286 lines
12 KiB
C++
286 lines
12 KiB
C++
/*
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* Copyright (C) 2010-2013 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 <sstream>
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#include "Vector.hh"
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#include "Matrix.hh"
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#include "LogPosteriorDensity.hh"
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#include <dynmex.h>
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class LogposteriorMexErrMsgTxtException
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{
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public:
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std::string errMsg;
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LogposteriorMexErrMsgTxtException(const std::string &msg) : errMsg(msg)
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{
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}
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inline const char *getErrMsg() { return errMsg.c_str(); }
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};
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void
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fillEstParamsInfo(const mxArray *bayestopt_, const mxArray *estim_params_info, EstimatedParameter::pType type,
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std::vector<EstimatedParameter> &estParamsInfo)
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{
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const mxArray *bayestopt_ubp = mxGetField(bayestopt_, 0, "ub"); // upper bound
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const mxArray *bayestopt_lbp = mxGetField(bayestopt_, 0, "lb"); // lower bound
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const mxArray *bayestopt_p1p = mxGetField(bayestopt_, 0, "p1"); // prior mean
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const mxArray *bayestopt_p2p = mxGetField(bayestopt_, 0, "p2"); // prior standard deviation
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const mxArray *bayestopt_p3p = mxGetField(bayestopt_, 0, "p3"); // lower bound
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const mxArray *bayestopt_p4p = mxGetField(bayestopt_, 0, "p4"); // upper bound
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const mxArray *bayestopt_p6p = mxGetField(bayestopt_, 0, "p6"); // first hyper-parameter (\alpha for the BETA and GAMMA distributions, s for the INVERSE GAMMAs, expectation for the GAUSSIAN distribution, lower bound for the UNIFORM distribution).
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const mxArray *bayestopt_p7p = mxGetField(bayestopt_, 0, "p7"); // second hyper-parameter (\beta for the BETA and GAMMA distributions, \nu for the INVERSE GAMMAs, standard deviation for the GAUSSIAN distribution, upper bound for the UNIFORM distribution).
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const mxArray *bayestopt_jscalep = mxGetField(bayestopt_, 0, "jscale"); // MCMC jump scale
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const size_t bayestopt_size = mxGetM(bayestopt_);
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const VectorConstView bayestopt_ub(mxGetPr(bayestopt_ubp), bayestopt_size, 1);
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const VectorConstView bayestopt_lb(mxGetPr(bayestopt_lbp), bayestopt_size, 1);
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const VectorConstView bayestopt_p1(mxGetPr(bayestopt_p1p), bayestopt_size, 1); //=mxGetField(bayestopt_, 0, "p1");
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const VectorConstView bayestopt_p2(mxGetPr(bayestopt_p2p), bayestopt_size, 1); //=mxGetField(bayestopt_, 0, "p2");
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const VectorConstView bayestopt_p3(mxGetPr(bayestopt_p3p), bayestopt_size, 1); //=mxGetField(bayestopt_, 0, "p3");
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const VectorConstView bayestopt_p4(mxGetPr(bayestopt_p4p), bayestopt_size, 1); //=mxGetField(bayestopt_, 0, "p4");
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const VectorConstView bayestopt_p6(mxGetPr(bayestopt_p6p), bayestopt_size, 1); //=mxGetField(bayestopt_, 0, "p6");
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const VectorConstView bayestopt_p7(mxGetPr(bayestopt_p7p), bayestopt_size, 1); //=mxGetField(bayestopt_, 0, "p7");
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const VectorConstView bayestopt_jscale(mxGetPr(bayestopt_jscalep), bayestopt_size, 1); //=mxGetField(bayestopt_, 0, "jscale");
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// loop processsing
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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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size_t bayestopt_count = estParamsInfo.size();
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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 #2 or #3
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double par_low_bound = bayestopt_lb(bayestopt_count); col++; //#3 epi(i, col++);
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double par_up_bound = bayestopt_ub(bayestopt_count); col++; //#4 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 low_bound = bayestopt_p3(bayestopt_count);
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double up_bound = bayestopt_p4(bayestopt_count);
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double fhp = bayestopt_p6(bayestopt_count); // double p3 = epi(i, col++);
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double shp = bayestopt_p7(bayestopt_count); // double p4 = epi(i, col++);
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Prior *p = Prior::constructPrior(shape, mean, std, low_bound, up_bound, fhp, shp); //1.0,INFINITY);//p3, p4);
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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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par_low_bound, par_up_bound, p));
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bayestopt_count++;
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}
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}
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template <class VEC1, class VEC2>
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double
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logposterior(VEC1 &estParams, const MatrixConstView &data,
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const mxArray *options_, const mxArray *M_, const mxArray *estim_params_,
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const mxArray *bayestopt_, const mxArray *oo_, VEC2 &steadyState, double *trend_coeff,
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VectorView &deepParams, Matrix &H, MatrixView &Q)
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{
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double loglinear = *mxGetPr(mxGetField(options_, 0, "loglinear"));
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if (loglinear == 1)
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throw LogposteriorMexErrMsgTxtException("Option loglinear is not supported");
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if (*mxGetPr(mxGetField(options_, 0, "endogenous_prior")) == 1)
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throw LogposteriorMexErrMsgTxtException("Option endogenous_prior is not supported");
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double with_trend = *mxGetPr(mxGetField(bayestopt_, 0, "with_trend"));
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if (with_trend == 1)
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throw LogposteriorMexErrMsgTxtException("Observation trends are not supported");
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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 basename(fName);
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mxFree(fName);
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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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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)
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throw LogposteriorMexErrMsgTxtException("Purely backward or purely forward models are not supported");
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if (lli.getCols() != n_endo)
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throw LogposteriorMexErrMsgTxtException("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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size_t presample = (size_t) *mxGetPr(mxGetField(options_, 0, "presample"));
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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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throw LogposteriorMexErrMsgTxtException("options_.varobs_id must be a row vector");
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size_t n_varobs = mxGetN(varobs_mx);
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// substract 1.0 from obsverved variables index
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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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throw LogposteriorMexErrMsgTxtException("Data does not have 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(bayestopt_, mxGetField(estim_params_, 0, "var_exo"), EstimatedParameter::shock_SD,
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estParamsInfo);
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fillEstParamsInfo(bayestopt_, mxGetField(estim_params_, 0, "var_endo"), EstimatedParameter::measureErr_SD,
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estParamsInfo);
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fillEstParamsInfo(bayestopt_, mxGetField(estim_params_, 0, "corrx"), EstimatedParameter::shock_Corr,
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estParamsInfo);
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fillEstParamsInfo(bayestopt_, mxGetField(estim_params_, 0, "corrn"), EstimatedParameter::measureErr_Corr,
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estParamsInfo);
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fillEstParamsInfo(bayestopt_, 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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bool noconstant = (bool) *mxGetPr(mxGetField(options_, 0, "noconstant"));
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// Allocate LogPosteriorDensity object
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LogPosteriorDensity lpd(basename, epd, n_endo, n_exo, zeta_fwrd, zeta_back, zeta_mixed, zeta_static,
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qz_criterium, varobs, riccati_tol, lyapunov_tol, noconstant);
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// Construct arguments of compute() method
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// Compute the posterior
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double logPD = lpd.compute(steadyState, estParams, deepParams, data, Q, H, presample);
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// Cleanups
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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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return logPD;
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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 != 7 )
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DYN_MEX_FUNC_ERR_MSG_TXT("logposterior: exactly 7 input arguments are required.");
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if (nlhs > 9 )
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DYN_MEX_FUNC_ERR_MSG_TXT("logposterior returns 8 output arguments at the most.");
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// Check and retrieve the RHS arguments
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if (!mxIsDouble(prhs[0]) || mxGetN(prhs[0]) != 1)
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DYN_MEX_FUNC_ERR_MSG_TXT("logposterior: 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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for (int i = 1; i < 7; ++i)
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if (!mxIsStruct(prhs[i]))
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{
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std::stringstream msg;
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msg << "logposterior: argument " << i+1 << " must be a Matlab structure";
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DYN_MEX_FUNC_ERR_MSG_TXT(msg.str().c_str());
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}
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const mxArray *dataset = prhs[1];
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const mxArray *options_ = prhs[2];
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const mxArray *M_ = prhs[3];
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const mxArray *estim_params_ = prhs[4];
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const mxArray *bayestopt_ = prhs[5];
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const mxArray *oo_ = prhs[6];
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const mxArray *dataset_data = mxGetField(dataset,0,"data");
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MatrixConstView data(mxGetPr(dataset_data), mxGetM(dataset_data), mxGetN(dataset_data), mxGetM(dataset_data));
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// Creaete LHS arguments
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size_t endo_nbr = (size_t) *mxGetPr(mxGetField(M_, 0, "endo_nbr"));
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size_t exo_nbr = (size_t) *mxGetPr(mxGetField(M_, 0, "exo_nbr"));
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size_t param_nbr = (size_t) *mxGetPr(mxGetField(M_, 0, "param_nbr"));
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size_t varobs_nbr = mxGetM(mxGetField(options_, 0, "varobs"));
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plhs[0] = mxCreateDoubleMatrix(1, 1, mxREAL);
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plhs[1] = mxCreateDoubleMatrix(1, 1, mxREAL);
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plhs[2] = mxCreateDoubleMatrix(endo_nbr, 1, mxREAL);
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plhs[3] = mxCreateDoubleMatrix(varobs_nbr, 1, mxREAL);
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plhs[4] = mxCreateDoubleMatrix(1, 1, mxREAL);
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plhs[5] = mxCreateDoubleMatrix(param_nbr, 1, mxREAL);
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plhs[6] = mxCreateDoubleMatrix(varobs_nbr, varobs_nbr, mxREAL);
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plhs[7] = mxCreateDoubleMatrix(exo_nbr, exo_nbr, mxREAL);
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double *lik = mxGetPr(plhs[0]);
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double *exit_flag = mxGetPr(plhs[1]);
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VectorView steadyState(mxGetPr(mxGetField(oo_,0,"steady_state")),endo_nbr, 1);
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VectorView deepParams(mxGetPr(mxGetField(M_, 0, "params")),param_nbr,1);
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MatrixView Q(mxGetPr(mxGetField(M_, 0, "Sigma_e")), exo_nbr, exo_nbr, exo_nbr);
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Matrix H(varobs_nbr,varobs_nbr);
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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(H_mx), varobs_nbr, varobs_nbr, varobs_nbr);
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double *trend_coeff = mxGetPr(plhs[3]);
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double *info_mx = mxGetPr(plhs[4]);
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// Compute and return the value
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try
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{
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*lik = logposterior(estParams, data, options_, M_, estim_params_, bayestopt_, oo_,
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steadyState, trend_coeff, deepParams, H, Q);
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*info_mx = 0;
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*exit_flag = 0;
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}
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catch (LogposteriorMexErrMsgTxtException e)
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{
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DYN_MEX_FUNC_ERR_MSG_TXT(e.getErrMsg());
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}
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catch (SteadyStateSolver::SteadyStateException e)
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{
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DYN_MEX_FUNC_ERR_MSG_TXT(e.message.c_str());
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}
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}
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