Model selection for integrated autoregressive processes of infinite order

被引：16
作者：
Ing, Ching-Kang [1 ]
Sin, Chor-yiu [2 ]
Yu, Shu-Hui [3 ]
机构：
[1] Acad Sinica, Inst Stat Sci, Taipei, Taiwan
[2] Natl Tsing Hua Univ, Dept Econ, Hsinchu, Taiwan
[3] Natl Univ Kaohsiung, Inst Stat, Kaohsiung, Taiwan
来源：
JOURNAL OF MULTIVARIATE ANALYSIS | 2012年 / 106卷
关键词：
Asymptotic efficiency; Integrated AR(infinity) processes; Model selection; Mean squared prediction error; TIME-SERIES; UNIT-ROOT; PREDICTION; INFORMATION; REGRESSION;
D O I：
10.1016/j.jmva.2011.10.008
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：
We show that Akaike's Information Criterion (AIC) and its variants are asymptotically efficient in integrated autoregressive processes of infinite order (AR(infinity)). This result. together with its stationary counterpart established previously in the literature, ensures that AIC can ultimately achieve prediction efficiency in an AR(infinity) process, without knowing the integration order. (C) 2011 Elsevier Inc. All rights reserved.
引用
收藏
页码：57 / 71
页数：15
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