Efficient Filtering in State-Space Representations

30 Pages Posted: 4 Feb 2009

See all articles by David N. DeJong

David N. DeJong

University of Pittsburgh - Department of Economics

Hariharan Dharmarajan

University of Pittsburgh

Roman Liesenfeld

University of Cologne, Department of Economics

Jean-Francois Richard

University of Pittsburgh - Department of Economics

Date Written: November 30, 2008

Abstract

We develop a numerical procedure that facilitates efficient filtering in applications involving non-linear and non-Gaussian state-space models. The procedure approximates necessary integrals using continuous approximations of target densities. Construction is achieved via efficient importance sampling, and approximating densities are adapted to fully incorporate current information. We illustrate our procedure for the bearings-only tracking problem.

Keywords: particle filter, adaption, efficient importance sampling, kernel density approximation

JEL Classification: C15, C22

Suggested Citation

Dejong, David N. and Dharmarajan, Hariharan and Liesenfeld, Roman and Richard, Jean-Francois, Efficient Filtering in State-Space Representations (November 30, 2008). Available at SSRN: https://ssrn.com/abstract=1337092 or http://dx.doi.org/10.2139/ssrn.1337092

David N. Dejong

University of Pittsburgh - Department of Economics ( email )

4A21 Forbes Quad
Pittsburgh, PA 15260
United States
(412) 648-2242 (Phone)

Hariharan Dharmarajan

University of Pittsburgh ( email )

135 N Bellefield Ave
Pittsburgh, PA 15260
United States

Roman Liesenfeld (Contact Author)

University of Cologne, Department of Economics ( email )

Albertus-Magnus-Platz
D-50931 Köln
Germany

Jean-Francois Richard

University of Pittsburgh - Department of Economics ( email )

4901 Wesley Posvar Hall
230 South Bouquet Street
Pittsburgh, PA 15260
United States
412-648-1750 (Phone)

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