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

Konferenzbeitrag

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Beiträge zur Jahrestagung des Vereins für Socialpolitik 2016: Demographischer Wandel - Session: Time Series Econometrics ; No. D01-V3

Classification and Topics

• Classification: Wirtschaft ; Hypothesis Testing: General ; Statistical Simulation Methods: General ; Multiple or Simultaneous Equation Models: Panel Data Models; Spatio-temporal Models

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Karaman Örsal, Deniz Dilan ; Arsova, Antonia
• Event: Veröffentlichung
• (who): ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften, Leibniz-Informationszentrum Wirtschaft
• (where): Kiel und Hamburg
• (when): 2016

Further information

• Handle: http://hdl.handle.net/10419/145822
• Last update: 10.03.2025, 11:41 AM CET