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Bayesian Artificial Neural Networks for Frontier Efficiency Analysis

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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.

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  • Valentin Zelenyuk & Valentyn Panchenko, 2023. "Bayesian Artificial Neural Networks for Frontier Efficiency Analysis," CEPA Working Papers Series WP022023, School of Economics, University of Queensland, Australia.
    • Handle: RePEc:qld:uqcepa:184
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    File URL: https://economics.uq.edu.au/files/42061/WP022023.pdf
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    1. Léopold Simar & Valentin Zelenyuk & Shirong Zhao, 2023. "Russell and Slack-Based Measures of Efficiency: A Unifying Framework," CEPA Working Papers Series WP092023, School of Economics, University of Queensland, Australia.

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    More about this item

    Keywords

    Efficiency; Productivity; Aggregation; Data Envelopment Analysis;
    All these keywords.

    JEL classification:

    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • O4 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity

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