Integration-based Kalman-filtering for a Dynamic Generalized Linear Trend Model

Sylvia Schnatter

Publication: Working/Discussion PaperWU Working Paper

Abstract

The topic of the paper is filtering for non-Gaussian dynamic (state space) models by approximate computation of posterior moments using numerical integration. A Gauss-Hermite procedure is implemented based on the approximate posterior mode estimator and curvature recently proposed in 121. This integration-based filtering method will be illustrated by a dynamic trend model for non-Gaussian time series. Comparision of the proposed method with other approximations ([15], [2]) is carried out by simulation experiments for time series from Poisson, exponential and Gamma distributions. (author's abstract)
Original languageEnglish
Place of PublicationVienna
PublisherDepartment of Statistics and Mathematics, WU Vienna University of Economics and Business
Publication statusPublished - 1991

Publication series

NameForschungsberichte / Institut für Statistik
No.9

WU Working Paper Series

  • Forschungsberichte / Institut für Statistik

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