Estimation of counterfactual distributions using quantile regression
Type
discussion paper
Date Issued
2006-02-10
Author(s)
Melly, Blaise
Abstract
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.
Language
English
Keywords
Quantile Regression
Quantile Treatment Effect
Oaxaca / Blinder Decomposition
Wage Differentials
Racial Discrimination
HSG Classification
contribution to scientific community
Refereed
No
Start page
50
Subject(s)
Division(s)
Eprints ID
22644
File(s)![Thumbnail Image]()
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Name
Monte Carlo internet.txt
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23.92 KB
Format
Text
Checksum (MD5)
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Name
Counterfactual.pdf
Size
1.29 MB
Format
Adobe PDF
Checksum (MD5)
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