Flexible mixture modelling of stochastic frontiers

Griffin, Jim E. and Steel, Mark F.J. (2008) Flexible mixture modelling of stochastic frontiers. Journal of Productivity Analysis, 29 (1). pp. 33-50. ISSN 0895-562X. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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This paper introduces new and flexible classes of inefficiency distributions for stochastic frontier models. We consider both generalized gamma distributions and mixtures of generalized gamma distributions. These classes cover many interesting cases and accommodate both positively and negatively skewed composed error distributions. Bayesian methods allow for useful inference with carefully chosen prior distributions. We recommend a two-component mixture model where a sensible amount of structure is imposed through the prior to distinguish the components, which are given an economic interpretation. This setting allows for efficiencies to depend on firm characteristics, through the probability of belonging to either component. Issues of label-switching and separate identification of both the measurement and inefficiency errors are also examined. Inference methods through MCMC with partial centring are outlined and used to analyse both simulated and real data. An illustration using hospital cost data is discussed in some detail.

Item Type: Article
Uncontrolled keywords: centring; efficiency; generalized gamma distribution; prior elicitation; skewness
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Jim Griffin
Date Deposited: 03 Jun 2008 14:05
Last Modified: 21 May 2014 11:26
Resource URI: https://kar.kent.ac.uk/id/eprint/3150 (The current URI for this page, for reference purposes)
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