Hypothesis testing based on a vector of statistics

Maxwell King, Xibin Zhang and Muhammad Akram

Journal of Econometrics, 2020, vol. 219, issue 2, 425-455

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; Smallest acceptance region tests (search for similar items in EconPapers)
JEL-codes: C01 C12 C14 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Working Paper: Hypothesis Testing Based on a Vector of Statistics (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:219:y:2020:i:2:p:425-455

DOI: 10.1016/j.jeconom.2020.03.010

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