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Bootstrap inference in econometrics

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  • James G. MacKinnon

Abstract

The astonishing increase in computer performance over the past two decades has made it possible for economists to base many statistical inferences on simulated, or bootstrap, distributions rather than on distributions obtained from asymptotic theory. In this paper, I review some of the basic ideas of bootstrap inference. I discuss Monte Carlo tests, several types of bootstrap test, and bootstrap confidence intervals. Although bootstrapping often works well, it does not do so in every case. Inférence par la méthode d’auto–amorçage (bootstrap) en économétrie. L’incroyable accroissement dans la puissance des ordinateurs au cours des deux dernières décennies a permis aux économistes de fonder plusieurs inférences sur des distributions simulées, ou obtenues par auto–amorçage, plutôt que sur des distributions obtenues par la théorie aymptotique. Dans ce texte, l’auteur passe en revue quelques–unes des idées de base de l’inférence par la méthode d’auto–amorçage. Le texte discute aussi des tests de Monte Carlo, de divers types de tests et des intervalles de confiance obtenus par la méthode d’auto–amorçage. Même si le processus d’auto–amorçage fonctionne souvent bien, cela n’est pas toujours le cas.

Suggested Citation

  • James G. MacKinnon, 2002. "Bootstrap inference in econometrics," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 35(4), pages 615-645, November.
  • Handle: RePEc:wly:canjec:v:35:y:2002:i:4:p:615-645
    DOI: 10.1111/0008-4085.00147
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