Journal of Productivity Analysis

, Volume 6, Issue 3, pp 247–268

Estimators and hypothesis tests for a stochastic frontier function: A Monte Carlo analysis

  • Tim Coelli
Article

DOI: 10.1007/BF01076978

Cite this article as:
Coelli, T. J Prod Anal (1995) 6: 247. doi:10.1007/BF01076978

Abstract

This paper uses Monte Carlo experimentation to investigate the finite sample properties of the maximum likelihood (ML) and corrected ordinary least squares (COLS) estimators of the half-normal stochastic frontier production function. Results indicate substantial bias in both ML and COLS when the percentage contribution of inefficiency in the composed error (denoted by γ*) is small, and also that ML should be used in preference to COLS because of large mean square error advantages when γ* is greater than 50%. The performance of a number of tests of the existence of technical inefficiency is also investigated. The Wald and likelihood ratio (LR) tests are shown to have incorrect size. A one-sided LR test and a test of the significance of the third moment of the OLS residuals are suggested as alternatives, and are shown to have correct size, with the one-sided LR test having the better power of the two.

Key words

Stochastic frontierMonte Carloestimationtests

Copyright information

© Kluwer Academic Publishers 1995

Authors and Affiliations

  • Tim Coelli
    • 1
  1. 1.Econometrics DepartmentUniversity of New EnglandArmidaleAustralia