100 lines
4.2 KiB
C++
100 lines
4.2 KiB
C++
/*
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* Copyright (C) 2009-2017 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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///////////////////////////////////////////////////////////
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// KalmanFilter.h
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// Implementation of the Class KalmanFilter
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// Created on: 02-Feb-2010 12:44:41
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///////////////////////////////////////////////////////////
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#if !defined(KF_213B0417_532B_4027_9EDF_36C004CB4CD1__INCLUDED_)
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#define KF_213B0417_532B_4027_9EDF_36C004CB4CD1__INCLUDED_
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#include "InitializeKalmanFilter.hh"
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/**
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* Vanilla Kalman filter without constant and with measurement error (use scalar
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* 0 when no measurement error).
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* If multivariate filter is faster, do as in Matlab: start with multivariate
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* filter and switch to univariate filter only in case of singularity
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*
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* mamber functions: compute() and filter()
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* OUTPUT
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* LIK: likelihood
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*
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* REFERENCES
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* See "Filtering and Smoothing of State Vector for Diffuse State Space
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* Models", S.J. Koopman and J. Durbin (2003, in Journal of Time Series
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* Analysis, vol. 24(1), pp. 85-98).
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*/
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class KalmanFilter
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{
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public:
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virtual
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~KalmanFilter();
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KalmanFilter(const std::string &basename, size_t n_endo, size_t n_exo, const std::vector<size_t> &zeta_fwrd_arg,
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const std::vector<size_t> &zeta_back_arg, const std::vector<size_t> &zeta_mixed_arg, const std::vector<size_t> &zeta_static_arg,
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double qz_criterium_arg, const std::vector<size_t> &varobs_arg,
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double riccati_tol_arg, double lyapunov_tol_arg,
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bool noconstant_arg);
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template <class Vec1, class Vec2, class Mat1>
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double
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compute(const MatrixConstView &dataView, Vec1 &steadyState,
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const Mat1 &Q, const Matrix &H, const Vec2 &deepParams,
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VectorView &vll, MatrixView &detrendedDataView, size_t start, size_t period)
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{
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if (period == 0) // initialise all KF matrices
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initKalmanFilter.initialize(steadyState, deepParams, R, Q, RQRt, T, Pstar, Pinf,
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dataView, detrendedDataView);
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else // initialise parameter dependent KF matrices only but not Ps
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initKalmanFilter.initialize(steadyState, deepParams, R, Q, RQRt, T,
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dataView, detrendedDataView);
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return filter(detrendedDataView, H, vll, start);
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}
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private:
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const std::vector<size_t> zeta_varobs_back_mixed;
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static std::vector<size_t> compute_zeta_varobs_back_mixed(const std::vector<size_t> &zeta_back_arg, const std::vector<size_t> &zeta_mixed_arg, const std::vector<size_t> &varobs_arg);
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Matrix Z, Zt; //nob*mm matrix mapping endogeneous variables and observations and its transpose
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Matrix T; //mm*mm transition matrix of the state equation.
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Matrix R; //mm*rr matrix, mapping structural innovations to state variables.
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Matrix Pstar; //mm*mm variance-covariance matrix of stationary variables
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Matrix Pinf; //mm*mm variance-covariance matrix of diffuse variables
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// allocate space for intermediary matrices
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Matrix RQRt, Ptmp; //mm*mm variance-covariance matrix of variable disturbances
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Matrix F, Finv; // nob*nob F=ZPZt +H an inv(F)
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Matrix K, KFinv, oldKFinv; // mm*nobs K=PZt and K*Finv gain matrices
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Vector a_init, a_new; // state vector
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Vector vt; // current observation error vectors
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Vector vtFinv; // intermediate observation error *Finv vector
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double riccati_tol;
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InitializeKalmanFilter initKalmanFilter; //Initialise KF matrices
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Vector FUTP; // F upper triangle packed as vector FUTP(i + (j-1)*j/2) = F(i,j) for 1<=i<=j;
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// Method
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double filter(const MatrixView &detrendedDataView, const Matrix &H, VectorView &vll, size_t start);
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};
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#endif // !defined(213B0417_532B_4027_9EDF_36C004CB4CD1__INCLUDED_)
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