Tests multiples simulés et tests de normalité basés sur plusieurs moments dans les modèles de régression

Jean-Marie Dufour (), Abdeljelil Farhat () and Lynda Khalaf

Cahiers de recherche from Centre interuniversitaire de recherche en économie quantitative, CIREQ

Abstract: This paper illustrates the usefulness of resampling based methods in the context of multiple (simultaneous) tests, with emphasis on econometric applications. Economic theory often suggests joint (or simultaneous) hypotheses on econometric models; consequently, the problem of evaluating joint rejection probabilities arises frequently in econometrics and statistics. In this regard, it is well known that ignoring the joint nature of multiple hypotheses may lead to serious test size distortions. Whereas most available multiple test techniques are conservative in the presence of non-independent statistics, our proposed tests provably achieve size control. Specifically, we use the Monte Carlo (MC) test technique to extend several well known combination methods to the non-independent statistics contexts. We first cast the multiple test problem into a unified statistical framework which: (i) serves to show how exact global size control is achieved through the MC test method, and (ii) yields a number of superior tests previously not considered. Secondly, we provide a review of relevant available results. Finally, we illustrate the applicability of our proposed procedure to the problem of moments-based normality tests. For this problem, we propose an exact variant of Kiefer and Salmon’s (1983) test, and an alternative combination method which exploits the well known Fisher-Pearson procedure. Our simulation study reveals that the latter method seems to correct for the problem of test biases against platikurtic alternatives. In general, our results show that concrete and non-spurious power gains (over standard combination methods) can be achieved through our multiple Monte Carlo test approach.

Keywords: linear regression; normality test; goodness of fit; skewness; kurtosis; higher moments; Monte Carlo; induced test; test combination; simultaneous inference; Tippett; Fisher; Pearson; SURE; heteroskedasticity test (search for similar items in EconPapers)
JEL-codes: C1 C12 C15 C2 C21 C22 C52 (search for similar items in EconPapers)
Pages: 27 pages
Date: 2005
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.cireqmontreal.com/wp-content/uploads/cahiers/07-2005-cah.pdf (application/pdf)

Related works:
Journal Article: Tests multiples simulés et tests de normalité basés sur plusieurs moments dans les modèles de régression (2020)
Working Paper: Tests multiples simulés et tests de normalité basés sur plusieurs moments dans les modèles de régression (2005)
Working Paper: Tests multiples simulés et tests de normalité basés sur plusieurs moments dans les modèles de régression (2005)
Journal Article: Tests multiples simulés et tests de normalité basés sur plusieurs moments dans les modèles de régression* (2004)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:mtl:montec:07-2005

Access Statistics for this paper

More papers in Cahiers de recherche from Centre interuniversitaire de recherche en économie quantitative, CIREQ Contact information at EDIRC.
Bibliographic data for series maintained by Sharon BREWER ().