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On panel data filtering in technical efficiency estimation

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  • Marco Di Marzio

    (Universitá G. d’Annunzio)

Abstract

. In present days it is commonly recognized that firm production datasets are affected by some level of random perturbation, and that consequently production frontiers have a stochastic nature. Mathematical programming methods, traditionally employed for frontier evaluation, are then reputed capable of mistaking errors for technical (in)efficiency. Therefore, recent literature is oriented towards a statistical view: frontiers are designed by enveloping data that have been preliminarly filtered from noise. In this paper a nonparametric smoother for filtering panel production data is presented. We pursue a recent approach of Kneip and Simar (1996), and frame it into a more general formulation whose a setting constitutes our specific proposal. The major feature of the method is that noise reduction and outlier detection are faced separately: i) a high order local polynomial fit is used as smoother; and ii) data are weighted by robustness scores. An extensive numerical study on some common production models yields encouraging results from a competition with Kneip and Simar’s filter.

Suggested Citation

  • Marco Di Marzio, 2004. "On panel data filtering in technical efficiency estimation," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 12(3), pages 319-329, February.
    • Handle: RePEc:spr:stmapp:v:12:y:2004:i:3:d:10.1007_s10260-003-0071-1
    DOI: 10.1007/s10260-003-0071-1
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