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

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  • DUFOUR, Jean-Marie
  • KHALAF, Lynda

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.

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File URL: http://hdl.handle.net/1866/462
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Bibliographic Info

Paper provided by Universite de Montreal, Departement de sciences economiques in its series Cahiers de recherche with number 9813.

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Length: 32 pages
Date of creation: 1998
Date of revision:
Handle: RePEc:mtl:montde:9813

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Cited by:
  1. Dufour, Jean-Marie & Khalaf, Lynda, 2001. "Finite-Sample Simulation-Based Tests in Seemingly Unrelated Regressions," Cahiers de recherche 0111, Université Laval - Département d'économique.
  2. Khalaf, Lynda & Saphores, Jean-Daniel & Bilodeau, Jean-François, 2000. "Simulation-Based Exact Tests with Unidentified Nuisance Parameters under the Null Hypothesis : the Case of Jumps Tests in Model with Conditional Heteroskedasticity," Cahiers de recherche 0004, Université Laval - Département d'économique.
  3. Dufour, J.M. & Khalaf, L., 2000. "Simulation-Based Finite and Large Sample Tests in Multivariate Regressions," Cahiers de recherche 2000-10, Centre interuniversitaire de recherche en économie quantitative, CIREQ.

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