Hypothesis Testing Based on a Vector of Statistics

Maxwell King, Xibin Zhang () and Muhammad Akram

No 30/19, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: This paper presents a new approach to hypothesis testing based on a vector of statistics. It involves simulating the statistics under the null hypothesis and then estimating the joint density of the statistics. This allows the p-value of the smallest acceptance region test to be estimated. We prove this p-value is a consistent estimate under some regularity conditions. The small-sample properties of the proposed procedure are investigated in the context of testing for autocorrelation, testing for normality, and testing for model misspecification through the information matrix. We find that our testing procedure has appropriate sizes and good powers.

Keywords: bootstrap; cross-market prediction; information matrix test; Markov chain Monte Carlo; multivariate kernel density; p-value. (search for similar items in EconPapers)
JEL-codes: C01 C12 C14 (search for similar items in EconPapers)
Pages: 84
Date: 2019
New Economics Papers: this item is included in nep-ecm and nep-ore
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Journal Article: Hypothesis testing based on a vector of statistics (2020)
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