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Panels with Nonstationary Multifactor Error Structures

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  • 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.

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Bibliographic Info

Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 2243.

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Length: 33 pages
Date of creation: Aug 2006
Date of revision:
Publication status: published in: Journal of Econometrics, 2011, 160 (2), 326-348
Handle: RePEc:iza:izadps:dp2243

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Keywords: cross section dependence; large panels; unit roots; principal components; common correlated effects;

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