Nonparametric Tests of Tail Behavior in Stochastic Frontier Models

Author(s)/Creator(s)

William C. Horrace, Syracuse UniversityFollow
Yulong Wang, Syracuse UniversityFollow

Description/Abstract

This article studies tail behavior for the error components in the stochastic frontier model, where one component has bounded support on one side, and the other has unbounded support on both sides. Under weak assumptions on the error components, we derive nonparametric tests that the unbounded component distribution has thin tails and that the component tails are equivalent. The tests are useful diagnostic tools for stochastic frontier analysis and kernel deconvolution density estimation. A simulation study and an application to a stochastic cost frontier for 6,100 US banks from 1998 to 2005 are provided. The new tests reject the normal or Laplace distributional assumptions, which are commonly imposed in the existing literature.

Document Type

Working Paper

Date

6-2020

Keywords

Hypothesis Testing, Production, Inefficiency, Deconvolution, Extreme Value Theory

Language

English

Series

Working Papers Series

Disciplines

Economic Policy | Economics | Public Affairs, Public Policy and Public Administration

ISSN

1525-3066

Additional Information

Working paper no. 230

Recommended Citation

Horrace, William C. and Wang, Yulong, "Nonparametric Tests of Tail Behavior in Stochastic Frontier Models" (2020). Center for Policy Research. 264.
https://surface.syr.edu/cpr/264

Source

Local input

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.