to solver
-% vx vector variances of the endogenous variables
% info vector info vector returned by resol
% exit_flag scalar exit flag returned to solver
+% vx vector variances of the endogenous variables
+% junk empty dummy output for conformable
+% header
%
% SPECIAL REQUIREMENTS
% none
-% Copyright (C) 2005-2013 Dynare Team
+% Copyright (C) 2005-2016 Dynare Team
%
% This file is part of Dynare.
%