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Hypothesis Testing Based on a Vector of Statistics

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  • Maxwell King
  • Xibin Zhang
  • Muhammad Akram

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.

Suggested Citation

  • Maxwell King & Xibin Zhang & Muhammad Akram, 2019. "Hypothesis Testing Based on a Vector of Statistics," Monash Econometrics and Business Statistics Working Papers 30/19, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2019-30
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    File URL: https://www.monash.edu/business/ebs/research/publications/ebs/wp30-2019.pdf
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    More about this item

    Keywords

    bootstrap; cross-market prediction; information matrix test; Markov chain Monte Carlo; multivariate kernel density; p-value.;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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