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

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This paper proposes a new likelihood-based panel cointegration rank test which allows for a linear time trend with heterogeneous breaks and cross sectional dependence. It is based on a novel modification of the inverse normal method which combines the p-values of the individual likelihood-ratio trace statistics of Trenkler et al. (2007). We call this new test a correlation augmented inverse normal (CAIN) test. It infers the unknown correlation between the probits of the individual p-values from an estimate of the average absolute correlation between the VAR processes' innovations, which is readily observable in practice. A Monte Carlo study demonstrates that this simple test is robust to various degrees of cross-sectional dependence generated by common factors. It has better size and power properties than other meta-analytic tests in panels with dimensions typically encountered in macroeconometric analysis.
Original languageEnglish
Title of host publicationJahrestagung des Vereins für Socialpolitik 2016: Demographischer Wandel : Session: Time Series Econometrics, No. D01-V3
Number of pages27
PublisherZBW - Leibniz Informationszentrum Wirtschaft
Publication date08.2016
Publication statusPublished - 08.2016
EventJahrestagung des Vereins für Socialpolitik - VfS 2016
: Demographischer Wandel
- Augsburg, Germany
Duration: 04.09.201607.09.2016
https://www.socialpolitik.de/De/jahrestagung-2016
https://www.socialpolitik.de/De/jahrestagung-2016

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  1. Kirsa Weidemann

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