We consider a Bayesian analysis of the stochastic frontier model with composed error. Under a commonly used class of (partly) noninformative prior distributions, the existence of the posterior distribution and of posterior moments is examined. Viewing this model as a Normal linear regression model with regression parameters corresponding to both the frontier and the inefficiency terms, generates the insights used to derive results in a very wide framework. It is found that in pure cross-section models posterior inference is precluded under this ``usual'' class of priors. Existence of a well-defined posterior distribution crucially hinges upon the structure imposed on the inefficiency terms. Exploiting panel data naturally suggests the use of more structured models, where Bayesian inference can be conducted.
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Paper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number
17.
Find related papers by JEL classification: C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data D24 - Microeconomics - - Production and Organizations - - - Production; Capital and Total Factor Productivity; Capacity
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