Estimation and testing of stochastic frontier models using variational Bayes
Gholamreza Hajargasht () and
William E. Griffiths ()
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William E. Griffiths: University of Melbourne
Journal of Productivity Analysis, 2018, vol. 50, issue 1, 1-24
Abstract We show how a wide range of stochastic frontier models can be estimated relatively easily using variational Bayes. We derive approximate posterior distributions and point estimates for parameters and inefficiency effects for (a) time invariant models with several alternative inefficiency distributions, (b) models with time varying effects, (c) models incorporating environmental effects, and (d) models with more flexible forms for the regression function and error terms. Despite the abundance of stochastic frontier models, there have been few attempts to test the various models against each other, probably due to the difficulty of performing such tests. One advantage of the variational Bayes approximation is that it facilitates the computation of marginal likelihoods that can be used to compare models. We apply this idea to test stochastic frontier models with different inefficiency distributions. Estimation and testing is illustrated using three examples.
Keywords: Technical efficiency; Marginal likelihood; Time-varying panel; Environmental effects; Mixture; Semiparametric model (search for similar items in EconPapers)
JEL-codes: C11 C12 C23 D24 (search for similar items in EconPapers)
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Working Paper: Estimation and Testing of Stochastic Frontier Models using Variational Bayes (2016)
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