Hypothesis testing based on a vector of statistics

Journal article

King, Maxwell L., Zhang, Xibin and Akram, Muhammad. (2020). Hypothesis testing based on a vector of statistics. Journal of Econometrics. 219(2), pp. 425-455. https://doi.org/10.1016/j.jeconom.2020.03.010
AuthorsKing, Maxwell L., Zhang, Xibin and Akram, Muhammad
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 -value of the smallest acceptance region test to be estimated. We prove this -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.

Keywordsbootstrap; cross-market prediction; Markov chain Monte Carlo; multivariate kernel density; p-value; smallest acceptance region tests
Year2020
JournalJournal of Econometrics
Journal citation219 (2), pp. 425-455
PublisherElsevier B.V.
ISSN0304-4076
Digital Object Identifier (DOI)https://doi.org/10.1016/j.jeconom.2020.03.010
Scopus EID2-s2.0-85082676489
Research or scholarlyResearch
Page range425-455
FunderAustralian Research Council (ARC)
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All rights reserved
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Controlled
Output statusPublished
Publication dates
Online30 Mar 2020
Publication process dates
Deposited28 May 2021
ARC Funded ResearchThis output has been funded, wholly or partially, under the Australian Research Council Act 2001
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