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Les méthodes du bootstrap dans les modèles de régression

  • Emmanuel Flachaire

    ()

    (GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - Université de la Méditerranée - Aix-Marseille II - Université Paul Cézanne - Aix-Marseille III - Ecole des Hautes Etudes en Sciences Sociales (EHESS) - CNRS : UMR6579)

Dans la pratique, la plupart des statistiques de test ont une distribution de probabilité de forme inconnue. Généralement, on utilise leur loi asymptotique comme approximation de la vraie loi. Mais, si l'échantillon dont on dispose n'est pas de taille suffisante cette approximation peut être de mauvaise qualité et les tests basés dessus largement biaisés. Les méthodes du bootstrap permettent d'obtenir une approximation de la vraie loi de la statistique en général plus précise que la loi asymptotique. Elles peuvent également servir à approximer la loi d'une statistique qu'on ne peut pas calculer analytiquement. Dans cet article, nous présentons une méthodologie générale du bootstrap dans le contexte des modèles de régression.

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Paper provided by HAL in its series Post-Print with number halshs-00175894.

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Date of creation: 2001
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Publication status: Published, Economie et Prévision, 2001, 142, 183-194
Handle: RePEc:hal:journl:halshs-00175894
Note: View the original document on HAL open archive server: http://halshs.archives-ouvertes.fr/halshs-00175894/en/
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  1. Davidson, R. & Mackinnon, J.G., 1996. "The Size Distorsion of Bootstrap Tests," G.R.E.Q.A.M. 96a15, Universite Aix-Marseille III.
  2. FLACHAIRE, Emmanuel, 1999. "A better way to bootstrap pairs," CORE Discussion Papers 1999024, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  3. Davidson , R. & Mackinnon, J.G., 1985. "Implicit alternatives and the local power of test statistics," CORE Discussion Papers 1985025, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  4. Dufour, J.M. & Kiviet, J.F., 1995. "Exact Inference Methods for First-Order Autoregressive Distributed Lag Models," Cahiers de recherche 9547, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
  5. Russell Davidson & Emmanuel Flachaire, 2000. "The Wild Bootstrap, Tamed at Last," Econometric Society World Congress 2000 Contributed Papers 1413, Econometric Society.
  6. Freedman, David A & Peters, Stephen C, 1984. "Bootstrapping an Econometric Model: Some Empirical Results," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(2), pages 150-58, April.
  7. Russell Davidson & James MacKinnon, 2000. "Bootstrap tests: how many bootstraps?," Econometric Reviews, Taylor & Francis Journals, vol. 19(1), pages 55-68.
  8. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-38, May.
  9. Horowitz, Joel L., 1994. "Bootstrap-based critical values for the information matrix test," Journal of Econometrics, Elsevier, vol. 61(2), pages 395-411, April.
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