An Efficient Filtering Approach to Likelihood Approximation for State-Space Representations

We develop a numerical filtering procedure that facilitates efficient likelihood evaluation in applications involving non-linear and non-gaussian state-space models. The procedure approximates necessary integrals using continuous or piecewise-continuous approximations of target densities. Construction is achieved via efficient importance sampling, and approximating densities are adapted to fully incorporate current information.

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Year of publication:	2007
Authors:	DeJong, David Neil ; Dharmarajan, Hariharan ; Liesenfeld, Roman ; Richard, Jean-François
Publisher:	Kiel : Kiel University, Department of Economics
Subject:	Zustandsraummodell \| Theorie \| EIS Filter \| Efficient Importance Sampling \| particle filter \| adaption \| efficient importance sampling \| kernel density approximation

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Series:	Economics Working Paper ; 2007-25
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Working Paper
Language:	English
Other identifiers:	546256120 [GVK] hdl:10419/22041 [Handle] RePEc:zbw:cauewp:6339 [RePEc]
Source:	EconStor

Persistent link: https://www.econbiz.de/10010296289