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Robust Bayesian Inference in Stochastic Frontier Models

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Article number183
<mark>Journal publication date</mark>4/12/2019
<mark>Journal</mark>Journal of Risk and Financial Management
Number of pages9
Publication StatusPublished
<mark>Original language</mark>English


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