Estimation of counterfactual distributions using quantile regression
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abstract
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This paper proposes estimators of unconditional distribution
functions in the presence of covariates. The methods are based on
the estimation of the conditional distribution by (parametric or
nonparametric) quantile regression. The conditional distribution is
then integrated over the range of the covariates, allowing for the
estimation of counterfactual distributions. In the parametric
settings, we propose an extension of the Oaxaca (1973) / Blinder
(1973) decomposition of means to the full distribution. In the
nonparametric setting, we develop an efficient local-linear
regression estimator for quantile treatment effects. We show root n
consistency and asymptotic normality of the estimators and present
analytical estimators of their variance. Monte-Carlo simulations
show that the procedures perform well in finite samples. An
application to the black-white wage gap illustrates the usefulness
of the estimators.
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type
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discussion paper (English)
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keywords
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Quantile Regression, Quantile Treatment Effect, Oaxaca / Blinder Decomposition, Wage Differentials, Racial Discrimination
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date of appearance
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10-2-2006
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page(s)
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50
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review
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not reviewed
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citation
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Melly, B. (2006). Estimation of counterfactual distributions using
quantile regression.
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