A panel cointegration rank test with structural breaks and cross-sectional dependence
Deniz Karaman Örsal () and
VfS Annual Conference 2016 (Augsburg): Demographic Change from Verein für Socialpolitik / German Economic Association
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.
JEL-codes: C12 C15 C33 (search for similar items in EconPapers)
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Journal Article: A panel cointegrating rank test with structural breaks and cross-sectional dependence (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:vfsc16:145822
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