Observation-driven models for Poisson counts

• 成果类型: Article
• 署名作者: Davis, RA; Dunsmuir, WTM; Streett, SB
• 署名单位: Colorado State University System; Colorado State University Fort Collins; University of New South Wales Sydney; National Center Atmospheric Research (NCAR) - USA
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/90.4.777
• 发表日期: 2003
• 页码: 777790
• 关键词: time-series; regression

摘要

This paper is concerned with a general class of observation-driven models for time series of counts whose conditional distributions given past observations and explanatory variables follow a Poisson distribution. These models provide a flexible framework for modelling a wide range of dependence structures. Conditions for stationarity and ergodicity of these processes are established from which the large-sample properties of the maximum likelihood estimators can be derived. Simulations are provided to give additional insight into the finite-sample behaviour of the estimators. Finally an application to a regression model for daily counts of asthma presentations at a Sydney hospital is described.