Semiparametric Estimation of a Censored Regression Model with an Unknown Transformation of the Dependent Variable
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.
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|Date of creation:||1995|
|Date of revision:|
|Contact details of provider:|| Postal: University of Iowa, Department of Economics, Henry B. Tippie College of Business, Iowa City, Iowa 52242|
Phone: (319) 335-0829
Fax: (319) 335-1956
Web page: http://tippie.uiowa.edu/economics/
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