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On the bias of the LSDV estimator in dynamic panel data models with endogenous regressors

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  • Kurennoy, Alexey

    (Russian Presidential Academy of National Economy and Public Administration (RANEPA))

Abstract

This paper studies the behaviour of the bias corrected LSDV estimator and GMM-based estimators in dynamic panel data models with endogenous regressors. We obtain an expansion of the conditional bias of the LSDV estimator with the leading term coinciding with the one in the expansion from (Kiviet, 1995) and (Kiviet, 1999). Nevertheless, our simulations suggest that in the presence of endogenous regressors the performance of the corrected LSDV estimator can be low. This indicates that although the bias has similar structure whether or not the exogeneity assumption holds, the approximation technique that the LSDVc estimator is based on can work poorly in the endogenous case. GMM-based estimators also have low performance in our experiment.

Suggested Citation

  • Kurennoy, Alexey, 2015. "On the bias of the LSDV estimator in dynamic panel data models with endogenous regressors," Published Papers kur001, Russian Presidential Academy of National Economy and Public Administration.
    • Handle: RePEc:rnp:ppaper:kur001
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    Cited by:

    1. Lee, Boon L. & Wilson, Clevo & Simshauser, Paul & Majiwa, Eucabeth, 2021. "Deregulation, efficiency and policy determination: An analysis of Australia's electricity distribution sector," Energy Economics, Elsevier, vol. 98(C).

    More about this item

    Keywords

    dynamic panel data model; endogenous regressors; LSDV estimator; bias expansion; simulation;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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