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Revert "adapted dsge-var code for new handling of penalties" 

This reverts commit 3966296aca.
time-shift
Michel Juillard 2015-10-09 14:21:26 +02:00
parent f464760c45
commit 295eed6118
5 changed files with 34 additions and 55 deletions
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2
matlab/PosteriorIRF_core1.m
View File

@ -194,7 +194,7 @@ while fpar<B
end
if MAX_nirfs_dsgevar
IRUN = IRUN+1;
[fval,junk1,junk2,cost_flag,SteadyState,junk3,info,PHI,SIGMAu,iXX] = dsge_var_likelihood(deep',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
[fval,junk1,junk2,cost_flag,info,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)));

78
matlab/dsge_var_likelihood.m
View File

@ -1,4 +1,4 @@
function [fval,grad,hess,exit_flag,SteadyState,trend_coeff,info,PHI,SIGMAu,iXX,prior] = dsge_var_likelihood(xparam1,DynareDataset,DynareInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults)
function [fval,grad,hess,exit_flag,info,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
@ -8,10 +8,6 @@ function [fval,grad,hess,exit_flag,SteadyState,trend_coeff,info,PHI,SIGMAu,iXX,p
% 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 SteadyState [double] Steady state vector possibly recomputed
% by call to dynare_results()
% o trend_coeff [double] place holder for trend coefficients,
% currently not supported by dsge_var
% o info [integer] Vector of informations about the penalty.
% 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).
@ -38,18 +34,17 @@ function [fval,grad,hess,exit_flag,SteadyState,trend_coeff,info,PHI,SIGMAu,iXX,p
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
global objective_function_penalty_base
persistent dsge_prior_weight_idx
grad=[];
hess=[];
exit_flag = [];
info = 0;
info = [];
PHI = [];
SIGMAu = [];
iXX = [];
prior = [];
SteadyState = [];
trend_coeff = [];
% Initialization of of the index for parameter dsge_prior_weight in Model.params.
if isempty(dsge_prior_weight_idx)
@ -87,21 +82,19 @@ exit_flag = 1;
% Return, with endogenous penalty, if some dsge-parameters are smaller than the lower bound of the prior domain.
if DynareOptions.mode_compute ~= 1 && any(xparam1 < BoundsInfo.lb)
fval = Inf;
exit_flag = 0;
info(1) = 41;
k = find(xparam1 < BoundsInfo.lb);
info(2) = sum((BoundsInfo.lb(k)-xparam1(k)).^2);
fval = objective_function_penalty_base+sum((BoundsInfo.lb(k)-xparam1(k)).^2);
exit_flag = 0;
info = 41;
return;
end
% Return, with endogenous penalty, if some dsge-parameters are greater than the upper bound of the prior domain.
if DynareOptions.mode_compute ~= 1 && any(xparam1 > BoundsInfo.ub)
fval = Inf;
exit_flag = 0;
info(1) = 42;
k = find(xparam1 > BoundsInfo.ub);
info(2) = sum((xparam1(k)-BoundsInfo.ub(k)).^2);
fval = objective_function_penalty_base+sum((xparam1(k)-BoundsInfo.ub(k)).^2);
exit_flag = 0;
info = 42;
return;
end
@ -121,14 +114,12 @@ Model.Sigma_e = Q;
dsge_prior_weight = Model.params(dsge_prior_weight_idx);
% Is the dsge prior proper?
if dsge_prior_weight<(NumberOfParameters+NumberOfObservedVariables)/ ...
NumberOfObservations;
fval = Inf;
if dsge_prior_weight<(NumberOfParameters+NumberOfObservedVariables)/NumberOfObservations;
fval = objective_function_penalty_base+abs(NumberOfObservations*dsge_prior_weight-(NumberOfParameters+NumberOfObservedVariables));
exit_flag = 0;
info(1) = 51;
info(2) = abs(NumberOfObservations*dsge_prior_weight-(NumberOfParameters+NumberOfObservedVariables));
% info(2)=dsge_prior_weight;
% info(3)=(NumberOfParameters+NumberOfObservedVariables)/DynareDataset.nobs;
info = 51;
info(2)=dsge_prior_weight;
info(3)=(NumberOfParameters+NumberOfObservedVariables)/DynareDataset.nobs;
return
end
@ -143,13 +134,13 @@ end
% Return, with endogenous penalty when possible, if dynare_resolve issues an error code (defined in resol).
if info(1) == 1 || info(1) == 2 || info(1) == 5 || info(1) == 7 || info(1) == 8 || ...
info(1) == 22 || info(1) == 24 || info(1) == 25 || info(1) == 10
fval = Inf;
info(2) = 0.1;
fval = objective_function_penalty_base+1;
info = info(1);
exit_flag = 0;
return
elseif info(1) == 3 || info(1) == 4 || info(1) == 19 || info(1) == 20 || info(1) == 21
fval = Inf;
info(2) = 0.1;
fval = objective_function_penalty_base+info(2);
info = info(1);
exit_flag = 0;
return
end
@ -218,9 +209,8 @@ if ~isinf(dsge_prior_weight)% Evaluation of the likelihood of the dsge-var model
SIGMAu = tmp0 - tmp1*tmp2*tmp1'; clear('tmp0');
[SIGMAu_is_positive_definite, penalty] = ispd(SIGMAu);
if ~SIGMAu_is_positive_definite
fval = Inf;
info(1) = 52;
info(2) = penalty;
fval = objective_function_penalty_base + penalty;
info = 52;
exit_flag = 0;
return;
end
@ -250,17 +240,15 @@ else% Evaluation of the likelihood of the dsge-var model when the dsge prior wei
end
if isnan(lik)
info(1) = 45;
info(2) = 0.1;
fval = Inf;
info = 45;
fval = objective_function_penalty_base + 100;
exit_flag = 0;
return
end
if imag(lik)~=0
info(1) = 46;
info(2) = 0.1;
fval = Inf;
info = 46;
fval = objective_function_penalty_base + 100;
exit_flag = 0;
return
end
@ -270,22 +258,20 @@ lnprior = priordens(xparam1,BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo
fval = (lik-lnprior);
if isnan(fval)
info(1) = 47;
info(2) = 0.1;
fval = Inf;
info = 47;
fval = objective_function_penalty_base + 100;
exit_flag = 0;
return
end
if imag(fval)~=0
info(1) = 48;
info(2) = 0.1;
fval = Inf;
info = 48;
fval = objective_function_penalty_base + 100;
exit_flag = 0;
return
end
if (nargout == 10)
if (nargout == 8)
if isinf(dsge_prior_weight)
iXX = iGXX;
else
@ -293,7 +279,7 @@ if (nargout == 10)
end
end
if (nargout==11)
if (nargout==9)
if isinf(dsge_prior_weight)
iXX = iGXX;
else
@ -305,8 +291,4 @@ if (nargout==11)
prior.ArtificialSampleSize = fix(dsge_prior_weight*NumberOfObservations);
prior.DF = prior.ArtificialSampleSize - NumberOfParameters - NumberOfObservedVariables;
prior.iGXX = iGXX;
end
if fval == Inf
pause
end

2
matlab/dsgevar_posterior_density.m
View File

@ -47,7 +47,7 @@ if ~options_.noconstant
bvar.NumberOfVariables;
end
[fval,grad,hess,cost_flag,SteadyState,trend_coeff,info,Steady,PHI,SIGMAu,iXX,prior] = dsge_var_likelihood(deep',DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
[fval,cost_flag,info,PHI,SIGMAu,iXX,prior] = dsge_var_likelihood(deep',DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
% Conditionnal posterior density of the lagged matrices (given Sigma) ->
% Matric-variate normal distribution.

4
matlab/optimization/numgrad3.m
View File

@ -38,9 +38,9 @@ badg=0;
for i=1:n
xiold = x(i);
x(i) = xiold+h;
f1 = penalty_objective_function(x, fcn, penalty, varargin{:});
f1 = penalty_objective_function(x, fcn, penatly, varargin{:});
x(i) = xiold-h;
f2 = penalty_objective_function(x, fcn, penalty, varargin{:});
f2 = penalty_objective_function(x, fcn, penatly, varargin{:});
g0 = (f1-f2)/H;
if abs(g0)< 1e15
g(i)=g0;

3
matlab/optimization/penalty_objective_function.m
View File

@ -1,8 +1,5 @@
function [fval,DLIK,Hess,exit_flag] = objective_function_penalty(x0,fcn,penalty,varargin)
[fval,DLIK,Hess,exit_flag,SteadyState,trend_coeff,info] = fcn(x0,varargin{:});
if info(1) ~= 0
fval = penalty + info(2);
end