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

2024/01/18 by Schnatter, Sylvia

paper · doi:10.57938/74c746e8-9212-4041-82a3-59f1289c50e6

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)

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