Nonparametric Censored Regression

Arthur Lewbel and Oliver Linton

No 1186, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: The nonparametric censored regression model is y = max[c, m(x) + e], where both the regression function m(x) and the distribution of the error e are unknown, but the fixed censoring point c is known. This paper provides a simple consistent estimator of the derivative of m(x) with respect to each element of x. The convergence rate of this estimator is the same as for the derivatives of an uncensored nonparametric regression. We then estimate the regression function itself by solving the associated partial differential equation system. We show that our estimator of m(x) achieves the same rate of convergence as the usual estimators in uncensored nonparametric regression. We also provide root n estimates of weighted average derivatives of m(x), which equal the coefficients in any linear or partly linear specification for m(x).

Keywords: Semiparametric; nonparametric; censored regression; Tobit; latent variable (search for similar items in EconPapers)
JEL-codes: C13 C14 C24 (search for similar items in EconPapers)
Pages: 24 pages
Date: 1998-07
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Published in Econometrica (2002), 70: 765-780

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