Testing for change points in time series models and limiting theorems for NED sequences

By S. Ling. 2007

68 citations recorded by the source corpus.

This paper first establishes a strong law of large numbers and a strong invariance principle for forward and backward sums of near-epoch dependent sequences. Using these limiting theorems, we develop a general asymptotic theory on the Wald test for change points in a general class of time series models under the no change-point hypothesis. As an application, we verify our assumptions for the long-memory fractional ARIMA model.

Subfields: Finance, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/42e280247e42ca1141d202848f9de6ec2fd370a5

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In Finance

In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

A MOSUM procedure for the estimation of multiple random change points (2018, background): “mixing time series (Theorem 4 in [27]) or near-epoch dependent time series ([29]).”
Non‐Parametric Change‐Point Tests for Long‐Range Dependent Data (2013, background): “For example, Ling (2007) studies the asymptotic distribution of the Wald test for parameter changes in near-epoch-dependent processes.”
Exploring the ability of the Pettitt method for detecting change point by Monte Carlo simulation (2014, background): “In statistical literatures, the change point problems have been much investigated and studied (Giraitis et al. 1996; Berkes et al. 2006; Ling 2007; Antoch et al. 2008; Wang 2008; Spokoiny 2009; Zhao et al. 2010; Yamaguchi 2011; Kim and Lee 2011; Dehling et al. 2012; Hušková and Kirch 2012; Tan et…”