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  • 1.
    Wikström, Daniel
    Dalarna University, School of Technology and Business Studies, Statistics.
    A finite sample improvement of the fixed effects estimator applied to technical inefficiency2015In: Journal of Productivity Analysis, ISSN 0895-562X, E-ISSN 1573-0441, Vol. 43, no 1, p. 29-46Article in journal (Refereed)
    Abstract [en]

    The fixed effects (‘FE’) estimator of technical inefficiency performs poorly when N (the ’number of firms’) is large and T (the ‘number of time observations’) is small. We propose kernel estimators, which includes the FE estimator as a special case. In terms of criteria based on collective conditional ‘mean square error’, it is demonstrated that some kernel estimators are more efficient than the FE estimators of firm effects and inefficiencies in finite sample settings. Monte Carlo simulations support our theoretical findings, and we use an empirical example to show how FE estimation and kernel estimation lead to very different conclusions about technical inefficiency among Indonesian rice farmers.

  • 2.
    Wikström, Daniel
    Dalarna University, School of Technology and Business Studies, Statistics.
    Modified fixed effects estimation of technical inefficiency2016In: Journal of Productivity Analysis, ISSN 0895-562X, E-ISSN 1573-0441, Vol. 46, no 1, p. 83-86Article in journal (Refereed)
    Abstract [en]

    We consider method-of-moments fixed effects (FE) estimation of technical inefficiency. When dealing with a large number of cross-sectional observations, N, it is possible to obtain consistent moment estimators of the inefficiency distribution. It is well known that the classical FE estimator may be seriously upward biased when N is large and T, the number of time observations, is small. The method-of-moments FE estimators do not suffer from this type of bias in large-N settings. The proposed methodology bridges classical FE and maximum likelihood estimation, leading to a reduction in bias without making the random effects assumption.

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