Posterior-based Wald-type statistics for hypothesis testing

Xiaobin Liu

doi:10.1016/j.jeconom.2021.11.003

Return

Posterior-based Wald-type statistics for hypothesis testing

2022-09-01

DOI：10.1016/j.jeconom.2021.11.003

Original

Save

View PDF

Abstract

En 中文

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 chi 2 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. (C) 2021 Elsevier B.V. All rights reserved.

Keywords:

Decision theory

Hypothesis testing

Latent variable models

Posterior simulation

Wald test

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.