A Monte Carlo Study of Efficiency Estimates from Frontier Models

Center for Policy Research Working Paper No. 97

34 Pages Posted: 21 Apr 2011 Last revised: 14 Mar 2015

See all articles by William C. Horrace

William C. Horrace

Syracuse University - Department of Economics

Seth Richards-Shubik

Lehigh University - Department of Economics; National Bureau of Economic Research (NBER)

Date Written: August 1, 2007

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 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.You can download a PDF version of the paper and view it and print it using a FREE copy of Adobe Acrobat Reader.

Keywords: Truncated Normal, Stochastic Frontier, Efficiency, Multivariate Probabilities

JEL Classification: C12, C16, C44, D24

Suggested Citation

Horrace, William C. and Richards-Shubik, Seth, A Monte Carlo Study of Efficiency Estimates from Frontier Models (August 1, 2007). Center for Policy Research Working Paper No. 97, Available at SSRN: https://ssrn.com/abstract=1815366 or http://dx.doi.org/10.2139/ssrn.1815366

William C. Horrace (Contact Author)

Syracuse University - Department of Economics ( email )

Syracuse, NY 13244-1020
United States
315-443-9061 (Phone)
315-443-1081 (Fax)

HOME PAGE: http://faculty.maxwell.syr.edu/whorrace

Seth Richards-Shubik

Lehigh University - Department of Economics ( email )

620 Taylor Street
Bethlehem, PA 18015
United States

HOME PAGE: http://www.lehigh.edu/~ser315

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

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