Testing for unit roots in short panels allowing for a structural break

Yiannis Karavias and Elias Tzavalis

Computational Statistics & Data Analysis, 2014, vol. 76, issue C, 391-407

Abstract: Panel data unit root tests which allow for a common structural break in the individual effects or linear trends of the AR(1) panel data model are suggested. These allow the date of the break to be unknown. The tests assume that the time-dimension of the panel (T) is fixed (finite) while the cross-section (N) is large. Under the null hypothesis of unit roots, they are similar to the initial conditions of the model and its individual effects. Extensions of the tests to the AR(2) model are provided. These highlight the difficulties in extending the tests to higher order serial correlation of the error terms. Monte Carlo experiments indicate that the small sample performance of the tests is very satisfactory. Application of the tests to the trade openness variable of the non-oil countries indicates that evidence of persistence of this variable can be attributed to trade liberalization policies adopted by many developing countries since the early nineties.

Keywords: Panel data models; Unit roots; Structural breaks; Sequential tests; Bootstrap; Trade openness (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (44)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947312003829
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:76:y:2014:i:c:p:391-407

DOI: 10.1016/j.csda.2012.10.014

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 ().