期刊名称：Central European Journal of Economic Modelling and Econometrics
印刷版ISSN：2080-0886
电子版ISSN：2080-119X
出版年度：2015
期号：4
页码：219-247
语种：English
出版社：Polska Akademia Nauk
摘要：Finite mixture and Markov-switching models generalize and, therefore, nest specifications featuring only one component. While specifying priors in the general (mixture) model and its special (single-component) case, it may be desirable to ensure that the prior assumptions introduced into both structures are compatible in the sense that the prior distribution in the nested model amounts to the conditional prior in the mixture model under relevant parametric restriction. The study provides the rudiments of setting compatible priors in Bayesian univariate finite mixture and Markov-switching models. Once some primary results are delivered, we derive specific conditions for compatibility in the case of three types of continuous priors commonly engaged in Bayesian modeling: the normal, inverse gamma, and gamma distributions. Further, we study the consequences of introducing additional constraints into the mixture model’s prior on the conditions. Finally, the methodology is illustrated through a discussion of setting compatible priors for Markov-switching AR(2) models.
关键词：Bayesian inference;prior coherence;prior compatibility, exponential family
