Weak convergence of the sequential empirical processes of residuals in nonstationary autoregressive models

被引:0
|
作者
Ling, SQ
机构
[1] Univ Hong Kong, Dept Stat, Hong Kong, Hong Kong
[2] Univ Western Australia, Dept Econ, Perth, WA 6009, Australia
来源
ANNALS OF STATISTICS | 1998年 / 26卷 / 02期
关键词
Brownian motions; Kiefer process; sequential empirical processes; nonstationary autoregressive model; weak convergence;
D O I
暂无
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
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.
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页码:741 / 754
页数:14
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