Multiple change‐point models for time series

I.B. MacNeill, V.K. Jandhyala, A. Kaul and S.B. Fotopoulos

Environmetrics, 2020, vol. 31, issue 1

Abstract: The “Bayes‐type” method of deriving change‐point test statistics was introduced by Chernoff and Zacks (1964). Other authors subsequently adapted this approach and derived Bayes‐type statistics for at most one change (AMOC), and for multiple change points, under a variety of model formulations. Asymptotic distribution theory has always been limited to the AMOC statistics because of the perceived complexity of multiple change‐point statistics. In this article, it is shown that the Bayes‐type statistic derived to test for multiple change points is directly proportional to the AMOC statistic. This result immediately provides distributional results for Bayes‐type multiple change‐point statistics. In addition, it fundamentally alters the current understanding of the AMOC statistic. It follows from this result that the Bayes‐type statistic derived under AMOC conditions in fact tests for at least one change (ALOC), even though the statistic is derived under AMOC formulation. Under asymptotic consideration, the result also extends to the case of model errors following a stationary process. As an example, the classical Nile River data are revisited and analyzed for the presence of multiple change points.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1002/env.2593

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:envmet:v:31:y:2020:i:1:n:e2593

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=1180-4009

Access Statistics for this article

More articles in Environmetrics from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().