### Abstract

Original language | English |
---|---|

Title of host publication | A Companion to Theoretical Econometrics |

Pages | 520-537 |

Number of pages | 17 |

DOIs | |

Publication status | Published - 2001 |

### Fingerprint

### Keywords

- bayesian analysis
- stochastic frontier models
- bayesian inference
- nonlinear production frontiers
- panel data

### Cite this

*A Companion to Theoretical Econometrics*(pp. 520-537) https://doi.org/10.1002/9780470996249.ch25

}

*A Companion to Theoretical Econometrics.*pp. 520-537. https://doi.org/10.1002/9780470996249.ch25

**Bayesian analysis of stochastic frontier models.** / Koop, Gary; Steel, Mark F.J.

Research output: Chapter in Book/Report/Conference proceeding › Chapter

TY - CHAP

T1 - Bayesian analysis of stochastic frontier models

AU - Koop, Gary

AU - Steel, Mark F.J.

PY - 2001

Y1 - 2001

N2 - In this chapter, we described a Bayesian approach to efficiency analysis using stochastic frontier models. With cross-sectional data and a log-linear frontier, a simple Gibbs sampler can be used to carry out Bayesian inference. In the case of a nonlinear frontier, more complicated posterior simulation methods are necessary. Bayesian efficiency measurement with panel data is then discussed. We show how a Bayesian analogue of the classical fixed effects panel data model can be used to calculate the efficiency of each firm relative to the most efficient firm. However, absolute efficiency calculations are precluded in this model and inference on efficiencies can be quite sensitive to prior assumptions. Accordingly, we describe a Bayesian analogue of the classical random effects panel data model which can be used for robust inference on absolute efficiencies. Throughout, we emphasize the computational methods necessary to carry out Bayesian inference. We show how random number generation from well-known distributions is sufficient to develop posterior simulators for a wide variety of models.

AB - In this chapter, we described a Bayesian approach to efficiency analysis using stochastic frontier models. With cross-sectional data and a log-linear frontier, a simple Gibbs sampler can be used to carry out Bayesian inference. In the case of a nonlinear frontier, more complicated posterior simulation methods are necessary. Bayesian efficiency measurement with panel data is then discussed. We show how a Bayesian analogue of the classical fixed effects panel data model can be used to calculate the efficiency of each firm relative to the most efficient firm. However, absolute efficiency calculations are precluded in this model and inference on efficiencies can be quite sensitive to prior assumptions. Accordingly, we describe a Bayesian analogue of the classical random effects panel data model which can be used for robust inference on absolute efficiencies. Throughout, we emphasize the computational methods necessary to carry out Bayesian inference. We show how random number generation from well-known distributions is sufficient to develop posterior simulators for a wide variety of models.

KW - bayesian analysis

KW - stochastic frontier models

KW - bayesian inference

KW - nonlinear production frontiers

KW - panel data

UR - http://eu.wiley.com/WileyCDA/WileyTitle/productCd-063121254X.html

UR - http://dx.doi.org/10.1002/9780470996249.ch25

U2 - 10.1002/9780470996249.ch25

DO - 10.1002/9780470996249.ch25

M3 - Chapter

SN - 063121254X

SP - 520

EP - 537

BT - A Companion to Theoretical Econometrics

ER -