Addressing endogeneity when estimating stochastic ray production frontiers: a Bayesian approach

Mike Tsionas, Marwan Izzeldin (), Arne Henningsen and Evaggelos Paravalos ()
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Marwan Izzeldin: Lancaster University Management School
Evaggelos Paravalos: Athens University of Economics and Business

Empirical Economics, 2022, vol. 62, issue 3, No 17, 1345-1363

Abstract: Abstract We propose a Bayesian approach for inference in the stochastic ray production frontier (SRPF), which can model multiple-input–multiple-output production technologies even in case of zero output quantities, i.e., if some outputs are not produced by some of the firms. However, the econometric estimation of the SRPF—as the estimation of distance functions in general—is susceptible to endogeneity problems. To address these problems, we apply a profit-maximizing framework to derive a system of equations after incorporating technical inefficiency. As the latter enters non-trivially into the system of equations and as the Jacobian is highly complicated, we use Monte Carlo methods of inference. Using US banking data to illustrate our innovative approach, we also address the problems of missing prices and the dependence on the ordering of the outputs via model averaging.

Keywords: Stochastic ray production frontier; Technical inefficiency; Endogeneity; Bayesian inference; Model averaging (search for similar items in EconPapers)
JEL-codes: C11 C13 D24 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00181-021-02060-0

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