Added new loglikelihood DLL (does not yet contain prior computation, only the likelihood)
Sébastien Villemot
parent
7410094588
commit
04905660b8
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@ -0,0 +1,53 @@
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vpath %.cc $(top_srcdir)/../../sources/estimation $(top_srcdir)/../../sources/estimation/libmat $(top_srcdir)/../../sources/estimation/utils
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vpath %.hh $(top_srcdir)/../../sources/estimation $(top_srcdir)/../../sources/estimation/libmat
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CPPFLAGS += -I$(top_srcdir)/../../sources/estimation/libmat -I$(top_srcdir)/../../sources/estimation/utils
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noinst_PROGRAMS = loglikelihood
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loglikelihood_LDADD = $(LIBADD_DLOPEN)
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MAT_SRCS = \
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Matrix.hh \
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Matrix.cc \
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Vector.hh \
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Vector.cc \
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BlasBindings.hh \
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DiscLyapFast.hh \
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GeneralizedSchurDecomposition.cc \
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GeneralizedSchurDecomposition.hh \
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LapackBindings.hh \
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LUSolver.cc \
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LUSolver.hh \
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QRDecomposition.cc \
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QRDecomposition.hh \
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VDVEigDecomposition.cc \
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VDVEigDecomposition.hh
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nodist_loglikelihood_SOURCES = \
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$(MAT_SRCS) \
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DecisionRules.cc \
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DecisionRules.hh \
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DetrendData.cc \
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DetrendData.hh \
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EstimatedParameter.cc \
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EstimatedParameter.hh \
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EstimatedParametersDescription.cc \
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EstimatedParametersDescription.hh \
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EstimationSubsample.cc \
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EstimationSubsample.hh \
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InitializeKalmanFilter.cc \
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InitializeKalmanFilter.hh \
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KalmanFilter.cc \
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KalmanFilter.hh \
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LogLikelihoodSubSample.cc \
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LogLikelihoodSubSample.hh \
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LogLikelihoodMain.hh \
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LogLikelihoodMain.cc \
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ModelSolution.cc \
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ModelSolution.hh \
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Prior.cc \
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Prior.hh \
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dynamic_dll.cc \
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dynamic_dll.hh \
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loglikelihood.cc
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@ -2,7 +2,7 @@ ACLOCAL_AMFLAGS = -I ../../../m4
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# libdynare++ must come before gensylv, k_order_perturbation, dynare_simul_
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if DO_SOMETHING
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SUBDIRS = mjdgges kronecker bytecode libdynare++ gensylv k_order_perturbation dynare_simul_
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SUBDIRS = mjdgges kronecker bytecode libdynare++ gensylv k_order_perturbation dynare_simul_ loglikelihood
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if HAVE_GSL
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SUBDIRS += swz
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endif
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@ -102,6 +102,7 @@ AC_CONFIG_FILES([Makefile
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bytecode/Makefile
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k_order_perturbation/Makefile
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dynare_simul_/Makefile
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swz/Makefile])
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swz/Makefile
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loglikelihood/Makefile])
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AC_OUTPUT
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@ -0,0 +1,2 @@
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include ../mex.am
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include ../../loglikelihood.am
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@ -2,7 +2,7 @@ ACLOCAL_AMFLAGS = -I ../../../m4
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# libdynare++ must come before gensylv, k_order_perturbation, dynare_simul_
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if DO_SOMETHING
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SUBDIRS = mjdgges kronecker bytecode libdynare++ gensylv k_order_perturbation dynare_simul_
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SUBDIRS = mjdgges kronecker bytecode libdynare++ gensylv k_order_perturbation dynare_simul_ loglikelihood
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if HAVE_GSL
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SUBDIRS += swz
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endif
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@ -84,6 +84,7 @@ AC_CONFIG_FILES([Makefile
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gensylv/Makefile
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k_order_perturbation/Makefile
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dynare_simul_/Makefile
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swz/Makefile])
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swz/Makefile
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loglikelihood/Makefile])
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AC_OUTPUT
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@ -0,0 +1,2 @@
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include ../mex.am
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include ../../loglikelihood.am
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@ -11,19 +11,25 @@ EXTRA_DIST = \
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DecisionRules.hh \
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DetrendData.cc \
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DetrendData.hh \
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EstimatedParameter.cc \
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EstimatedParameter.hh \
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EstimatedParametersDescription.cc \
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EstimatedParametersDescription.hh \
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EstimationSubsample.cc \
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EstimationSubsample.hh \
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InitializeKalmanFilter.cc \
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InitializeKalmanFilter.hh \
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KalmanFilter.cc \
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KalmanFilter.hh \
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LogLikelihoodMain.hh \
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LogLikelihoodSubSample.cc \
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LogLikelihoodSubSample.hh \
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LogLikelihoodMain.hh \
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LogLikelihoodMain.cc \
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ModelSolution.cc \
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ModelSolution.hh \
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Prior.cc \
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Prior.hh \
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loglikelihood.cc \
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utils/dynamic_dll.cc \
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utils/dynamic_dll.hh \
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utils/ts_exception.h
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@ -36,3 +36,22 @@ Prior::~Prior()
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}
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Prior *
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Prior::constructPrior(pShape shape, double mean, double standard, double lower_bound, double upper_bound, double fhp, double shp)
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{
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switch (shape)
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{
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case Beta:
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return new BetaPrior(mean, standard, lower_bound, upper_bound, fhp, shp);
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case Gamma:
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return new GammaPrior(mean, standard, lower_bound, upper_bound, fhp, shp);
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case Gaussian:
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return new GaussianPrior(mean, standard, lower_bound, upper_bound, fhp, shp);
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case Inv_gamma_1:
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return new InvGamma1_Prior(mean, standard, lower_bound, upper_bound, fhp, shp);
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case Uniform:
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return new UniformPrior(mean, standard, lower_bound, upper_bound, fhp, shp);
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case Inv_gamma_2:
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return new InvGamma2_Prior(mean, standard, lower_bound, upper_bound, fhp, shp);
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}
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}
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@ -87,6 +87,7 @@ public:
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return 0.0;
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};
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static Prior *constructPrior(pShape shape, double mean, double standard, double lower_bound, double upper_bound, double fhp, double shp);
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};
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struct BetaPrior : public Prior
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@ -5,15 +5,18 @@ endif
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endif
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EXTRA_DIST = \
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Matrix.hh \
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Matrix.cc \
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Vector.hh \
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Vector.cc \
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BlasBindings.hh \
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DiscLyapFast.hh \
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GeneralizedSchurDecomposition.cc \
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GeneralizedSchurDecomposition.hh \
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LapackBindings.hh \
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LUSolver.cc \
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LUSolver.hh \
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Matrix.cc \
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Matrix.hh \
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QRDecomposition.cc \
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QRDecomposition.hh \
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Vector.cc \
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Vector.hh
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VDVEigDecomposition.cc \
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VDVEigDecomposition.hh
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@ -0,0 +1,204 @@
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/*
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* Copyright (C) 2010 Dynare Team
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*
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* This file is part of Dynare.
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*
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* Dynare is free software: you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation, either version 3 of the License, or
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* (at your option) any later version.
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*
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* Dynare is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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*/
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#include <string>
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#include <vector>
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#include <algorithm>
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#include <functional>
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#include "Vector.hh"
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#include "Matrix.hh"
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#include "LogLikelihoodMain.hh"
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#include "mex.h"
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void
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fillEstParamsInfo(const mxArray *estim_params_info, EstimatedParameter::pType type,
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std::vector<EstimatedParameter> &estParamsInfo)
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{
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size_t m = mxGetM(estim_params_info), n = mxGetN(estim_params_info);
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MatrixConstView epi(mxGetPr(estim_params_info), m, n, m);
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for (size_t i = 0; i < m; i++)
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{
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size_t col = 0;
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size_t id1 = (size_t) epi(i, col++) - 1;
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size_t id2 = 0;
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if (type == EstimatedParameter::shock_Corr
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|| type == EstimatedParameter::measureErr_Corr)
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id2 = (size_t) epi(i, col++) - 1;
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col++; // Skip init_val
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double low_bound = epi(i, col++);
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double up_bound = epi(i, col++);
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Prior::pShape shape = (Prior::pShape) epi(i, col++);
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double mean = epi(i, col++);
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double std = epi(i, col++);
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double p3 = epi(i, col++);
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double p4 = epi(i, col++);
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// Prior *p = Prior::constructPrior(shape, mean, std, low_bound, up_bound, p3, p4);
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Prior *p = NULL;
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// Only one subsample
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std::vector<size_t> subSampleIDs;
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subSampleIDs.push_back(0);
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estParamsInfo.push_back(EstimatedParameter(type, id1, id2, subSampleIDs,
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low_bound, up_bound, p));
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}
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}
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double
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loglikelihood(const VectorConstView &estParams, const MatrixConstView &data,
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const std::string &mexext)
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{
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// Retrieve pointers to global variables
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const mxArray *M_ = mexGetVariablePtr("global", "M_");
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const mxArray *oo_ = mexGetVariablePtr("global", "oo_");
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const mxArray *options_ = mexGetVariablePtr("global", "options_");
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const mxArray *estim_params_ = mexGetVariablePtr("global", "estim_params_");
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// Construct arguments of constructor of LogLikelihoodMain
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char *fName = mxArrayToString(mxGetField(M_, 0, "fname"));
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std::string dynamicDllFile(fName);
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mxFree(fName);
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dynamicDllFile += "_dynamic" + mexext;
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size_t n_endo = (size_t) *mxGetPr(mxGetField(M_, 0, "endo_nbr"));
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size_t n_exo = (size_t) *mxGetPr(mxGetField(M_, 0, "exo_nbr"));
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size_t n_param = (size_t) *mxGetPr(mxGetField(M_, 0, "param_nbr"));
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size_t n_estParams = estParams.getSize();
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std::vector<size_t> zeta_fwrd, zeta_back, zeta_mixed, zeta_static;
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const mxArray *lli_mx = mxGetField(M_, 0, "lead_lag_incidence");
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MatrixConstView lli(mxGetPr(lli_mx), mxGetM(lli_mx), mxGetN(lli_mx), mxGetM(lli_mx));
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if (lli.getRows() != 3 || lli.getCols() != n_endo)
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mexErrMsgTxt("Incorrect lead/lag incidence matrix");
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for (size_t i = 0; i < n_endo; i++)
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{
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if (lli(0, i) == 0 && lli(2, i) == 0)
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zeta_static.push_back(i);
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else if (lli(0, i) != 0 && lli(2, i) == 0)
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zeta_back.push_back(i);
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else if (lli(0, i) == 0 && lli(2, i) != 0)
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zeta_fwrd.push_back(i);
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else
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zeta_mixed.push_back(i);
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}
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double qz_criterium = *mxGetPr(mxGetField(options_, 0, "qz_criterium"));
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double lyapunov_tol = *mxGetPr(mxGetField(options_, 0, "lyapunov_complex_threshold"));
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double riccati_tol = *mxGetPr(mxGetField(options_, 0, "riccati_tol"));
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std::vector<size_t> varobs;
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const mxArray *varobs_mx = mxGetField(options_, 0, "varobs_id");
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if (mxGetM(varobs_mx) != 1)
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mexErrMsgTxt("options_.varobs_id must be a row vector");
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size_t n_varobs = mxGetN(varobs_mx);
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std::transform(mxGetPr(varobs_mx), mxGetPr(varobs_mx) + n_varobs, back_inserter(varobs),
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std::bind2nd(std::minus<size_t>(), 1));
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if (data.getRows() != n_varobs)
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mexErrMsgTxt("Data has not as many rows as there are observed variables");
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std::vector<EstimationSubsample> estSubsamples;
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estSubsamples.push_back(EstimationSubsample(0, data.getCols() - 1));
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std::vector<EstimatedParameter> estParamsInfo;
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fillEstParamsInfo(mxGetField(estim_params_, 0, "var_exo"), EstimatedParameter::shock_SD,
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estParamsInfo);
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fillEstParamsInfo(mxGetField(estim_params_, 0, "var_endo"), EstimatedParameter::measureErr_SD,
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estParamsInfo);
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fillEstParamsInfo(mxGetField(estim_params_, 0, "corrx"), EstimatedParameter::shock_Corr,
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estParamsInfo);
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fillEstParamsInfo(mxGetField(estim_params_, 0, "corrn"), EstimatedParameter::measureErr_Corr,
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estParamsInfo);
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fillEstParamsInfo(mxGetField(estim_params_, 0, "param_vals"), EstimatedParameter::deepPar,
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estParamsInfo);
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EstimatedParametersDescription epd(estSubsamples, estParamsInfo);
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// Allocate LogLikelihoodMain object
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int info;
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LogLikelihoodMain llm(dynamicDllFile, epd, n_endo, n_exo, zeta_fwrd, zeta_back, zeta_mixed, zeta_static,
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qz_criterium, varobs, riccati_tol, lyapunov_tol, info);
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// Construct arguments of compute() method
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Matrix steadyState(n_endo, 1);
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mat::get_col(steadyState, 0) = VectorConstView(mxGetPr(mxGetField(oo_, 0, "steady_state")), n_endo, 1);
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Vector estParams2(n_estParams);
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estParams2 = estParams;
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Vector deepParams(n_param);
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deepParams = VectorConstView(mxGetPr(mxGetField(M_, 0, "params")), n_param, 1);
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Matrix Q(n_exo);
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Q = MatrixConstView(mxGetPr(mxGetField(M_, 0, "Sigma_e")), n_exo, n_exo, 1);
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Matrix H(n_varobs);
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const mxArray *H_mx = mxGetField(M_, 0, "H");
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if (mxGetM(H_mx) == 1 && mxGetN(H_mx) == 1 && *mxGetPr(H_mx) == 0)
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H.setAll(0.0);
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else
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H = MatrixConstView(mxGetPr(mxGetField(M_, 0, "H")), n_varobs, n_varobs, 1);
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// Compute the likelihood
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double lik = llm.compute(steadyState, estParams2, deepParams, data, Q, H, 0, info);
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// Cleanups
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/*
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for (std::vector<EstimatedParameter>::iterator it = estParamsInfo.begin();
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it != estParamsInfo.end(); it++)
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delete it->prior;
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*/
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return lik;
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}
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void
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mexFunction(int nlhs, mxArray *plhs[],
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int nrhs, const mxArray *prhs[])
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{
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if (nrhs != 3)
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mexErrMsgTxt("loglikelihood: exactly three arguments are required.");
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if (nlhs != 1)
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mexErrMsgTxt("loglikelihood: exactly one return argument is required.");
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// Check and retrieve the arguments
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if (!mxIsDouble(prhs[0]) || mxGetN(prhs[0]) != 1)
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mexErrMsgTxt("First argument must be a column vector of double-precision numbers");
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VectorConstView estParams(mxGetPr(prhs[0]), mxGetM(prhs[0]), 1);
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if (!mxIsDouble(prhs[1]))
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mexErrMsgTxt("Second argument must be a matrix of double-precision numbers");
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MatrixConstView data(mxGetPr(prhs[1]), mxGetM(prhs[1]), mxGetN(prhs[1]), mxGetM(prhs[1]));
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if (!mxIsChar(prhs[2]))
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mexErrMsgTxt("Third argument must be a character string");
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char *mexext_mx = mxArrayToString(prhs[2]);
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std::string mexext(mexext_mx);
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mxFree(mexext_mx);
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// Compute and return the value
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double lik = loglikelihood(estParams, data, mexext);
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plhs[0] = mxCreateDoubleMatrix(1, 1, mxREAL);
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*mxGetPr(plhs[0]) = lik;
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}
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@ -250,6 +250,12 @@ SymbolTable::writeOutput(ostream &output) const throw (NotYetFrozenException)
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for (vector<int>::const_iterator it = varobs.begin();
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it != varobs.end(); it++)
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output << "options_.varobs = strvcat(options_.varobs, '" << getName(*it) << "');" << endl;
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output << "options_.varobs_id = [ ";
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for (vector<int>::const_iterator it = varobs.begin();
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it != varobs.end(); it++)
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output << getTypeSpecificID(*it)+1 << " ";
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output << " ];" << endl;
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}
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}
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