Asymptotic properties of the maximum likelihood estimator in autoregressive models with Markov regime

• 成果类型: Article
• 署名作者: Douc, R; Moulines, T; Rydén, T
• 署名单位: IMT - Institut Mines-Telecom; IMT Atlantique; Lund University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/009053604000000021
• 发表日期: 2004
• 页码: 2254-2304
• 关键词: time-series; STATE-SPACE; CONVERGENCE; Consistency; ergodicity; volatility; algorithms; STABILITY; normality

摘要

An autoregressive process with Markov regime is an autoregressive process for which the regression function at each time point is given by a nonobservable Markov chain. In this paper we consider the asymptotic properties of the maximum likelihood estimator in a possibly nonstationary process of this kind for which the hidden state space is compact but not necessarily finite. Consistency and asymptotic normality are shown to follow from uniform exponential forgetting of the initial distribution for the hidden Markov chain conditional on the observations.