On panel data filtering in technical efficiency estimation

Marco Di Marzio ()
Additional contact information
Marco Di Marzio: Universitá G. d’Annunzio

Statistical Methods & Applications, 2004, vol. 12, issue 3, No 4, 319-329

Abstract: 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.

Keywords: Polynominal regression; Production function; Robustness; Stochastic frontier models; Weighted cross-validation (search for similar items in EconPapers)
Date: 2004
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10260-003-0071-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:stmapp:v:12:y:2004:i:3:d:10.1007_s10260-003-0071-1

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10260/PS2

DOI: 10.1007/s10260-003-0071-1

Access Statistics for this article

Statistical Methods & Applications is currently edited by Tommaso Proietti

More articles in Statistical Methods & Applications from Springer, Società Italiana di Statistica
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().