On infimum Dickey–Fuller unit root tests allowing for a trend break under the null

David Harvey, Stephen Leybourne () and Robert Taylor

Computational Statistics & Data Analysis, 2014, vol. 78, issue C, 235-242

Abstract: Trend breaks appear to be prevalent in macroeconomic time series. Consequently, to avoid the catastrophic impact that unmodelled trend breaks have on power, it is standard empirical practice to employ unit root tests which allow for such effects. A popularly applied approach is the infimum ADF-type test. Its appeal has endured with practitioners despite results which show that the infimum ADF statistic diverges to −∞ as the sample size diverges, with the consequence that the test has an asymptotic size of unity when a break in trend is present under the unit root null hypothesis. The result for additive outlier-type breaks in trend (but not intercept) is refined and shows that divergence to −∞ occurs only when the true break fraction is smaller than 2/3. An alternative testing strategy based on the maximum of the original infimum statistic and the corresponding statistic constructed using the time-reversed sample data is considered.

Keywords: Unit root test; Trend break; Minimum Dickey–Fuller test (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947312003854
Full text for ScienceDirect subscribers only.

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:eee:csdana:v:78:y:2014:i:c:p:235-242

DOI: 10.1016/j.csda.2012.10.017

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().