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Testing for Panel Cointegration Using Common Correlated Effects

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  • Anindya Banerjee
  • Josep Lluis Carrion-i-Silvestre

Abstract

Spurious regression analysis in panel data when time series are cross-section dependent is analyzed in the paper. We show that consistent estimation of the long-run average parameter is possible once we control for cross-section dependence using cross-section averages in the spirit of the common correlated effects approach in Pesaran (2006), Holly, Pesaran and Yamagata (2010) and Kapetanios, Pesaran and Yamagata (2011). This result is used to design a panel cointegration test statistic. The performance of the proposal is investigated in comparison with factor-based methods to control for dependence when both strong and weak cross-section dependence may be present.

Suggested Citation

  • Anindya Banerjee & Josep Lluis Carrion-i-Silvestre, 2011. "Testing for Panel Cointegration Using Common Correlated Effects," Discussion Papers 11-16, Department of Economics, University of Birmingham.
    • Handle: RePEc:bir:birmec:11-16
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    File URL: https://repec.cal.bham.ac.uk/pdf/11-16.pdf
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    References listed on IDEAS

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    1. Kapetanios, G. & Pesaran, M. Hashem & Yamagata, T., 2011. "Panels with non-stationary multifactor error structures," Journal of Econometrics, Elsevier, vol. 160(2), pages 326-348, February.
    2. Holly, Sean & Pesaran, M. Hashem & Yamagata, Takashi, 2010. "A spatio-temporal model of house prices in the USA," Journal of Econometrics, Elsevier, vol. 158(1), pages 160-173, September.
    3. M. Hashem Pesaran, 2006. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," Econometrica, Econometric Society, vol. 74(4), pages 967-1012, July.
    4. Peter Phillips & Hyungsik Moon, 2000. "Nonstationary panel data analysis: an overview of some recent developments," Econometric Reviews, Taylor & Francis Journals, vol. 19(3), pages 263-286.
    5. Phillips, P.C.B., 1986. "Understanding spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 33(3), pages 311-340, December.
    6. Georges Bresson & Badi H. Baltagi & Alain Pirotte, 2007. "Panel unit root tests and spatial dependence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(2), pages 339-360.
    7. Peter C. B. Phillips & Hyungsik R. Moon, 1999. "Linear Regression Limit Theory for Nonstationary Panel Data," Econometrica, Econometric Society, vol. 67(5), pages 1057-1112, September.
    8. Alexander Chudik & M. Hashem Pesaran & Elisa Tosetti, 2011. "Weak and strong cross‐section dependence and estimation of large panels," Econometrics Journal, Royal Economic Society, vol. 14(1), pages 45-90, February.
    9. Banerjee, Anindya & Carrion-i-Silvestre, Josep Lluís, 2006. "Cointegration in panel data with breaks and cross-section dependence," Working Paper Series 591, European Central Bank.
    10. Granger, C. W. J. & Newbold, P., 1974. "Spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 2(2), pages 111-120, July.
    11. M. Hashem Pesaran, 2021. "General diagnostic tests for cross-sectional dependence in panels," Empirical Economics, Springer, vol. 60(1), pages 13-50, January.
    12. M. Hashem Pesaran, 2007. "A simple panel unit root test in the presence of cross-section dependence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(2), pages 265-312.
    13. Jushan Bai & Serena Ng, 2004. "A PANIC Attack on Unit Roots and Cointegration," Econometrica, Econometric Society, vol. 72(4), pages 1127-1177, July.
    14. Markus Eberhardt & Anindya Banerjee and J. James Reade, 2010. "Panel Estimation for Worriers," Economics Series Working Papers 514, University of Oxford, Department of Economics.
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    More about this item

    Keywords

    Panel cointegration; cross-section dependence; common factors; spatial econometrics;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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