A Posterior-Based Wald-Type Statistic for Hypothesis Testing

15:00-16:30, Monday, June 15, 2020

Tencent Meeting (Meeting ID: 910 977 323)

Dr. Xiaobin LIU is now an Assistant Professor at College of Economics, Zhejiang University. He obtained his Ph.D. of Economics from Singapore Management University in 2018. His fields of interest are asset pricing, financial econometrics, and Bayesian econometrics. His papers have appeared in Journal of Econometrics and Quantitative Finance.

A new Wald-type statistic is proposed for hypothesis testing based on Bayesian posterior distributions. The new statistic can be explained as a posterior version of Wald test and have several nice properties. First, it is well-defined under improper prior distributions. Second, it avoids Jeffreys-Lindley-Bartlett’s paradox. Third, under the null hypothesis and repeated sampling, it follows a χ2 distribution asymptotically, offering an asymptotically pivotal test. Fourth, it only requires inverting the posterior covariance for the parameters of interest. Fifth and perhaps most importantly, when a random sample from the posterior distribution (such as an MCMC output) is available, the proposed statistic can be easily obtained as a by-product of posterior simulation. In addition, the numerical standard error of the estimated proposed statistic can be computed based on the random sample. The finite sample performance of the statistic is examined in Monte Carlo studies. The method is applied to two latent variable models used in microeconometrics and financial econometrics.

For more information of the seminar, scan the following QR code(s) to join Tencent QQ group (904 544 292) or WeChat group named "IAER Seminar", please.

QQ Group

WeChat Group (Valid until 6/17/2020)