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La régression quantile en pratique

Author

Listed:
  • P. GIVORD

    (Insee-Crest)

  • X. DHAULTFOEUILLE

    (Crest)

Abstract
Quantile regressions are statistical tools that describe the impact of explanatory variables on a variable of interest. They provide a more detailed picture than classic linear regression, as they focus on the entire conditional distribution of the dependent variable, not only on its mean. They are also more suited to some kind of data such as truncated and censored dependent variable, outcomes with fat-tailed distributions, nonlinear models... This document proposes a practical introduction to these tools, with a special interest on their implementation in standard statistical software (Sas, R, Stata). We also present in details two empirical applications, to help people interpreting studies that rely on these methods. Finally, we propose for more advanced readers recent extensions in particular on endogeneity issues (instrumental variables, panel data...).

Suggested Citation

  • P. Givord & X. Dhaultfoeuille, 2013. "La régression quantile en pratique," Documents de Travail de l'Insee - INSEE Working Papers m2013-01, Institut National de la Statistique et des Etudes Economiques.
    • Handle: RePEc:nse:doctra:m2013-01
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    File URL: http://www.insee.fr/fr/publications-et-services/docs_doc_travail/doc_regression_quantile.pdf
    File Function: Document de travail "Méthodologie Statistique" de la DMCSI numéro M2013/01
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    References listed on IDEAS

    as
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    Cited by:

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    2. Issofou NJIFEN & Aicha PEMBOURA, 2020. "Hétérogénéité dans les rendements de l’éducation au Cameroun : une estimation en présence des biais de sélection et d’endogénéité," Region et Developpement, Region et Developpement, LEAD, Universite du Sud - Toulon Var, vol. 52, pages 105-126.
    3. Voyant, Cyril & Motte, Fabrice & Notton, Gilles & Fouilloy, Alexis & Nivet, Marie-Laure & Duchaud, Jean-Laurent, 2018. "Prediction intervals for global solar irradiation forecasting using regression trees methods," Renewable Energy, Elsevier, vol. 126(C), pages 332-340.
    4. Nadine Levratto & Aziza Garsaa & Luc Tessier, 2013. "To what extent do exemptions from social security contributions affect firm growth? New evidence using quantile estimations on panel data," Working Papers hal-00833049, HAL.
    5. Matthieu Chtioui Cepn & Nadine Levratto, 2020. "Fiscalité locale et dynamique d'emploi des territoires : analyse empirique sur les communes françaises (Version preprint) A paraitre dans la Revue d'Economie Régionale et urbaine, 2021," Working Papers halshs-02901499, HAL.
    6. Pauline Givord & Milena Suarez Castillo, 2021. "What Makes a Good High School? Measuring School Effects beyond the Average," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 528-529, pages 29-45.
    7. 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.
    8. B. Garbinti & P. Lamarche, 2014. "Do the High-Income Households Save More?," Documents de Travail de l'Insee - INSEE Working Papers g2014-10, Institut National de la Statistique et des Etudes Economiques.
    9. 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.
    10. Salomé Bakaloglou & Dorothée Charlier, 2018. "The role of individual preferences to explain the energy performance gap," Policy Papers 2018.08, FAERE - French Association of Environmental and Resource Economists.
    11. Bertrand Garbinti & Pierre Lamarche, 2014. "Les hauts revenus épargnent‑ils davantage ?," Économie et Statistique, Programme National Persée, vol. 472(1), pages 49-64.
    12. T. Razafindranovona, 2016. "Exploitation de l'enquête expérimentale Logement internet/papier," Document de travail "Methodologie Statistique" - DMS Working Paper m2016-08, Institut National de la Statistique et des Etudes Economiques.
    13. Bakaloglou, Salomé & Charlier, Dorothée, 2021. "The role of individual preferences in explaining the energy performance gap," Energy Economics, Elsevier, vol. 104(C).
    14. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2018. "Économétrie & Machine Learning," Working Papers hal-01568851, HAL.
    15. Ben Rejeb, Aymen, 2017. "On the volatility spillover between lslamic and conventional stock markets: A quantile regression analysis," Research in International Business and Finance, Elsevier, vol. 42(C), pages 794-815.
    16. Jamal Bouoiyour & Refk Selmi, 2017. "The Bitcoin price formation: Beyond the fundamental sources," Working Papers hal-01548710, HAL.
    17. Maichanou, Ahamadou & Dan Baky, Agada, 2022. "Private Intra-household Transfers as a Palliative for the Incompleteness of Social Protection: Evidence from Niger," African Journal of Economic Review, African Journal of Economic Review, vol. 10(2), March.
    18. Clara Champagne & Ariane Pailhé & Anne Solaz, 2015. "Le temps domestique et parental des hommes et des femmes : quels facteurs d'évolutions en 25 ans ?," Économie et Statistique, Programme National Persée, vol. 478(1), pages 209-242.

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    More about this item

    Keywords

    Quantile Regression; Quantile Treatment Effect; Instrumental Variable Quantile Regression; Quantile Regression with panel data.;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables

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