Commit 857168dd authored by Frédéric Karamé's avatar Frédéric Karamé

Added routines for Dynamic Striated Metropolis Hastings.

parent 84d213ea
function [ ix2, temperedlogpost, loglik, ModelName, MetropolisFolder, npar, NumberOfParticles, bayestopt_] = ...
DSMH_initialization(TargetFun, xparam1, mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_)
% function [ ix2, ilogpo2, ModelName, MetropolisFolder, FirstBlock, FirstLine, npar, NumberOfParticles, bayestopt_] = ...
% DSMH_initialization(TargetFun, xparam1, mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_)
% Dynamic Striated Metropolis-Hastings initialization.
%
% INPUTS
% o TargetFun [char] string specifying the name of the objective
% function (tempered posterior kernel and likelihood).
% o xparam1 [double] (p*1) vector of parameters to be estimated (initial values).
% o mh_bounds [double] (p*2) matrix defining lower and upper bounds for the parameters.
% o dataset_ data structure
% o dataset_info dataset info structure
% o options_ options structure
% o M_ model structure
% o estim_params_ estimated parameters structure
% o bayestopt_ estimation options structure
% o oo_ outputs structure
%
% OUTPUTS
% o ix2 [double] (NumberOfParticles*npar) vector of starting points for different chains
% o ilogpo2 [double] (NumberOfParticles*1) vector of initial posterior values for different chains
% o iloglik2 [double] (NumberOfParticles*1) vector of initial likelihood values for different chains
% o ModelName [string] name of the mod-file
% o MetropolisFolder [string] path to the Metropolis subfolder
% o npar [scalar] number of parameters estimated
% o NumberOfParticles [scalar] Number of particles requested for the parameters distributions
% o bayestopt_ [structure] estimation options structure
%
% SPECIAL REQUIREMENTS
% None.
% Copyright (C) 2006-2017 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
%Initialize outputs
ix2 = [];
ilogpo2 = [];
iloglik2 = [];
ModelName = [];
MetropolisFolder = [];
npar = [];
NumberOfParticles = [];
ModelName = M_.fname;
if ~isempty(M_.bvar)
ModelName = [ModelName '_bvar'];
end
MetropolisFolder = CheckPath('dsmh',M_.dname);
BaseName = [MetropolisFolder filesep ModelName];
NumberOfParticles = options_.dsmh.number_of_particles; %Number of particles for the parameters
npar = length(xparam1);
% Here we start a new DS Metropolis-Hastings, previous draws are discarded.
disp('Estimation::dsmh: Initialization...')
% Delete old dsmh files if any...
files = dir([BaseName '_dsmh*_blck*.mat']);
%if length(files)
% delete([BaseName '_dsmh*_blck*.mat']);
% disp('Estimation::smc: Old dsmh-files successfully erased!')
%end
% Delete old log file.
file = dir([ MetropolisFolder '/dsmh.log']);
%if length(file)
% delete([ MetropolisFolder '/dsmh.log']);
% disp('Estimation::dsmh: Old dsmh.log file successfully erased!')
% disp('Estimation::dsmh: Creation of a new dsmh.log file.')
%end
fidlog = fopen([MetropolisFolder '/dsmh.log'],'w');
fprintf(fidlog,'%% DSMH log file (Dynare).\n');
fprintf(fidlog,['%% ' datestr(now,0) '.\n']);
fprintf(fidlog,' \n\n');
fprintf(fidlog,'%% Session 1.\n');
fprintf(fidlog,' \n');
prior_draw(bayestopt_,options_.prior_trunc);
% Find initial values for the NumberOfParticles chains...
set_dynare_seed('default');
fprintf(fidlog,[' Initial values of the parameters:\n']);
disp('Estimation::dsmh: Searching for initial values...');
ix2 = zeros(npar,NumberOfParticles);
temperedlogpost = zeros(NumberOfParticles,1);
loglik = zeros(NumberOfParticles,1);
%stderr = sqrt(bsxfun(@power,mh_bounds.ub-mh_bounds.lb,2)/12)/10;
for j=1:NumberOfParticles
validate = 0;
while validate == 0
candidate = prior_draw()';
% candidate = xparam1(:) + 0.001*randn(npar,1);%bsxfun(@times,stderr,randn(npar,1)) ;
if all(candidate(:) >= mh_bounds.lb) && all(candidate(:) <= mh_bounds.ub)
ix2(:,j) = candidate ;
[temperedlogpost(j),loglik(j)] = tempered_likelihood(TargetFun,candidate,0.0,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_);
if isfinite(loglik(j)) % if returned log-density is Inf or Nan (penalized value)
validate = 1;
end
end
end
end
fprintf(fidlog,' \n');
disp('Estimation::dsmh: Initial values found!')
skipline()
This diff is collapsed.
function [tlogpostkern,loglik] = tempered_likelihood(TargetFun,xparam1,lambda,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_)
logpostkern = -feval(TargetFun,xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
logprior = priordens(xparam1,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7,bayestopt_.p3,bayestopt_.p4);
loglik = logpostkern-logprior ;
tlogpostkern = lambda*loglik + logprior;
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