Goodness-of-fit in production models: A Bayesian perspective

Mike Tsionas, Valentin Zelenyuk and Xibin Zhang

European Journal of Operational Research, 2025, vol. 324, issue 2, 644-653

Abstract: We propose a general approach for modeling production technologies, allowing for modeling both inefficiency and noise that are specific for each input and each output. The approach is based on amalgamating ideas from nonparametric activity analysis models for production and consumption theory with stochastic frontier models. We do this by effectively re-interpreting the activity analysis models as simultaneous equations models in Bayesian compression and artificial neural networks framework. We make minimal assumptions about noise in the data and we allow for flexible approximations to input- and output-specific slacks. We use compression to solve the problem of an exceeding number of parameters in general production technologies and also incorporate environmental variables in the estimation. We also present Monte Carlo simulation results and an empirical illustration of this approach for US banking data.

Keywords: Productivity and competitiveness; Bayesian compression; Data Envelopment Analysis; Bayesian artificial neural networks; Frontier methods; Goodness-of-fit; US banking (search for similar items in EconPapers)
JEL-codes: C11 C14 C39 C44 C45 C61 D24 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:324:y:2025:i:2:p:644-653

DOI: 10.1016/j.ejor.2025.01.030

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