Journal of Probability and Statistics
Volume 2011 (2011), Article ID 568457, 13 pages
Research Article

Estimation of Stochastic Frontier Models with Fixed Effects through Monte Carlo Maximum Likelihood

1Business Economics Group, Wageningen University, 6707 KN Wageningen, The Netherlands
2Department of Agricultural Economics and Rural Sociology, The Pennsylvania State University, University Park, PA 16802, USA

Received 30 June 2011; Accepted 31 August 2011

Academic Editor: Mike Tsionas

Copyright © 2011 Grigorios Emvalomatis et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Estimation of nonlinear fixed-effects models is plagued by the incidental parameters problem. This paper proposes a procedure for choosing appropriate densities for integrating the incidental parameters from the likelihood function in a general context. The densities are based on priors that are updated using information from the data and are robust to possible correlation of the group-specific constant terms with the explanatory variables. Monte Carlo experiments are performed in the specific context of stochastic frontier models to examine and compare the sampling properties of the proposed estimator with those of the random-effects and correlated random-effects estimators. The results suggest that the estimator is unbiased even in short panels. An application to a cross-country panel of EU manufacturing industries is presented as well. The proposed estimator produces a distribution of efficiency scores suggesting that these industries are highly efficient, while the other estimators suggest much poorer performance.