Inference in the nonparametric stochastic frontier model

Christopher F. Parmeter, Leopold Simar, Ingrid Van Keilegom () and Valentin Zelenyuk
Additional contact information
Christopher F. Parmeter: University of Miami
Ingrid Van Keilegom: Université catholique de Louvain, LIDAM/ISBA, Belgium

No 2024013, LIDAM Reprints ISBA from Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA)

Abstract: This article 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 nonparametric 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 nonparametric 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: Efficiency; local-polynomial least-squares; productivity analysis; simulation; stochastic frontier analysis (search for similar items in EconPapers)
JEL-codes: C1 C13 C14 (search for similar items in EconPapers)
Pages: 22
Date: 2024-04-13
Note: In: Econometric Reviews, 2024, vol. 43(7), p. 518-539
References: Add references at CitEc
Citations: View citations in EconPapers (1)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
Journal Article: Inference in the nonparametric stochastic frontier model (2024)
Working Paper: Inference in the Nonparametric Stochastic Frontier Model (2021)
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:aiz:louvar:2024013

DOI: 10.1080/07474938.2024.2339193

Access Statistics for this paper

More papers in LIDAM Reprints ISBA from Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA) Voie du Roman Pays 20, 1348 Louvain-la-Neuve (Belgium). Contact information at EDIRC.
Bibliographic data for series maintained by Nadja Peiffer ().