Posterior-based Wald-type statistics for hypothesis testing

Xiaobin Liu, Yong Li, Jun Yu and Tao Zeng

Journal of Econometrics, 2022, vol. 230, issue 1, 83-113

Abstract: A new Wald-type statistic is proposed for hypothesis testing based on Bayesian posterior distributions under the correct model specification. The new statistic can be explained as a posterior version of the Wald statistic and has 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 parameters of interest. Fifth and perhaps most importantly, when a random sample from the posterior distribution (such as 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 random samples. A robust version of the test statistic is developed under model misspecification and inherits many nice properties of the new posterior statistic. The finite sample performance of the statistics is examined in Monte Carlo studies. The method is applied to two latent variable models used in microeconometrics and financial econometrics.

Keywords: Decision theory; Hypothesis testing; Latent variable models; Posterior simulation; Wald test (search for similar items in EconPapers)
JEL-codes: C11 C12 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Working Paper: A Posterior-Based Wald-Type Statistic for Hypothesis Testing (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:230:y:2022:i:1:p:83-113

DOI: 10.1016/j.jeconom.2021.11.003

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