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A Stochastic Frontier Model for Discrete Ordinal Outcomes: A Health Production Function

William Griffiths (), Xiaohui Zhang () and Xueyan Zhao ()

No 3/10, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: The stochastic frontier model used for continuous dependent variables is extended to accommodate output measured as a discrete ordinal outcome variable. Conditional on the inefficiency error, the assumptions of the ordered probit model are adopted for the log of output. Bayesian estimation utilizing a Gibbs sampler with data augmentation is applied to a convenient re-parameterisation of the model. Using panel data from an Australian longitudinal survey, demographic and socioeconomic characteristics are specified as inputs to health production, whereas production efficiency is made dependent on lifestyle factors. Posterior summary statistics are obtained for selected health status probabilities, efficiencies, and marginal effects.

Keywords: Bayesian estimation; Gibbs sampling; ordered probit; production efficiency. (search for similar items in EconPapers)
JEL-codes: C11 C21 C23 I12 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-eff
Date: 2010-02
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