Hypothesis testing based on a vector of statistics

Maxwell L. King, Xibin Zhang, Muhammad Akram

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)425-455
Number of pages31
JournalJournal of Econometrics
Issue number2
Publication statusPublished - Dec 2020


  • Bootstrap
  • Cross-market prediction
  • Information matrix test
  • Markov chain Monte Carlo
  • Multivariate kernel density
  • p-value
  • Smallest acceptance region tests

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