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Is Adaptive Estimation Useful for Panel Models With Heteroskedasticity in the Unit-Specific Error Component? Some Monte Carlo Evidence

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  • Nilanjana Roy

Abstract

This paper first derives an adaptive estimator when heteroskedasticity is present in the unit-specific error in an error component model and then compares the finite sample performance of the proposed estimator with various other estimators. While the Monte Carlo results show that the proposed estimator performs adequately in terms of relative efficiency, its performance on the basis of empirical size is quite similar to the other estimators considered. The results from using the different estimators in two applications highlight the importance of devising a test in future to distinguish between the source of heteroskedasticity.

Suggested Citation

  • Nilanjana Roy, 1999. "Is Adaptive Estimation Useful for Panel Models With Heteroskedasticity in the Unit-Specific Error Component? Some Monte Carlo Evidence," Econometrics Working Papers 9913, Department of Economics, University of Victoria.
  • Handle: RePEc:vic:vicewp:9913
    Note: ISSN 1485-6441
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    File URL: https://www.uvic.ca/socialsciences/economics/_assets/docs/econometrics/ewp9913.pdf
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    References listed on IDEAS

    as
    1. Baltagi, Badi H & Griffin, James M, 1988. "A Generalized Error Component Model with Heteroscedastic Disturbances," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 29(4), pages 745-753, November.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Heteroskedasticity; Kernel Estimation; Error Component Model;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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