A Monte Carlo Study of Efficiency Estimates from Frontier Models

Author(s)/Creator(s)

William Clinton Horrace, Syracuse University. Center for Policy ResearchFollow
Seth O. Richards

Description/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 estimated (a) the conditional means of inefficiency and (b) probabilities that firms are most or least efficient. Monte Carlo experiments suggest that the efficiency probabilities are more reliable 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 environment.

Document Type

Working Paper

Date

2007

Keywords

Truncated normal, stochastic frontier, efficiency, multivariate probabilities.

Language

English

Series

Working Papers Series

Disciplines

Econometrics

Additional Information

Harvest from RePEc at http://repec.org

Recommended Citation

Horrace, William Clinton and Richards, Seth O., "A Monte Carlo Study of Efficiency Estimates from Frontier Models" (2007). Center for Policy Research. 68.
https://surface.syr.edu/cpr/68

Source

Metadata from RePEc