A stochastic frontier regression model with dynamic frontier

Abstract

We consider a stochastic frontier regression model with a time dependent efficiency process, which is assumed to follow an exponential autoregressive sequence. The likelihood for the model is derived in the context of a bivariate exponential distribution. Bayesian method is suggested for the estimation of parameters. We apply the model and the estimation procedure to a panel of US airlines data and show empirically that the model is dynamic in the sense that it reveals improvement in the efficiency or reduction in the inefficiency over time.

Keywords:

• Exponential autoregression
• Gumbel bivariate exponential distribution
• Stochastic frontier models
• Technical efficiency

Acknowledgments

The authors are thankful to the associate editor and the referee for some valuable comments. T. V. Ramanathan would like to acknowledge the Department of Statistics and Actuarial Science, University of Waterloo, Canada, for the facilities provided during his short visit.

Additional information

Funding

The research of Bovas Abraham was supported in part by a grant from the Natural Sciences and Engineering Research Council (NSERC) of Canada.