Monitoring procedures for strict stationarity based on the multivariate characteristic function

被引：3

作者：

Lee, Sangyeol ^{[1
]}

Meintanis, Simos G. ^{[2
,3
]}

Pretorius, Charl ^{[3
,4
]}

机构：

[1] Seoul Natl Univ, Dept Stat, Seoul, South Korea

[2] Natl & Kapodistrian Univ Athens, Dept Econ, Athens, Greece

[3] North West Univ, Pure & Appl Analyt, Potchefstroom, South Africa

[4] Charles Univ Prague, Dept Probabil & Math Stat, Prague, Czech Republic

来源：

JOURNAL OF MULTIVARIATE ANALYSIS | 2022年 / 189卷

基金：

新加坡国家研究基金会;

关键词：

Block bootstrap; Change-point; Empirical characteristic function; Strict stationarity; CHANGE-POINT ANALYSIS; TIME-SERIES; 2ND-ORDER STATIONARITY; PARTIAL-SUMS; ROBUST; TESTS; COVARIANCE; VARIANCE;

D O I：

10.1016/j.jmva.2021.104892

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 consider model-free monitoring procedures for strict stationarity of a given time series. The new criteria are formulated as L2-type statistics incorporating the multivariate empirical characteristic function. Asymptotic results are obtained for the closed-end scenario and Monte Carlo results are presented. The new methods are also employed in order to test for possible stationarity breaks in time-series data from the financial sector. (C) 2021 Elsevier Inc. All rights reserved.

引用

页数：20

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