Robust Bayesian Inference in Stochastic Frontier Models

Tsionas, Mike G. (2019) Robust Bayesian Inference in Stochastic Frontier Models. Journal of Risk and Financial Management: 183. ISSN 1911-8074

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We use the concept of coarsened posteriors to provide robust Bayesian inference via coarsening in order to robustify posteriors arising from stochastic frontier models. These posteriors arise from tempered versions of the likelihood when at most a pre-specified amount of data is used, and are robust to changes in the model. Specifically, we examine robustness to changes in the distribution of the composed error in the stochastic frontier model (SFM). Moreover, coarsening is a form of regularization, reduces overfitting and makes inferences less sensitive to model choice. The new techniques are illustrated using artificial data as well as in a substantive application to large U.S. banks

Item Type:
Journal Article
Journal or Publication Title:
Journal of Risk and Financial Management
?? productivity and efficiencybayesian analysisrobustnessstochastic frontier models ??
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Deposited On:
07 Dec 2020 14:55
Last Modified:
13 Jun 2024 01:45