On Hypothesis Testing in Latent Class and Finite Mixture Stochastic Frontier Models, with Application to a Contaminated Normal-Half Normal Model

Abstract

Latent class and finite mixture stochastic frontier models have been proposed as a means of allowing either for technological heterogeneity or more flexible distributions of noise and inefficiency. As in the wider literature on latent class and finite mixture models, we are interested in class enumeration, particularly testing against homogeneity. We apply a modified likelihood ratio test for homogeneity in a stochastic frontier setting, based on established results for non-Gaussian latent class and finite mixture models, and provide evidence from Monte Carlo experiments which suggest the applicability of results regarding a modified likelihood ratio test to the stochastic frontier setting. We demonstrate an application to testing a model with a contaminated normal noise term against a model with a normally distributed noise term, finding that the former is preferred, with significant implications for efficiency prediction.

Metadata

Item Type:	Article
Authors/Creators:	Stead, AD https://orcid.org/0000-0002-7836-3827 Wheat, P https://orcid.org/0000-0003-0659-5052 Greene, WH
Copyright, Publisher and Additional Information:	© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Keywords:	Stochastic frontier analysis Latent classes; Finite mixtures; Modified likelihood ratio
Dates:	Published: 3 April 2023 Published (online): 3 April 2023 Accepted: 13 March 2023
Institution:	The University of Leeds
Academic Units:	The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Economics and Discrete Choice (Leeds)
Depositing User:	Symplectic Publications
Date Deposited:	14 Mar 2023 11:58
Last Modified:	26 Jun 2023 13:41
Status:	Published
Publisher:	Springer Nature
Identification Number:	10.1007/s11123-023-00669-0
Related URLs:	Dataset
Open Archives Initiative ID (OAI ID):	oai:eprints.whiterose.ac.uk:197345