Bayesian Artificial Neural Networks for Frontier Efficiency Analysis

Valentin Zelenyuk and Valentyn Panchenko
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Valentyn Panchenko: School of Economics, University of New South Wales, Australia;

No WP022023, CEPA Working Papers Series from University of Queensland, School of Economics

Abstract: We present a cohesive generalized framework for an aggregation of the Nerlovian profit indicators and of the directional distance functions, frequently used in productivity and efficiency analysis in operations research and econometrics (e.g., via data envelopment analysis or stochastic frontier analysis). Our theoretical framework allows for greater flexibility than previous approaches, and embraces many other approaches as special cases. In the proposed aggregation scheme, the aggregation weights are mathematically derived from assumptions made about the optimization behavior and about the chosen directions of measurement. We also discuss various interesting special cases of popular directions, including the case of Farrelltype effiiency.

Keywords: Efficiency; Productivity; Aggregation; Data Envelopment Analysis (search for similar items in EconPapers)
JEL-codes: D24 O4 (search for similar items in EconPapers)
Date: 2023-01
New Economics Papers: this item is included in nep-cmp and nep-eff
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Related works:
Journal Article: Bayesian Artificial Neural Networks for frontier efficiency analysis (2023)
Working Paper: Bayesian Artificial Neural Networks for Frontier Efficiency Analysis (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:qld:uqcepa:184

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