Simulation-Based Finite and Large Sample Tests in Multivariate Regressions

Jean-Marie Dufour () and Lynda Khalaf ()

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

Abstract: In the context of multivariate linear regression (MLR) models, it is well known that commonly employed asymptotic test criteria are seriously biased towards overrejection. In this paper, we propose a general method for constructing exact tests of possibly nonlinear hypotheses on the coefficients of MLR systems.

Keywords: LINEAR MODEL; REGRESSION ANALYSIS; TESTS (search for similar items in EconPapers)
JEL-codes: C20 C15 C42 (search for similar items in EconPapers)
Date: Written 2000

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
Working Paper: Simulation-Based Finite and Large Sample Tests in Multivariate Regressions (2000)
Working Paper: Simulation Based Finite and Large Sample Tests in Multivariate Regressions (2000)
Journal Article: Simulation based finite and large sample tests in multivariate regressions (2002)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Access Statistics for this paper

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