Commit fcb315dc authored by Sébastien Villemot's avatar Sébastien Villemot
Browse files

Merge remote branch 'george/master'

parents aef46cf7 f0fc974b
......@@ -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);
};
......
/*
* 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){
}
*/
/*
* 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;
......
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