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

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  • Arsova, Antonia
  • Karaman Örsal, Deniz Dilan

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.

Suggested Citation

  • Arsova, Antonia & Karaman Örsal, Deniz Dilan, 2021. "A panel cointegrating rank test with structural breaks and cross-sectional dependence," Econometrics and Statistics, Elsevier, vol. 17(C), pages 107-129.
    • Handle: RePEc:eee:ecosta:v:17:y:2021:i:c:p:107-129
    DOI: 10.1016/j.ecosta.2020.05.002
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    More about this item

    Keywords

    Panel cointegrating rank test; Structural breaks; Cross-sectional dependence; Likelihood-ratio; Time trend;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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