Objective Bayesian Model Selection
Frequentist and Bayesian approaches to statistics have long been seen as incompatible, but recent work has been done to try and unify them (Bayarri and Berger, 2004; Efron, 2005). Empirical Bayes, approximate Bayesian analysis, and the matching prior approach are examples of methods where prior elicitation is driven by a frequentist interpretation of the data. In this report, we review some standard frequentist and Bayesian model selection techniques and describe how objective Bayes theory can help in this decision-oriented framework, typically by enabling consideration of uncertainty in the model-building process. Objective Bayes mehtods are then used and compared with standard model selection methods on data from the financial sector in Switzerland, Greece, and the United States during 1999 to 2013; results show broad agreement between the methods, but conclusions are less clear-cut in the objective Bayes framework.