Model uncertainty and efficiency measurement in stochastic frontier analysis with generalized errors
نویسندگان
چکیده
Abstract Advanced efficiency measurement methods usually fall within Stochastic Frontier Analysis (SFA), Data Envelopment (DEA), or their derivatives. Although SFA has some theoretical advantages, it been criticized for relying on arbitrary and potentially restrictive assumptions about model specification. One strand of the literature suggests use nonparametric SF models to cope with issue. We follow an alternative path demonstrate that is possible deal specification uncertainty while maintaining advantages parametric approach. First, we develop a flexible stochastic based generalized t beta second kind distributions, which encompasses virtually all known specifications. Second, apply Bayesian inference methods, are less than those used so far, propose feasible approximate alternatives maximum likelihood. Third, pool results from specifications using averaging. Our focus distributional regarding compound error in since this aspect not addressed far satisfactory way. However, extensions other elements uncertainty, like choice frontier functional form, straightforward. Finally, show simulations analyze two well-researched datasets, obtain probabilistic (density) estimates scores take into account estimation formally justified manner.
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ژورنال
عنوان ژورنال: Journal of Productivity Analysis
سال: 2022
ISSN: ['0895-562X', '1573-0441']
DOI: https://doi.org/10.1007/s11123-022-00639-y