A General Regression Changepoint Test for Time Series Data

Michael W. Robbins, Colin M. Gallagher and Robert B. Lund

Journal of the American Statistical Association, 2016, vol. 111, issue 514, 670-683

Abstract: This article develops a test for a single changepoint in a general setting that allows for correlated time series regression errors, a seasonal cycle, time-varying regression factors, and covariate information. Within, a changepoint statistic is constructed from likelihood ratio principles and its asymptotic distribution is derived. The asymptotic distribution of the changepoint statistic is shown to be invariant of the seasonal cycle and the covariates should the latter obey some simple limit laws; however, the limit distribution depends on any time-varying factors. A new test based on ARMA residuals is developed and is shown to have favorable properties with finite samples. Driving our work is a changepoint analysis of the Mauna Loa record of monthly carbon dioxide concentrations. This series has a pronounced seasonal cycle, a nonlinear trend, heavily correlated regression errors, and covariate information in the form of climate oscillations. In the end, we find a prominent changepoint in the early 1990s, often attributed to the eruption of Mount Pinatubo, which cannot be explained by covariates. Supplementary materials for this article are available online.

Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://hdl.handle.net/10.1080/01621459.2015.1029130 (text/html)
Access to full text is restricted to subscribers.

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:taf:jnlasa:v:111:y:2016:i:514:p:670-683

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UASA20

DOI: 10.1080/01621459.2015.1029130

Access Statistics for this article

Journal of the American Statistical Association is currently edited by Xuming He, Jun Liu, Joseph Ibrahim and Alyson Wilson

More articles in Journal of the American Statistical Association from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().