Instrumental Variables Estimation of Quantile Treatment Effects
Harvard University - Harvard Kennedy School (HKS); National Bureau of Economic Research (NBER)
Joshua D. Angrist
Massachusetts Institute of Technology (MIT) - Department of Economics; National Bureau of Economic Research (NBER); Institute for the Study of Labor (IZA)
Guido W. Imbens
University of California, Berkeley - Department of Economics; National Bureau of Economic Research (NBER); Institute for the Study of Labor (IZA)
NBER Working Paper No. t0229
This paper introduces an instrumental variables estimator for the effect of a binary treatment on the quantiles of potential outcomes. The quantile treatment effects (QTE) estimator accommodates exogenous covariates and reduces to quantile regression as a special case when treatment status is exogenous. Asymptotic distribution theory and computational methods are derived. QTE minimizes a piecewise linear objective function for which a local minimum can be obtained using a modified Barrodale-Roberts algorithm. The QTE estimator is illustrated by estimating the effect of childbearing on the distribution of family income.
Number of Pages in PDF File: 31working papers series
Date posted: September 15, 2000
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