Inference in the Nonparametric Stochastic Frontier Model

Christopher Parmeter, Leopold Simar, Ingrid Van Keilegom and Valentin Zelenyuk
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Ingrid Van Keilegom: Université catholique de Louvain, LIDAM/ISBA, Belgium

No 2021029, LIDAM Discussion Papers ISBA from Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA)

Abstract: This paper is the first in the literature to discuss in detail how to conduct various types of inference in the stochastic frontier model when it is estimated using non-parametric methods. We discuss a general and versatile inferential technique that allows for a range of practical hypotheses of interest to be tested. We also discuss several challenges that currently exist in this framework in an effort to alert researchers to potential pitfalls. Namely, it appears that when one wishes to estimate a stochastic frontier in a fully non-parametric framework, separability between inputs and determinants of inefficiency is an essential ingredient for the correct empirical size of a test. We showcase the performance of the test with a variety of Monte Carlo simulations.

Keywords: Stochastic Frontier Analysis; Efficiency; Productivity Analysis; Local-Polynomial Least- Squares (search for similar items in EconPapers)
JEL-codes: C1 C13 C14 (search for similar items in EconPapers)
Pages: 34
Date: 2021-09-09
New Economics Papers: this item is included in nep-ecm, nep-eff, nep-isf and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Related works:
Journal Article: Inference in the nonparametric stochastic frontier model (2024)
Working Paper: Inference in the nonparametric stochastic frontier model (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:aiz:louvad:2021029

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