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Panels with Nonstationary Multifactor Error Structures

Author

Listed:
  • Kapetanios, George

    (Queen Mary, University of London)

  • Pesaran, M. Hashem

    (University of Cambridge)

  • Yamagata, Takashi

    (University of York)

Abstract

The presence of cross-sectionally correlated error terms invalidates much inferential theory of panel data models. Recently work by Pesaran (2006) has suggested a method which makes use of cross-sectional averages to provide valid inference for stationary panel regressions with multifactor error structure. This paper extends this work and examines the important case where the unobserved common factors follow unit root processes and could be cointegrated. It is found that the presence of unit roots does not affect most theoretical results which continue to hold irrespective of the integration and the cointegration properties of the unobserved factors. This finding is further supported for small samples via an extensive Monte Carlo study. In particular, the results of the Monte Carlo study suggest that the cross-sectional average based method is robust to a wide variety of data generation processes and has lower biases than all of the alternative estimation methods considered in the paper.

Suggested Citation

  • Kapetanios, George & Pesaran, M. Hashem & Yamagata, Takashi, 2006. "Panels with Nonstationary Multifactor Error Structures," IZA Discussion Papers 2243, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp2243
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    More about this item

    Keywords

    cross section dependence; large panels; unit roots; principal components; common correlated effects;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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