Nonparametric censored and truncated regression

Arthur Lewbel and Oliver Linton

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: The nonparametric censored regression model, with a fixed, known censoring point (normalized to zero), is y = max[0,m(x) + e], where both the regression function m(x) and the distribution of the error e are unknown. This paper provides estimators of m(x) and its derivatives. The convergence rate is the same as for an uncensored nonparametric regression and its derivatives. We also provide root n estimates of weighted average derivatives of m(x), which equal the coefficients in linear or partly linearr specifications for m(x). An extension permits estimation in the presence of a general form of heteroscedasticity. We also extend the estimator to the nonparametric truncated regression model, in which only uncensored data points are observed. The estimators are based on the relationship E(yk\x)/m(x) = kE[yk-1/(y > 0)x ], which we show holds for positive integers k.

Keywords: Semiparametric; nonparametric; censored regression; truncated regression; Tobit; latent variable (search for similar items in EconPapers)
JEL-codes: C13 C14 C24 (search for similar items in EconPapers)
Pages: 35 pages
Date: 2000-04
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Citations: View citations in EconPapers (1)

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http://eprints.lse.ac.uk/2060/ Open access version. (application/pdf)

Related works:
Journal Article: Nonparametric Censored and Truncated Regression (2002)
Working Paper: Nonparametric Censored and Truncated Regression (2000)
Working Paper: Nonparametric Censored and Truncated Regression (2000)
Working Paper: Nonparametric Censored and Truncated Regression (2000)
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:2060

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