A panel cointegrating rank test with structural breaks and cross-sectional dependence

Antonia Arsova and Deniz Karaman Örsal

Econometrics and Statistics, 2021, vol. 17, issue C, 107-129

Abstract: A new panel cointegrating rank test which allows for a linear time trend with breaks and cross-sectional dependence is proposed. The new correlation-augmented inverse normal (CAIN) test is based on a modification of the inverse normal method and combines the p-values of individual likelihood-ratio trace statistics by assuming that the number of breaks and break points are known. A Monte Carlo study demonstrates its robustness to cross-sectional dependence and its superior size and power properties compared to other meta-analytic tests used in practice. The test is applied to investigate the long-run relationship between regional house prices and personal income in the United States in view of the structural break introduced by the Global Financial Crisis.

Keywords: Panel cointegrating rank test; Structural breaks; Cross-sectional dependence; Likelihood-ratio; Time trend (search for similar items in EconPapers)
JEL-codes: C12 C15 C33 (search for similar items in EconPapers)
Date: 2021
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/S2452306220300484
Full text for ScienceDirect subscribers only. Contains open access articles

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:ecosta:v:17:y:2021:i:c:p:107-129

DOI: 10.1016/j.ecosta.2020.05.002

Access Statistics for this article

Econometrics and Statistics is currently edited by E.J. Kontoghiorghes, H. Van Dijk and A.M. Colubi

More articles in Econometrics and Statistics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().