Title：Hypothesis Testing Using Posterior-test-based Bayes Factors

　　Schedule: April 27, Wed 12-1 PM

　　Location: 腾讯会议 110 470 028

　　Abstract:Hypothesis testing based on p-values has been criticized in recent years. The conventional Bayes factors (BFs) have been tipped as possible alternatives of p-values. However, conventional BFs suffer from several theoretical and practical difficulties. For example, the conventional BFs are not well-defined under improper priors and they are subject to Jeffreys-Lindley-Bartlett's paradox when proper but vague priors are used. Moreover, they are difficult to compute for many models. In this paper, we propose to compare the sampling distributions of the posterior-test-based statistics for hypothesis testing. Two posterior-test-based statistics are considered, namely the posterior version of likelihood ratio (LR) test and the posterior version of Wald test. Under some regularity conditions, we establish the consistency property of the new method. We also show how the proposed method can address the problems in p-values and those in the conventional BFs. The advantages of the proposed method are highlighted using several simulation studies and empirical studies.

　　Introduction :汪念玲，首都经济贸易大学金融学院讲师。中国人民大学汉青经济与金融高级研究院经济学博士。研究方向为金融计量，贝叶斯计量经济学，资产定价等。曾访问耶鲁大学经济学院、新加坡管理大学经济学院，已有研究发表在Journal of Econometrics , International Review of Finance ，Applied Economics Letters 等期刊。