Simulation-Based Finite-and Large-sample Inference Methods in Multivariate Regressions and Seemingly Unrelated Regressions

Jean-Marie Dufour (jean-marie.dufour@mcgill.ca) and Lynda Khalaf

Cahiers de recherche from Universite de Montreal, Departement de sciences economiques

Abstract: In the context of multivariate regression (MLR) and seemingly unrelated regressions (SURE) models, it is well known that commonly employed asymptotic test criteria are seriously biased towards overrejection. in this paper, we propose finite-and large-sample likelihood-based test procedures for possibly non-linear hypotheses on the coefficients of MLR and SURE systems.

JEL-codes: C20 C21 C40 (search for similar items in EconPapers)
Pages: 32 pages
Date: 1998
References: Add references at CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://hdl.handle.net/1866/462 (application/pdf)

Related works:
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:montde:9813

Access Statistics for this paper

More papers in Cahiers de recherche from Universite de Montreal, Departement de sciences economiques Contact information at EDIRC.
Bibliographic data for series maintained by Sharon BREWER (sharon.brewer@umontreal.ca).