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Heterogeneity and Non-Constant Effect in Two-Stage Quantile Regression

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  • Christophe Muller

    (GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - ECM - École Centrale de Marseille - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - AMU - Aix Marseille Université)

Abstract

Heterogeneity in how some independent variables affect a dependent variable has become a major topic of study in econometrics and statistics. In this respect, this paper addresses the question of constant versus non-constant effect through quantile regression modeling. For linear quantile regression under endogeneity, it is often believed that the fitted- value setting (i.e., replacing endogenous regressors with their exogenous fitted-values) implies constant effect (that is: the coefficients of the covariates do not depend on the considered quantile, except for the intercept). Here, it is shown that, under a weakened instrumental variable restriction, the fitted-value setting can allow for non-constant effect, even though only the constant-effect coefficients of the model can be identified. An application to food demand estimation in 2012 Egypt shows the practical potential of this approach.

Suggested Citation

  • Christophe Muller, 2017. "Heterogeneity and Non-Constant Effect in Two-Stage Quantile Regression," Working Papers halshs-01157552, HAL.
  • Handle: RePEc:hal:wpaper:halshs-01157552
    Note: View the original document on HAL open archive server: https://halshs.archives-ouvertes.fr/halshs-01157552v3
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    Cited by:

    1. Christophe Muller, 2019. "Linear Quantile Regression and Endogeneity Correction," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 9(5), pages 123-128, August.

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