Wheat, P orcid.org/0000-0003-0659-5052, Stead, AD orcid.org/0000-0002-7836-3827 and Greene, WH (2019) Robust stochastic frontier analysis: a Student’s t-half normal model with application to highway maintenance costs in England. Journal of Productivity Analysis, 51 (1). pp. 21-38. ISSN 0895-562X
Abstract
The presence of outliers in the data has implications for stochastic frontier analysis, and indeed any performance analysis methodology, because they may lead to imprecise parameter estimates and, crucially, lead to an exaggerated spread of efficiency predictions. In this paper we replace the normal distribution for the noise term in the standard stochastic frontier model with a Student’s t distribution, which generalises the normal distribution by adding a shape parameter governing the degree of kurtosis. This has the advantages of introducing flexibility in the heaviness of the tails, which can be determined by the data, as well as containing the normal distribution as a limiting case, and we outline how to test against the standard model. Monte Carlo simulation results for the maximum simulated likelihood estimator confirm that the model recovers appropriate frontier and distributional parameter estimates under various values of the true shape parameter. The simulation results also indicate the influence of a phenomenon we term ‘wrong kurtosis’ in the case of small samples, which is analogous to the issue of ‘wrong skewness’ previously identified in the literature. We apply a Student’s t-half normal cost frontier to data for highways authorities in England, and this formulation is found to be preferred by statistical testing to the comparator normal-half normal cost frontier model. The model yields a significantly narrower range of efficiency predictions, which are non-monotonic at the tails of the residual distribution.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, 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. |
Keywords: | Stochastic frontier analysis; Robust analysis; Student's t; Outliers; Wrong Kurtosis |
Dates: |
|
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) |
Funding Information: | Funder Grant number Measure2Improve M2I |
Depositing User: | Symplectic Publications |
Date Deposited: | 13 Dec 2018 12:25 |
Last Modified: | 25 Jun 2023 21:38 |
Status: | Published |
Publisher: | Springer US |
Identification Number: | 10.1007/s11123-018-0541-y |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:139935 |
Download
Filename: Wheat2019_Article_RobusTSTochasTicFronTierAnalys.pdf
Licence: CC-BY 4.0