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Bridging the Divide? Bayesian Artificial Neural Networks for Frontier Efficiency Analysis

Mike Tsionas, Christopher Parmeter and Valentin Zelenyuk

No WP082021, CEPA Working Papers Series from University of Queensland, School of Economics

Abstract: The literature on firm efficiency has seen its share of research comparing and contrasting Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA), the two workhorse estimators. These studies rely on both Monte Carlo experiments and actual data sets to examine a range of performance issues which can be used to elucidate insights on the benefits or weaknesses of one method over the other. As can be imagined, neither method is universally better than the other. The present paper proposes an alternative approach that is quite flexible in terms of functional form and distributional assumptions and it amalgamates the benefits of both DEA and SFA. Specifically, we bridge these two popular approaches via Bayesian Artificial Neural Networks. We examine the performance of this new approach using Monte Carlo experiments. The performance is found to be very good, comparable or often better than the current standards in the literature. To illustrate the new techniques, we provide an application of this approach to a recent data set of large US banks.

Keywords: Simulation; OR in Banking; Stochastic Frontier Models; Data Envelopment Analysis; Flexible Functional Forms. (search for similar items in EconPapers)
Date: 2021-06
New Economics Papers: this item is included in nep-ban, nep-big, nep-cmp, nep-ecm, nep-eff and nep-ore
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