Semiparametric Estimation of a Censored Regression Model with an Unknown Transformation of the Dependent Variable

Tue Gorgens and Joel Horowitz ()
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Tue Gorgens: Univ. of Iowa

Econometrics from University Library of Munich, Germany

Abstract: In this paper we develo psemiparametric estimators of L and y in the model L(Y) = min[b›X + U,C], where Y is a nonnegative dependent variable, X is a vector of explanatory variables, U is an unobserved random "error" term with unknown distribution function y, C is a random censoring variable, b is an unknown parameter vector, and L is an unknown strictly increasing function. This model includes as a special case the censored proportional hazards model with unobserved heterogeneity. Estimators of L and y already exist for the case where either L or y belongs to a known finite-dimensional parametric family, and methods for estimating b exist for the general case. In this paper we propose estimators of L and y which do not assume that L and y belong to known parametric families. We obtain their asymptotic distributions and investigate the small sample properties of the estimators by Monte Carlo simulation.

JEL-codes: C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Pages: 39 pages
Date: 1996-03-05
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Related works:
Journal Article: Semiparametric estimation of a censored regression model with an unknown transformation of the dependent variable (1999)
Working Paper: Semiparametric Estimation of a Censored Regression Model with an Unknown Transformation of the Dependent Variable (1995)
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