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‘Random Coefficient Panel Data Models’

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  • Hsiao, C.
  • Pesaran, M.H.

Abstract

This paper provides a review of linear panel data models with slope heterogeneity, introduces various types of random coefficients models and suggests a common framework for dealing with them. It considers the fundamental issues of statistical inference of a random coefficients formulation using both the sampling and Bayesian approaches. The paper also provides a review of heterogeneity, simultaneous equation random coefficient models, and the more recent developments in the area of cross-sectional dependence in panel data models.

Suggested Citation

  • Hsiao, C. & Pesaran, M.H., 2004. "‘Random Coefficient Panel Data Models’," Cambridge Working Papers in Economics 0434, Faculty of Economics, University of Cambridge.
    • Handle: RePEc:cam:camdae:0434
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    References listed on IDEAS

    as
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    9. Pesaran, H.M., 2003. "Estimation and Inference in Large Heterogeneous Panels with Cross Section Dependence," Cambridge Working Papers in Economics 0305, Faculty of Economics, University of Cambridge.
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    16. 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.
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    28. Klein, Lawrence R., 1988. "The statistical approach to economics," Journal of Econometrics, Elsevier, vol. 37(1), pages 7-26, January.
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    More about this item

    Keywords

    random coefficient models; dynamic heterogeneous panels; Classical and Bayesian approaches; tests of slope heterogeneity; cross section dependence.;
    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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