Joint production in stochastic non-parametric envelopment of data with firm-specific directions
نویسندگان
چکیده
We propose a likelihood-based approach to Stochastic Non-Parametric Envelopment of Data (StoNED) estimator using directional distance function with firm-specific vectors. Additionally, we show how estimate inefficiency estimates instead focusing on their average only. Moreover, models that are robust misspecification in general and the use unit-information-priors this class models. These priors control amount information be exactly equal one observation. In context, Bayesian Bootstrapping further mitigate possible misspecification. also empirical tests for identification model. Monte Carlo experiments good performance new techniques an application technology large U.S. banks shows feasibility techniques.
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ژورنال
عنوان ژورنال: European Journal of Operational Research
سال: 2023
ISSN: ['1872-6860', '0377-2217']
DOI: https://doi.org/10.1016/j.ejor.2022.09.029