Adaptive Test for Periodicity in Autoregressive Conditional Heteroskedastic Processes

被引：4

作者：

Bentarzi, M. ^{[2
]}

Merzougui, M. ^{[1
]}

机构：

[1] Univ MHamed Bouguera, Boumerdes, Algeria

[2] Univ Sci & Technol, Algiers, Algeria

来源：

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | 2010年 / 39卷 / 09期

关键词：

Adaptive test; Local asymptotic "most stringent" test; Local asymptotic normality; Periodic ARCH; Swensen's conditions; ASYMPTOTICALLY OPTIMAL TESTS; GARCH; MODELS;

D O I：

10.1080/03610918.2010.512694

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 article is concerned with the periodicity testing problem in Autoregressive Conditional Heteroskedastic (ARCH) process. Adaptive locally asymptotically optimal test is derived, when the innovation density is unspecified but symmetric satisfying only some general technical assumptions, for the null hypothesis of classical ARCH process against an alternative of periodically correlated ARCH dependence. The main technical tool is LeCam's (1960) Local Asymptotic Normality (LAN) property. The LAN property of the central sequence is shown via the adapted sufficient Swensen's conditions (1985). The performance of the established test is shown via simulation studies.

引用

页码：1735 / 1753

页数：19

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