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Les méthodes du bootstrap dans les modèles de régression

  • Emmanuel Flachaire

[eng] Bootstrap Methods in Regression Models by Emmanuel Flachaire In practice, we rarely know the true probability distribution of a test statistic and we generally base tests on its asymptotic distribution. If the sample size is not large enough, the asymptotic distribution could be a poor approximation of the true distribution. Consequently, tests based on it could be largely biased. Bootstrap methods yield a more accurate approximation of the distribution of a test statistic than the approximation obtained from the first-order asymptotic theory. Moreover, they provide a way of substituting computation for mathematical analysis when it proves hard to calculate the asymptotic distribution of an estimator or statistic. In this paper, we present a general methodology of the bootstrap in regression models. [fre] 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 biaises. 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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Article provided by Programme National Persée in its journal Économie & prévision.

Volume (Year): 142 (2000)
Issue (Month): 1 ()
Pages: 183-194

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Handle: RePEc:prs:ecoprv:ecop_0249-4744_2000_num_142_1_5996
Note: DOI:10.3406/ecop.2000.5996
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  1. Russell Davidson & Emmanuel Flachaire, 2008. "The wild bootstrap, tamed at last," Post-Print hal-00649250, HAL.
  2. 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.
  3. FLACHAIRE, Emmanuel, 1999. "A better way to bootstrap pairs," CORE Discussion Papers 1999024, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  4. Davidson, Russell & MacKinnon, James G, 1987. "Implicit Alternatives and the Local Power of Test Statistics," Econometrica, Econometric Society, vol. 55(6), pages 1305-29, November.
  5. 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.
  6. Horowitz, Joel L., 1994. "Bootstrap-based critical values for the information matrix test," Journal of Econometrics, Elsevier, vol. 61(2), pages 395-411, April.
  7. Davidson, R. & Mackinnon, J.G., 1996. "The Size Distorsion of Bootstrap Tests," G.R.E.Q.A.M. 96a15, Universite Aix-Marseille III.
  8. Russell Davidson & James MacKinnon, 2000. "Bootstrap tests: how many bootstraps?," Econometric Reviews, Taylor & Francis Journals, vol. 19(1), pages 55-68.
  9. 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.
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