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Nonparametric Censored and Truncated Regression

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  • Arthur Lewbel

    ()
    (Dept. of Economics, Boston College, 140 Commonwealth Avenue, Chestnut Hill, MA 02467, U.S.A.)

  • Oliver Linton

    ()
    (Dept. of Economics, London School of Economics, Houghton Street, London WC2A 2AE, United Kingdom)

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.

(This abstract was borrowed from another version of this item.)

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Bibliographic Info

Article provided by Econometric Society in its journal Econometrica.

Volume (Year): 70 (2002)
Issue (Month): 2 (March)
Pages: 765-779

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Handle: RePEc:ecm:emetrp:v:70:y:2002:i:2:p:765-779

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