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

Christophe Muller ()
Authors registered in the RePEc Author Service: Tae-Hwan Kim ()

Working Papers from HAL

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.

Keywords: quantile regression; fitted-value setting; two-stage estimation; non constant effect; partial identification (search for similar items in EconPapers)
Date: 2017-02
Note: View the original document on HAL open archive server: https://halshs.archives-ouvertes.fr/halshs-01157552v3
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Related works:
Journal Article: Heterogeneity and nonconstant effect in two-stage quantile regression (2018) Downloads
Working Paper: Heterogeneity and nonconstant effect in two-stage quantile regression (2018)
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