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Frontier estimation in the presence of measurement error with unknown variance

Alois Kneip, Leopold Simar () and Ingrid Van Keilegom

Journal of Econometrics, 2015, vol. 184, issue 2, 379-393

Abstract: Frontier estimation appears in productivity analysis. Firm’s performance is measured by the distance between its output and an optimal production frontier. Frontier estimation becomes difficult if outputs are measured with noise and most approaches rely on restrictive parametric assumptions. This paper contributes to nonparametric approaches, with unknown frontier and unknown variance of a normally distributed error. We propose a nonparametric method identifying and estimating both quantities simultaneously. Consistency and rate of convergence of our estimators are established, and simulations verify the performance of the estimators for small samples. We illustrate our method with data on American electricity companies.

Keywords: Deconvolution; Stochastic frontier estimation; Nonparametric estimation; Penalized likelihood (search for similar items in EconPapers)
JEL-codes: C13 C14 C49 D24 (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:184:y:2015:i:2:p:379-393

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Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

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