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La régression quantile en pratique

Author

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  • Xavier D’Haultfoeuille
  • Pauline Givord

Abstract

[fre] L’usage des régressions quantiles s’est beaucoup répandu au cours de la dernière décennie. Celles-ci reposent sur un principe proche de celui de la régression linéaire classique. De même que cette dernière se fonde sur une modélisation linéaire de l’espérance conditionnelle de la variable d’intérêt en fonction de ses déterminants, les régressions quantiles consistent à supposer que les quantiles conditionnels de cette variable d’intérêt sont linéaires. Elles fournissent cependant une description plus riche que les régressions linéaires, puisqu’on peut ainsi étudier l’ensemble de la distribution conditionnelle de la variable d’intérêt et non seulement la moyenne de celle-ci. Cette analyse est particulièrement intéressante pour les mesures d’évaluation des politiques publiques : un programme peut avoir un effet moyen limité, mais permettre d’augmenter suffisamment les niveaux les plus faibles de la variable d’intérêt pour que son implémentation soit souhaitable. Les régressions quantiles permettent également de décrire les déterminants des évolutions des inégalités de revenu. En outre, elles sont parfois plus adaptées pour certains types de données (variables censurées ou tronquées, présence de valeurs extrêmes, modèles non linéaires...). Les régressions quantiles peuvent être aujourd’hui effectuées aisément avec de nombreux logiciels statistiques. Cet article rappelle les principes statistiques sous-jacents à cette modélisation, ainsi que des extensions qui ont été développées pour répondre au problème, classique en économétrie, de l’endogénéité de certaines variables explicatives (données de panel, variables instrumentales…). Il fournit également un guide d’interprétation des résultats d’une régression quantile, dont l’analyse est peut-être moins intuitive que celle d’une régression linéaire. Pour bien comprendre l’utilisation qui peut en être faite, deux applications concrètes sont présentées à titre d’illustration.

Suggested Citation

  • Xavier D’Haultfoeuille & Pauline Givord, 2014. "La régression quantile en pratique," Économie et Statistique, Programme National Persée, vol. 471(1), pages 85-111.
  • Handle: RePEc:prs:ecstat:estat_0336-1454_2014_num_471_1_10484
    Note: DOI:10.3406/estat.2014.10484
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    References listed on IDEAS

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    Cited by:

    1. Bertrand Garbinti & Pierre Lamarche, 2014. "Les hauts revenus épargnent‑ils davantage ?," Économie et Statistique, Programme National Persée, vol. 472(1), pages 49-64.
    2. Jamal Bouoiyour & Refk Selmi, 2017. "The Bitcoin price formation: Beyond the fundamental sources," Papers 1707.01284, arXiv.org.
    3. Ben Rejeb, Aymen, 2016. "Volatility Spillover between Islamic and conventional stock markets: evidence from Quantile Regression analysis," MPRA Paper 73302, University Library of Munich, Germany.
    4. S. Béreau & V. Faubert & K. Schmidt, 2018. "Explaining and Forecasting Euro Area Inflation: the Role of Domestic and Global Factors," Working papers 663, Banque de France.
    5. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2018. "Économétrie & Machine Learning," Working Papers hal-01568851, HAL.
    6. repec:eee:riibaf:v:42:y:2017:i:c:p:794-815 is not listed on IDEAS

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