Estimation of inefficiency in stochastic frontier models: a Bayesian kernel approach
- 34 Downloads
We propose a kernel-based Bayesian framework for the analysis of stochastic frontiers and efficiency measurement. The primary feature of this framework is that the unknown distribution of inefficiency is approximated by a transformed Rosenblatt-Parzen kernel density estimator. To justify the kernel-based model, we conduct a Monte Carlo study and also apply the model to a panel of U.S. large banks. Simulation results show that the kernel-based model is capable of providing more precise estimation and prediction results than the commonly-used exponential stochastic frontier model. The Bayes factor also favors the kernel-based model over the exponential model in the empirical application.
KeywordsKernel density estimation Efficiency measurement Stochastic distance frontier Markov Chain Monte Carlo
JEL ClassificationC11 D24 G21
We would like to thank Professor Cheng Hsiao at the University of Southern California for helpful discussion. We would also like to thank the reviewers for their constructive comments that have led to substantial improvement of the paper.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- Berger AN, Mester LJ (2003) Explaining the dramatic changes in the performance of U.S. banks: technological change, deregulation, and dynamic changes in competition. J Financ Inter 12:57–95Google Scholar
- Geweke JF (2009) Complete and incomplete econometric models. Princeton University Press, New JerseyGoogle Scholar
- Greene W (2008) The econometric approach to efficiency analysis. In: Fried HO, Knox Lovell CA, Schmidt P (eds) The Measurement of Productive Efficiency. Oxford University Press, New YorkGoogle Scholar
- Griffin J, Steel MFJ (2008) Flexible mixture modeling of stochastic frontiers. J Product Anal 21:157–178Google Scholar
- Johnson N, Kotz S (1972) Distributions in statistics: Continuous multivariate distributions. Wiley, New YorkGoogle Scholar
- Koop G (2003) Bayesian econometrics. Wiley, ChichesterGoogle Scholar
- Malikov, E., Kumbhakar, S., Tsionas, E. (2015) A cost system approach to the stochastic directional technology distance function with undesirable outputs: The case of US banks in 2001-2010. J Appl Econ. https://doi.org/10.1002/jae.2491
- Schmidt P, Sickles RC (1984) Production frontiers and panel data. J Bus Econ Stat 2(4):367–374Google Scholar
- Yuan A, de Gooijer JG (2007) Semiparametric regression with kernel error model. Scand J Stat 34(4):841–869Google Scholar