Merge branch 'master' of ssh://kirikou.dynare.org/srv/d_kirikou/git/dynare
Michel Juillard
commit
f07c807702
|
|
@ -31,7 +31,9 @@ DetrendData::DetrendData(const bool INlogLinear) //, Vector& INtrendCoeff)
|
|||
};
|
||||
|
||||
void
|
||||
DetrendData::detrend(const Vector &SteadyState, const MatrixView &dataView, Matrix &Yiew)
|
||||
DetrendData::detrend(const Vector &SteadyState, const MatrixView &dataView, Matrix &Y)
|
||||
{
|
||||
// dummy
|
||||
Y=dataView;
|
||||
};
|
||||
|
||||
|
|
|
|||
|
|
@ -33,7 +33,7 @@ class DetrendData
|
|||
public:
|
||||
virtual ~DetrendData(){};
|
||||
DetrendData(const bool logLinear); // add later Vector& trendCoeff);
|
||||
void detrend(const Vector &SteadyState, const MatrixView &dataView, Matrix &Yiew);
|
||||
void detrend(const Vector &SteadyState, const MatrixView &dataView, Matrix &Y);
|
||||
|
||||
private:
|
||||
const bool logLinear;
|
||||
|
|
|
|||
|
|
@ -41,7 +41,7 @@ InitializeKalmanFilter::InitializeKalmanFilter(const std::string &modName, size_
|
|||
ghx(n_endo_arg, zeta_back_arg.size() + zeta_mixed_arg.size()),
|
||||
ghx_raw(n_endo_arg, zeta_back_arg.size() + zeta_mixed_arg.size()),
|
||||
ghu(n_endo_arg, n_exo_arg), ghu_raw(n_endo_arg, n_exo_arg),
|
||||
Rt(n_exo_arg, riv.size()), RQ(riv.size(), n_exo_arg), RQR(riv.size(), riv.size())
|
||||
Rt(n_exo_arg, riv.size()), RQ(riv.size(), n_exo_arg)
|
||||
{
|
||||
size_t n_pred = ghx.getCols();
|
||||
size_t n_static = zeta_static_arg.size();
|
||||
|
|
@ -52,18 +52,19 @@ InitializeKalmanFilter::InitializeKalmanFilter(const std::string &modName, size_
|
|||
}
|
||||
|
||||
void
|
||||
InitializeKalmanFilter::initialize(Vector &steadyState, Vector &deepParams, const Vector &xparams1,
|
||||
Matrix &R, Matrix &Z, Matrix &Q, Matrix &T, Matrix &Pstar, Matrix &Pinf,
|
||||
double &penalty, const MatrixView &dataView, MatrixView &yView, int &info)
|
||||
InitializeKalmanFilter::initialize(Vector &steadyState, const Vector &deepParams, Matrix &R, const Matrix &Z,
|
||||
const Matrix &Q, Matrix &RQRt, Matrix &T, Matrix &Pstar, Matrix &Pinf,
|
||||
double &penalty, const MatrixView &dataView, Matrix &Y, int &info)
|
||||
{
|
||||
modelSolution.compute(steadyState, deepParams, ghx_raw, ghu_raw);
|
||||
detrendData.detrend(steadyState, dataView, Y);
|
||||
|
||||
mat::reorderRowsByVectors(ghx, mat::nullVec, ghx_raw, order_var);
|
||||
mat::reorderRowsByVectors(ghu, mat::nullVec, ghu_raw, order_var);
|
||||
|
||||
setT(T, info);
|
||||
setR(R, info);
|
||||
setPstar(Pstar, Pinf, T, R, Q, info);
|
||||
setPstar(Pstar, Pinf, T, R, Q, RQRt, info);
|
||||
}
|
||||
|
||||
void
|
||||
|
|
@ -86,21 +87,21 @@ InitializeKalmanFilter::setR(Matrix &R, int &info)
|
|||
}
|
||||
|
||||
void
|
||||
InitializeKalmanFilter::setPstar(Matrix &Pstar, Matrix &Pinf, Matrix &T, Matrix &R, Matrix &Q, int &info)
|
||||
InitializeKalmanFilter::setPstar(Matrix &Pstar, Matrix &Pinf, Matrix &T, Matrix &R, const Matrix &Q, Matrix &RQRt, int &info)
|
||||
{
|
||||
|
||||
// Matrix RQR=R*Q*R'
|
||||
// Matrix RQRt=R*Q*R'
|
||||
RQ.setAll(0.0);
|
||||
blas::gemm("N", "N", 1.0, R, Q, 0.0, RQ); // R*Q
|
||||
RQR.setAll(0.0);
|
||||
RQRt.setAll(0.0);
|
||||
//mat::transpose(Rt, R);
|
||||
blas::gemm("N", "T", 1.0, RQ, R, 0.0, RQR); // R*Q*R'
|
||||
blas::gemm("N", "T", 1.0, RQ, R, 0.0, RQRt); // R*Q*R'
|
||||
//mat::transpose(RQR);
|
||||
|
||||
try
|
||||
{
|
||||
// disclyap_fast(T, RQR, Pstar, lyapunov_tol, 0); // 1 to check chol
|
||||
discLyapFast.solve_lyap(T, RQR, Pstar, lyapunov_tol, 0); // 1 to check chol
|
||||
// disclyap_fast(T, RQR, Pstar, lyapunov_tol, 0 or 1 to check chol)
|
||||
discLyapFast.solve_lyap(T, RQRt, Pstar, lyapunov_tol, 0);
|
||||
|
||||
Pinf.setAll(0.0);
|
||||
}
|
||||
|
|
|
|||
|
|
@ -45,8 +45,8 @@ public:
|
|||
const Matrix &llincidence, double qz_criterium, const std::vector<size_t> &order_var_arg, const std::vector<size_t> &inv_order_var_arg,
|
||||
const std::vector<size_t> &riv, const std::vector<size_t> &ric, double lyapunov_tol, int &info);
|
||||
virtual ~InitializeKalmanFilter();
|
||||
void initialize(Vector &steadyState, Vector &deepParams, const Vector &xparams1, Matrix &R, Matrix &Z, Matrix &Q,
|
||||
Matrix &T, Matrix &Pstar, Matrix &Pinf, double &penalty, const MatrixView &dataView, MatrixView &yView, int &info);
|
||||
void initialize(Vector &steadyState, const Vector &deepParams, Matrix &R, const Matrix &Z, const Matrix &Q, Matrix &RQRt,
|
||||
Matrix &T, Matrix &Pstar, Matrix &Pinf, double &penalty, const MatrixView &dataView, Matrix &Y, int &info);
|
||||
|
||||
private:
|
||||
const std::vector<size_t> riv; // restrict_var_list
|
||||
|
|
@ -59,10 +59,10 @@ private:
|
|||
DiscLyapFast discLyapFast; //Lyapunov solver
|
||||
Matrix ghx, ghx_raw;
|
||||
Matrix ghu, ghu_raw;
|
||||
Matrix Rt, RQ, RQR;
|
||||
Matrix Rt, RQ;
|
||||
void setT(Matrix &T, int &info);
|
||||
void setR(Matrix &R, int &info);
|
||||
void setPstar(Matrix &Pstar, Matrix &Pinf, Matrix &T, Matrix &R, Matrix &Q, int &info);
|
||||
void setPstar(Matrix &Pstar, Matrix &Pinf, Matrix &T, Matrix &R, const Matrix &Q, Matrix &RQRt, int &info);
|
||||
|
||||
};
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,154 @@
|
|||
/*
|
||||
* Copyright (C) 2009-2010 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/>.
|
||||
*/
|
||||
|
||||
///////////////////////////////////////////////////////////
|
||||
// KalmanFilter.cpp
|
||||
// Implementation of the Class KalmanFilter
|
||||
// Created on: 02-Feb-2010 12:44:41
|
||||
///////////////////////////////////////////////////////////
|
||||
|
||||
#include "KalmanFilter.hh"
|
||||
|
||||
KalmanFilter::~KalmanFilter()
|
||||
{
|
||||
|
||||
}
|
||||
|
||||
KalmanFilter::KalmanFilter(const std::string &modName, size_t n_endo, size_t n_exo,
|
||||
const std::vector<size_t> &zeta_fwrd_arg, const std::vector<size_t> &zeta_back_arg,
|
||||
const std::vector<size_t> &zeta_mixed_arg, const std::vector<size_t> &zeta_static_arg,
|
||||
const Matrix &ll_incidence, double qz_criterium, const std::vector<size_t> &order_var,
|
||||
const std::vector<size_t> &inv_order_var, const std::vector<size_t> &varobs_arg,
|
||||
const std::vector<size_t> &riv, const std::vector<size_t> &ric,
|
||||
double riccati_tol_in, double lyapunov_tol, int &info) :
|
||||
Z(varobs_arg.size(), riv.size()), T(riv.size()), R(riv.size(), n_exo),
|
||||
Pstar(riv.size(), riv.size()), Pinf(riv.size(), riv.size()), RQRt(riv.size(), riv.size()),
|
||||
Pold(riv.size(), riv.size()), Ptmp(riv.size(), riv.size()), F(varobs_arg.size(), varobs_arg.size()),
|
||||
Finv(varobs_arg.size(), varobs_arg.size()), K(riv.size(), varobs_arg.size()),
|
||||
KFinv(riv.size(), varobs_arg.size()), Finverter(varobs_arg.size()), a_init(riv.size(), 1),
|
||||
a_new(riv.size(), 1), vt(varobs_arg.size(), 1), vtFinv(1, varobs_arg.size()), vtFinvVt(1), riccati_tol(riccati_tol_in),
|
||||
initKalmanFilter(modName, n_endo, n_exo, zeta_fwrd_arg, zeta_back_arg, zeta_mixed_arg,
|
||||
zeta_static_arg, ll_incidence, qz_criterium, order_var, inv_order_var, riv, ric, lyapunov_tol, info)
|
||||
{
|
||||
a_init.setAll(0.0);
|
||||
Z.setAll(0.0);
|
||||
for (size_t i = 0; i < varobs_arg.size(); ++i)
|
||||
Z(i, varobs_arg[i]) = 1.0;
|
||||
|
||||
}
|
||||
|
||||
double
|
||||
KalmanFilter::compute(const MatrixView &dataView, Vector &steadyState,
|
||||
const Matrix &Q, const Matrix &H, const Vector &deepParams,
|
||||
Vector &vll, size_t start, double &penalty, int &info)
|
||||
{
|
||||
double ll;
|
||||
Matrix Y(dataView.getRows(), dataView.getCols()); // data
|
||||
|
||||
initKalmanFilter.initialize(steadyState, deepParams, R, Z, Q, RQRt, T, Pstar, Pinf,
|
||||
penalty, dataView, Y, info);
|
||||
|
||||
return ll = filter(Y, H, vll, start, info);
|
||||
|
||||
};
|
||||
|
||||
/**
|
||||
* 30:*
|
||||
*/
|
||||
double
|
||||
KalmanFilter::filter(const Matrix &dataView, const Matrix &H, Vector &vll, size_t start, int &info)
|
||||
{
|
||||
double loglik, ll, logFdet;
|
||||
int p = Finv.getRows();
|
||||
|
||||
bool nonstationary = true;
|
||||
for (size_t t = 0; t < dataView.getCols(); ++t)
|
||||
{
|
||||
if (nonstationary)
|
||||
{
|
||||
// K=PZ'
|
||||
K.setAll(0.0);
|
||||
blas::gemm("N", "T", 1.0, Pstar, Z, 1.0, K);
|
||||
|
||||
//F=ZPZ' +H = ZK+H
|
||||
F = H;
|
||||
blas::gemm("N", "N", 1.0, Z, K, 1.0, F);
|
||||
// logFdet=log|F|
|
||||
|
||||
// Finv=inv(F)
|
||||
mat::set_identity(Finv);
|
||||
Finverter.invMult("N", F, Finv); // F now contains its LU decomposition!
|
||||
// KFinv
|
||||
KFinv.setAll(0.0);
|
||||
blas::gemm("N", "N", 1.0, K, Finv, 1.0, KFinv);
|
||||
// deteminant of F:
|
||||
logFdet = 1;
|
||||
for (int d = 0; d < p; ++d)
|
||||
logFdet *= (-F(d, d));
|
||||
Pold = Pstar;
|
||||
Ptmp = Pstar;
|
||||
// Pt+1= T(Pt - KFinvK')T' +RQR'
|
||||
// 1) Ptmp= Pt - K*FinvK'
|
||||
blas::gemm("N", "T", -1.0, KFinv, K, 1.0, Ptmp);
|
||||
// 2) Ptmp= T*Ptmp
|
||||
Pstar = Ptmp;
|
||||
Ptmp.setAll(0.0);
|
||||
blas::gemm("N", "N", 1.0, T, Pstar, 1.0, Ptmp);
|
||||
// 3) Pt+1= Ptmp*T' +RQR'
|
||||
Pstar = RQRt;
|
||||
blas::gemm("N", "T", 1.0, Ptmp, T, 1.0, Pstar);
|
||||
nonstationary = mat::isDiffSym(Pstar, Pold, riccati_tol);
|
||||
}
|
||||
else
|
||||
{
|
||||
}
|
||||
|
||||
// err= Yt - Za
|
||||
//VectorView yt(dataView.getData()+t*dataView.getRows(),dataView.getRows(),1); // current observation vector
|
||||
MatrixConstView yt(dataView, 0, t, dataView.getRows(), 1); // current observation vector
|
||||
vt = yt;
|
||||
blas::gemm("N", "N", -1.0, Z, a_init, 1.0, vt);
|
||||
// at+1= T(at+ KFinv *err)
|
||||
blas::gemm("N", "N", 1.0, KFinv, vt, 1.0, a_init);
|
||||
blas::gemm("N", "N", 1.0, T, a_init, 0.0, a_new);
|
||||
a_init = a_new;
|
||||
|
||||
/*****************
|
||||
Here we calc likelihood and store results.
|
||||
*****************/
|
||||
blas::gemm("T", "N", 1.0, vt, Finv, 0.0, vtFinv);
|
||||
blas::gemm("N", "N", 1.0, vtFinv, vt, 0.0, vtFinvVt);
|
||||
|
||||
ll = -0.5*(p*log(2*M_PI)+logFdet+*(vtFinvVt.getData()));
|
||||
|
||||
vll(t) = ll;
|
||||
if (t > start) loglik += ll;
|
||||
|
||||
}
|
||||
|
||||
return loglik;
|
||||
}
|
||||
|
||||
/**
|
||||
* 89:*
|
||||
double KalmanFilter::calcStepLogLik(const PLUFact &Finv, const Vector &v){
|
||||
|
||||
}
|
||||
*/
|
||||
|
||||
|
|
@ -0,0 +1,84 @@
|
|||
/*
|
||||
* Copyright (C) 2009-2010 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/>.
|
||||
*/
|
||||
|
||||
///////////////////////////////////////////////////////////
|
||||
// KalmanFilter.h
|
||||
// Implementation of the Class KalmanFilter
|
||||
// Created on: 02-Feb-2010 12:44:41
|
||||
///////////////////////////////////////////////////////////
|
||||
|
||||
#if !defined(KF_213B0417_532B_4027_9EDF_36C004CB4CD1__INCLUDED_)
|
||||
#define KF_213B0417_532B_4027_9EDF_36C004CB4CD1__INCLUDED_
|
||||
|
||||
#include "InitializeKalmanFilter.hh"
|
||||
|
||||
/**
|
||||
* •vanilla Kalman filter without constant and with measurement error (use scalar
|
||||
* 0 when no measurement error).
|
||||
* If multivariate filter is faster, do as in Matlab: start with multivariate
|
||||
* filter and switch to univariate filter only in case of singularity
|
||||
*
|
||||
* mamber functions: compute() and filter()
|
||||
* OUTPUT
|
||||
* LIK: likelihood
|
||||
*
|
||||
* REFERENCES
|
||||
* See "Filtering and Smoothing of State Vector for Diffuse State Space
|
||||
* Models", S.J. Koopman and J. Durbin (2003, in Journal of Time Series
|
||||
* Analysis, vol. 24(1), pp. 85-98).
|
||||
*/
|
||||
|
||||
class KalmanFilter {
|
||||
|
||||
public:
|
||||
virtual ~KalmanFilter();
|
||||
KalmanFilter(const std::string &modName, size_t n_endo, size_t n_exo, const std::vector<size_t> &zeta_fwrd_arg,
|
||||
const std::vector<size_t> &zeta_back_arg, const std::vector<size_t> &zeta_mixed_arg, const std::vector<size_t> &zeta_static_arg,
|
||||
const Matrix &llincidence, double qz_criterium, const std::vector<size_t> &order_var_arg, const std::vector<size_t> &inv_order_var,
|
||||
const std::vector<size_t> &varobs_arg, const std::vector<size_t> &riv, const std::vector<size_t> &ric,
|
||||
double riccati_tol, double lyapunov_tol, int &info);
|
||||
|
||||
double compute(const MatrixView &dataView, Vector &steadyState,
|
||||
const Matrix &Q, const Matrix &H, const Vector &deepParams,
|
||||
Vector &vll, size_t start, double &penalty, int &info);
|
||||
|
||||
protected:
|
||||
// static double calcStepLogLik(const PLUFact &Finv, const Vector &v);
|
||||
|
||||
private:
|
||||
Matrix Z; //nob*mm matrix mapping endogeneous variables and observations
|
||||
Matrix T; //mm*mm transition matrix of the state equation.
|
||||
Matrix R; //mm*rr matrix, mapping structural innovations to state variables.
|
||||
Matrix Pstar; //mm*mm variance-covariance matrix of stationary variables
|
||||
Matrix Pinf; //mm*mm variance-covariance matrix of diffuse variables
|
||||
// allocate space for intermediary matrices
|
||||
Matrix RQRt, Pold, Ptmp; //mm*mm variance-covariance matrix of variable disturbances
|
||||
Matrix F, Finv; // nob*nob F=ZPZt +H an inv(F)
|
||||
Matrix K, KFinv; // mm*nobs K=PZt and nm*nm K*Finv
|
||||
LUSolver Finverter; // matrix inversion algorithm
|
||||
Matrix a_init, a_new; // state vector
|
||||
Matrix vt; // current observation error vectors
|
||||
Matrix vtFinv, vtFinvVt; // intermeiate observation error *Finv vector
|
||||
double riccati_tol;
|
||||
InitializeKalmanFilter initKalmanFilter; //Initialise KF matrices
|
||||
double filter(const Matrix &dataView, const Matrix &H, Vector &vll, size_t start, int &info);
|
||||
|
||||
};
|
||||
|
||||
#endif // !defined(213B0417_532B_4027_9EDF_36C004CB4CD1__INCLUDED_)
|
||||
|
|
@ -136,7 +136,8 @@ main(int argc, char **argv)
|
|||
varobs.push_back(svarobs[i]-1);
|
||||
|
||||
Matrix
|
||||
T(riv.size(), riv.size()), R(riv.size(), n_exo), Pstar(riv.size(), riv.size()),
|
||||
T(riv.size(), riv.size()), R(riv.size(), n_exo),
|
||||
RQRt(riv.size(), riv.size()), Pstar(riv.size(), riv.size()),
|
||||
Pinf(riv.size(), riv.size()), Z(varobs.size(), riv.size()), Q(n_exo);
|
||||
Z.setAll(0.0);
|
||||
for (size_t i = 0; i < varobs.size(); ++i)
|
||||
|
|
@ -160,10 +161,10 @@ main(int argc, char **argv)
|
|||
|
||||
double lyapunov_tol = 1e-16;
|
||||
int info = 0;
|
||||
const MatrixView
|
||||
const MatrixView
|
||||
dataView(&lyapunov_tol, 1, 1, 1); // dummy
|
||||
MatrixView
|
||||
yView(&lyapunov_tol, 1, 1, 1); // dummy
|
||||
Matrix
|
||||
yView(dataView.getRows(),dataView.getCols()); // dummy
|
||||
const Vector
|
||||
xparams1(0); // dummy
|
||||
double penalty = 1e8;
|
||||
|
|
@ -176,11 +177,12 @@ main(int argc, char **argv)
|
|||
std::cout << "Initilise KF with Q: " << std::endl << Q << std::endl;
|
||||
std::cout << "and Z" << std::endl << Z << std::endl;
|
||||
|
||||
initializeKalmanFilter.initialize(steadyState, deepParams, xparams1, R, Z, Q, T, Pstar, Pinf,
|
||||
initializeKalmanFilter.initialize(steadyState, deepParams, R, Z, Q, RQRt, T, Pstar, Pinf,
|
||||
penalty, dataView, yView, info);
|
||||
|
||||
std::cout << "Matrix T: " << std::endl << T << std::endl;
|
||||
std::cout << "Matrix R: " << std::endl << R << std::endl;
|
||||
std::cout << "Matrix RQRt: " << std::endl << RQRt << std::endl;
|
||||
std::cout << "Matrix Pstar: " << std::endl << Pstar << std::endl;
|
||||
std::cout << "Matrix Pinf: " << std::endl << Pinf << std::endl;
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue