Weak convergence of the sequential empirical processes of residuals in nonstationary autoregressive models
成果类型：
Article
署名作者：
Ling, SQ
署名单位：
University of Hong Kong; University of Western Australia
刊物名称：
ANNALS OF STATISTICS
ISSN/ISSBN：
0090-5364
发表日期：
1998
页码：
741-754
关键词：
摘要：
This paper establishes the weak convergence of the sequential empirical process (K) over bar(n) of the estimated residuals in nonstationary autoregressive models. Under some regular conditions, it is shown that (K) over bar(n) converges weakly to a Kiefer process when the characteristic polynomial does not include the unit root 1; otherwise (K) over bar(n) converges weakly to a Kiefer process plus a functional of stochastic integrals in terms of the standard Brownian motion. The latter differs not only from that given by Koul and Levental for an explosive AR(1) model but also from that given by Bai for a stationary ARMA model.