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remove global objective_function_penalty_base

time-shift
Michel Juillard 2015-09-30 09:44:29 +02:00
parent 3966296aca
commit cf858c7fcb
10 changed files with 58 additions and 70 deletions
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2
matlab/TaRB_metropolis_hastings_core.m
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@ -59,7 +59,6 @@ function myoutput = TaRB_metropolis_hastings_core(myinputs,fblck,nblck,whoiam, T
%
% 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;
if nargin<4,
whoiam=0;
@ -163,7 +162,6 @@ for curr_chain = fblck:nblck,
[xopt_current_block, fval, exitflag, hess_mat_optimizer, options_, Scale] = dynare_minimize_objective(@TaRB_optimizer_wrapper,par_start_current_block,options_.TaRB.mode_compute,options_,[mh_bounds.lb(indices(blocks==block_iter,1),1) mh_bounds.ub(indices(blocks==block_iter,1),1)],bayestopt_.name,bayestopt_,[],...
current_draw,indices(blocks==block_iter,1),TargetFun,...% inputs for wrapper
dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_); %inputs for objective
objective_function_penalty_base=Inf; %reset penalty that may have been changed by optimizer
%% covariance for proposal density
hessian_mat = reshape(hessian('TaRB_optimizer_wrapper',xopt_current_block, ...
options_.gstep,...

2
matlab/cli/prior.m
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@ -40,8 +40,6 @@ if isempty(varargin) || ( isequal(length(varargin), 1) && isequal(varargin{1},'h
return
end
global options_ M_ estim_params_ bayestopt_ oo_ objective_function_penalty_base
donesomething = false;
% Temporarly change qz_criterium option value

5
matlab/dynare_estimation_init.m
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@ -49,8 +49,6 @@ function [dataset_, dataset_info, xparam1, hh, M_, options_, oo_, estim_params_,
% 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
hh = [];
if isempty(gsa_flag)
@ -386,9 +384,6 @@ else% Yes!
end
end
% Set the "size" of penalty.
objective_function_penalty_base = 1e8;
% Get informations about the variables of the model.
dr = set_state_space(oo_.dr,M_,options_);
oo_.dr = dr;

5
matlab/independent_metropolis_hastings_core.m
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@ -37,7 +37,7 @@ if nargin<4,
whoiam=0;
end
global bayestopt_ estim_params_ options_ M_ oo_ objective_function_penalty_base
global bayestopt_ estim_params_ options_ M_ oo_
% Reshape 'myinputs' for local computation.
% In order to avoid confusion in the name space, the instruction struct2local(myinputs) is replaced by:
@ -67,9 +67,6 @@ if whoiam
bayestopt_.p3,bayestopt_.p4,1);
end
% (re)Set the penalty.
objective_function_penalty_base = Inf;
MetropolisFolder = CheckPath('metropolis',M_.dname);
BaseName = [MetropolisFolder filesep ModelName];

40
matlab/minus_logged_prior_density.m
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@ -29,8 +29,6 @@ function [fval,fake_1, fake_2, exit_flag ] = minus_logged_prior_density(xparams,
% 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
fake_1 = 1;
fake_2 = 1;
@ -44,18 +42,20 @@ info = 0;
% Return, with endogenous penalty, if some parameters are smaller than the lower bound of the prior domain.
if ~isequal(DynareOptions.mode_compute,1) && any(xparams<p3)
k = find(xparams<p3);
fval = objective_function_penalty_base+sum((p3(k)-xparams(k)).^2);
fval = Inf;
exit_flag = 0;
info = 41;
info(1) = 41;
info(2) = sum((p3(k)-xparams(k)).^2);
return
end
% Return, with endogenous penalty, if some parameters are greater than the upper bound of the prior domain.
if ~isequal(DynareOptions.mode_compute,1) && any(xparams>p4)
k = find(xparams>p4);
fval = objective_function_penalty_base+sum((xparams(k)-p4(k)).^2);
fval = Inf;
exit_flag = 0;
info = 42;
info(1) = 42;
info(2) = sum((xparams(k)-p4(k)).^2);
return
end
@ -71,18 +71,20 @@ if ~issquare(Q) || EstimatedParams.ncx || isfield(EstimatedParams,'calibrated_co
[Q_is_positive_definite, penalty] = ispd(Q);
if ~Q_is_positive_definite
% The variance-covariance matrix of the structural innovations is not definite positive. We have to compute the eigenvalues of this matrix in order to build the endogenous penalty.
fval = objective_function_penalty_base+penalty;
fval = Inf;
exit_flag = 0;
info = 43;
info(1) = 43;
info(2) = penalty;
return
end
if isfield(EstimatedParams,'calibrated_covariances')
correct_flag=check_consistency_covariances(Q);
if ~correct_flag
penalty = sum(Q(EstimatedParams.calibrated_covariances.position).^2);
fval = objective_function_penalty_base+penalty;
fval = Inf;
exit_flag = 0;
info = 71;
info(1) = 71;
info(2) = penalty;
return
end
end
@ -94,18 +96,20 @@ if ~issquare(H) || EstimatedParams.ncn || isfield(EstimatedParams,'calibrated_co
[H_is_positive_definite, penalty] = ispd(H);
if ~H_is_positive_definite
% The variance-covariance matrix of the measurement errors is not definite positive. We have to compute the eigenvalues of this matrix in order to build the endogenous penalty.
fval = objective_function_penalty_base+penalty;
fval = Inf;
exit_flag = 0;
info = 44;
info(1) = 44;
info(2) = penalty;
return
end
if isfield(EstimatedParams,'calibrated_covariances_ME')
correct_flag=check_consistency_covariances(H);
if ~correct_flag
penalty = sum(H(EstimatedParams.calibrated_covariances_ME.position).^2);
fval = objective_function_penalty_base+penalty;
fval = Inf;
exit_flag = 0;
info = 72;
info(1) = 72;
info(2) = penalty;
return
end
end
@ -122,13 +126,13 @@ M_ = set_all_parameters(xparams,EstimatedParams,DynareModel);
% 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) == 19
fval = objective_function_penalty_base+1;
info = info(1);
fval = Inf;
info(1) = info(1);
info(2) = 0.1;
exit_flag = 0;
return
elseif info(1) == 3 || info(1) == 4 || info(1)==6 || info(1) == 20 || info(1) == 21 || info(1) == 23
fval = objective_function_penalty_base+info(2);
info = info(1);
fval = Inf;
exit_flag = 0;
return
end

57
matlab/non_linear_dsge_likelihood.m
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@ -121,8 +121,6 @@ function [fval,ys,trend_coeff,exit_flag,info,Model,DynareOptions,BayesInfo,Dynar
% AUTHOR(S) stephane DOT adjemian AT univ DASH lemans DOT fr
% frederic DOT karame AT univ DASH lemans DOT fr
global objective_function_penalty_base
% Declaration of the penalty as a persistent variable.
persistent init_flag
persistent restrict_variables_idx observed_variables_idx state_variables_idx mf0 mf1
persistent sample_size number_of_state_variables number_of_observed_variables number_of_structural_innovations
@ -145,18 +143,20 @@ end
% Return, with endogenous penalty, if some parameters are smaller than the lower bound of the prior domain.
if (DynareOptions.mode_compute~=1) && any(xparam1<BoundsInfo.lb)
k = find(xparam1(:) < BoundsInfo.lb);
fval = objective_function_penalty_base+sum((BoundsInfo.lb(k)-xparam1(k)).^2);
fval = Inf;
exit_flag = 0;
info = 41;
info(1) = 41;
info(2) = sum((BoundsInfo.lb(k)-xparam1(k)).^2);
return
end
% Return, with endogenous penalty, if some parameters are greater than the upper bound of the prior domain.
if (DynareOptions.mode_compute~=1) && any(xparam1>BoundsInfo.ub)
k = find(xparam1(:)>BoundsInfo.ub);
fval = objective_function_penalty_base+sum((xparam1(k)-BoundsInfo.ub(k)).^2);
fval = Inf;
exit_flag = 0;
info = 42;
info(1) = 42;
info(2) = sum((xparam1(k)-BoundsInfo.ub(k)).^2);
return
end
@ -168,18 +168,20 @@ H = Model.H;
if ~issquare(Q) || EstimatedParameters.ncx || isfield(EstimatedParameters,'calibrated_covariances')
[Q_is_positive_definite, penalty] = ispd(Q);
if ~Q_is_positive_definite
fval = objective_function_penalty_base+penalty;
fval = Inf;
exit_flag = 0;
info = 43;
info(1) = 43;
info(2) = penalty;
return
end
if isfield(EstimatedParameters,'calibrated_covariances')
correct_flag=check_consistency_covariances(Q);
if ~correct_flag
penalty = sum(Q(EstimatedParameters.calibrated_covariances.position).^2);
fval = objective_function_penalty_base+penalty;
fval = Inf;
exit_flag = 0;
info = 71;
info(1) = 71;
info(2) = penalty;
return
end
end
@ -189,18 +191,20 @@ end
if ~issquare(H) || EstimatedParameters.ncn || isfield(EstimatedParameters,'calibrated_covariances_ME')
[H_is_positive_definite, penalty] = ispd(H);
if ~H_is_positive_definite
fval = objective_function_penalty_base+penalty;
fval = Inf;
exit_flag = 0;
info = 44;
info(1) = 44;
info(2) = penalty;
return
end
if isfield(EstimatedParameters,'calibrated_covariances_ME')
correct_flag=check_consistency_covariances(H);
if ~correct_flag
penalty = sum(H(EstimatedParameters.calibrated_covariances_ME.position).^2);
fval = objective_function_penalty_base+penalty;
fval = Inf;
exit_flag = 0;
info = 72;
info(1) = 72;
info(2) = penalty;
return
end
end
@ -215,11 +219,12 @@ end
[T,R,SteadyState,info,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults,'restrict');
if info(1) == 1 || info(1) == 2 || info(1) == 5 || info(1) == 25 || info(1) == 10 || info(1) == 7
fval = objective_function_penalty_base+1;
fval = Inf;
exit_flag = 0;
info(2) = 0.1;
return
elseif info(1) == 3 || info(1) == 4 || info(1)==6 ||info(1) == 19 || info(1) == 20 || info(1) == 21
fval = objective_function_penalty_base+info(2);
fval = Inf;
exit_flag = 0;
return
end
@ -298,12 +303,14 @@ DynareOptions.warning_for_steadystate = 0;
LIK = feval(DynareOptions.particle.algorithm,ReducedForm,Y,start,DynareOptions.particle,DynareOptions.threads);
set_dynare_random_generator_state(s1,s2);
if imag(LIK)
info = 46;
likelihood = objective_function_penalty_base;
info(1) = 46;
info(2) = 0.1;
likelihood = Inf;
exit_flag = 0;
elseif isnan(LIK)
info = 45;
likelihood = objective_function_penalty_base;
info(1) = 45;
info(2) = 0.1;
likelihood = Inf;
exit_flag = 0;
else
likelihood = LIK;
@ -316,15 +323,17 @@ lnprior = priordens(xparam1(:),BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesI
fval = (likelihood-lnprior);
if isnan(fval)
info = 47;
fval = objective_function_penalty_base + 100;
info(1) = 47;
info(2) = 0.1;
fval = Inf;
exit_flag = 0;
return
end
if imag(fval)~=0
info = 48;
fval = objective_function_penalty_base + 100;
info(1) = 48;
info(2) = 0.1
fval = Inf;
exit_flag = 0;
return
end

2
matlab/optimization/newrat.m
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@ -47,8 +47,6 @@ function [xparam1, hh, gg, fval, igg] = newrat(func0, x, analytic_derivation, ft
% 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
penalty = 1e8;
icount=0;

7
matlab/optimize_prior.m
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@ -43,13 +43,6 @@ while look_for_admissible_initial_condition
end
end
% Evaluate the prior density at the initial condition.
objective_function_penalty_base = minus_logged_prior_density(xinit, BayesInfo.pshape, ...
BayesInfo.p6, ...
BayesInfo.p7, ...
BayesInfo.p3, ...
BayesInfo.p4,DynareOptions,ModelInfo,EstimationInfo,DynareResults);
% Maximization of the prior density
[xparams, lpd, hessian_mat] = ...
maximize_prior_density(xinit, BayesInfo.pshape, ...

4
matlab/parallel/masterParallel.m
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@ -28,8 +28,8 @@ function [fOutVar,nBlockPerCPU, totCPU] = masterParallel(Parallel,fBlock,nBlock,
% The number of parallelized threads will be equal to (nBlock-fBlock+1).
%
% Treatment of global variables:
% Global variables used within the called function (e.g.
% objective_function_penalty_base) are wrapped and passed by storing their
% Global variables used within the called function
% are wrapped and passed by storing their
% values at the start of the parallel computation in a file via
% storeGlobalVars.m. This file is then loaded in the separate,
% independent slave Matlab sessions. By keeping them separate, no

4
matlab/random_walk_metropolis_hastings.m
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@ -57,10 +57,6 @@ function random_walk_metropolis_hastings(TargetFun,ProposalFun,xparam1,vv,mh_bou
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
% In Metropolis, we set penalty to Inf so as to reject all parameter sets triggering an error during target density computation
global objective_function_penalty_base
objective_function_penalty_base = Inf;
% Initialization of the random walk metropolis-hastings chains.
[ ix2, ilogpo2, ModelName, MetropolisFolder, fblck, fline, npar, nblck, nruns, NewFile, MAX_nruns, d ] = ...
metropolis_hastings_initialization(TargetFun, xparam1, vv, mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);