Diagnostic tests for non-causal time series with infinite variance

被引：3

作者：

Cui, Yunwei ^{[1
]}

Fisher, Thomas J. ^{[2
]}

Wu, Rongning ^{[3
]}

机构：

[1] Univ Houston Downtown, Dept Math & Stat, Houston, TX 77002 USA

[2] Miami Univ, Dept Stat, Oxford, OH 45056 USA

[3] CUNY Bernard M Baruch Coll, Dept Stat & Comp Informat Syst, New York, NY 10010 USA

来源：

JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 2014年 / 147卷

关键词：

AR process; Portmanteau Test; alpha-stable distributions; MAXIMUM-LIKELIHOOD-ESTIMATION; ABSOLUTE DEVIATION ESTIMATION; GOODNESS-OF-FIT; MODEL;

D O I：

10.1016/j.jspi.2013.10.010

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：

Goodness-of-fit testing for non-causal autoregressive time series with non-Gaussian stable noise is studied. To model time series exhibiting sharp spikes or occasional bursts of outlying observations, the exponent of the stable errors is assumed to be less than two. Under such a condition, the innovation variables have no finite second moment. We prove that the sample autocorrelation functions of the trimmed residuals are asymptotically normal. Nonparametric tests are also investigated. An assortment of test statistics is suggested for model assessment. (C) 2013 Elsevier B.V. All rights reserved.

引用

页码：117 / 131

页数：15

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