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  • SRPF_Bayesian

    Rights statement: The final publication is available at Springer via http://dx.doi.org/10.1007/s00181-021-02060-0

    Accepted author manuscript, 573 KB, PDF document

    Embargo ends: 2/05/22

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

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

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E-pub ahead of print
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<mark>Journal publication date</mark>2/05/2021
<mark>Journal</mark>Empirical Economics
Number of pages19
Publication StatusE-pub ahead of print
Early online date2/05/21
<mark>Original language</mark>English

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.

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The final publication is available at Springer via http://dx.doi.org/10.1007/s00181-021-02060-0