Bayesian stochastic frontier analysis using WinBUGS
Griffin, Jim E. and Steel, Mark F. J.. (2007) Bayesian stochastic frontier analysis using WinBUGS. Journal of Productivity Analysis, Vol.27 (No.3). pp. 163-176. ISSN 0895-562XFull text not available from this repository.
Official URL: http://dx.doi.org/10.1007/s11123-007-0033-y
Markov chain Monte Carlo (MCMC) methods have become a ubiquitous tool in Bayesian analysis. This paper implements MCMC methods for Bayesian analysis of stochastic frontier models using the WinBUGS package, a freely available software. General code for cross-sectional and panel data are presented and various ways of summarizing posterior inference are discussed. Several examples illustrate that analyses with models of genuine practical interest can be performed straightforwardly and model changes are easily implemented. Although WinBUGS may not be that efficient for more complicated models, it does make Bayesian inference with stochastic frontier models easily accessible for applied researchers and its generic structure allows for a lot of flexibility in model specification.
|Item Type:||Journal Article|
|Subjects:||H Social Sciences > HF Commerce
H Social Sciences > HC Economic History and Conditions
H Social Sciences
|Divisions:||Faculty of Science > Statistics|
|Journal or Publication Title:||Journal of Productivity Analysis|
|Publisher:||Springer New York LLC|
|Official Date:||June 2007|
|Number of Pages:||14|
|Page Range:||pp. 163-176|
|Access rights to Published version:||Restricted or Subscription Access|
Actions (login required)