Affairs"
BY: VICTOR CHERNOZHUKOV
Massachusetts Institute of Technology (MIT)
Department of Economics
HAN HONG
Princeton University
Department of Economics
Document: Available from the SSRN Electronic Paper Collection:
http://papers.ssrn.com/paper.taf?abstract_id=272499
Paper ID: MIT Dept. of Economics Working Paper No. 01-20
Date: March 2001
Contact: VICTOR CHERNOZHUKOV
Email: Mailto:vchern at mit.edu
Postal: Massachusetts Institute of Technology (MIT)
Department of Economics
50 Memorial Drive
Cambridge, MA 02142 USA
Phone: 617-253-4767
Fax: 617-253-1330
Co-Auth: HAN HONG
Email: Mailto:doubleh at princeton.edu
Postal: Princeton University
Department of Economics
202-26 Prospect Avenue
Princeton, NJ 08544 USA
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ABSTRACT:
This paper suggests simple 3- and 4-step estimators for censored
quantile regression models with an envelope or a separation
restriction on the censoring probability. The estimators are
theoretically attractive (asymptotically as efficient as the
celebrated Powell's censored least absolute deviation
estimator). At the same time, they are conceptually simple and
have trivial computational expenses. They are especially useful
in samples of small size or models with many regressors, with
desirable finite sample properties and small bias. The envelope
restriction costs a small reduction of generality relative to
the canonical censored regression quantile model, yet its main
plausible features remain intact. The estimator can also be used
to estimate a large class of traditional models, including
normal Amemiya-Tobin model and many accelerated failure and
proportional hazard models. The main empirical example involves
a very large data-set on extramarital affairs, with high 68%
censoring. We estimate 45%-90% conditional quantiles. Effects of
covariates are not representable as location-shifts. Less
religious women, with fewer children, and higher status, tend to
engage into the matters relatively more than their opposites,
especially at the extremes. Marriage longevity effect is
positive at moderately high quantiles and negative at high
quantiles. Education and marriage happiness effects are
negative, especially at the extremes. We also briefly consider
the survival quantile regression on the Stanford heart
transplant data. We estimate the age and prior surgery effects
across survival quantiles.
Keywords: Quantile regression, median regression, censoring,
duration, survival, classification, discriminant analysis
JEL Classification: C14, C24, C41, C51, D13