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Generalized �Fixed-T Panel Unit Root Tests Allowing for Structural Breaks

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  • Karavias, Yiannis
  • Tzavalis, Elias

Abstract

In this paper we suggest panel data unit root tests which allow for a structural breaks in the individual effects or linear trends of panel data models. This is done under the assumption that the disturbance terms of the panel are heterogeneous and serially correlated. The limiting distributions of the suggested test statistics are derived under the assumption that the time-dimension of the panel (T) is �fixed, while the cross-section (N) grows large. Thus, they are appropriate for short panels, where T is small. The tests consider the cases of a known and unknown date break. For the latter case, the paper gives the analytic form of the distribution of the test statistics. Monte Carlo evidence suggest that our tests have size which is very close to its nominal level and satisfactory power in small-T panels. This is true even for cases where the degree of serial correlation is large and negative, where single time series unit root tests are found to be critically oversized.

Suggested Citation

  • Karavias, Yiannis & Tzavalis, Elias, 2012. "Generalized �Fixed-T Panel Unit Root Tests Allowing for Structural Breaks," MPRA Paper 43128, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:43128
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    File URL: https://mpra.ub.uni-muenchen.de/43128/1/MPRA_paper_43128.pdf
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    References listed on IDEAS

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    1. Karavias, Yiannis & Tzavalis, Elias, 2013. "The Power Performance of Fixed-T Panel Unit Root Tests allowing for Structural Breaks," MPRA Paper 46012, University Library of Munich, Germany.

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    More about this item

    Keywords

    Panel data models; unit roots; structural breaks;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • 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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