Journal of Productivity Analysis

, Volume 38, Issue 2, pp 155–165

A Monte Carlo study of ranked efficiency estimates from frontier models

Authors

    • Center for Policy ResearchSyracuse University
  • Seth Richards-Shubik
    • H. John Heinz III College, Carnegie Mellon University
Article

DOI: 10.1007/s11123-011-0238-y

Cite this article as:
Horrace, W.C. & Richards-Shubik, S. J Prod Anal (2012) 38: 155. doi:10.1007/s11123-011-0238-y

Abstract

Parametric stochastic frontier models yield firm-level conditional distributions of inefficiency that are truncated normal. Given these distributions, how should one assess and rank firm-level efficiency? This study compares the techniques of estimating (a) the conditional mean of inefficiency and (b) probabilities that firms are most or least efficient. Monte Carlo experiments suggest that the efficiency probabilities are easier to estimate (less noisy) in terms of mean absolute percent error when inefficiency has large variation across firms. Along the way we tackle some interesting problems associated with simulating and assessing estimator performance in the stochastic frontier model.

Keywords

Truncated normalStochastic frontierEfficiencyMultivariate probabilities

JEL Classifications

C12C16C44D24

Copyright information

© Springer Science+Business Media, LLC 2011