Semiparametric estimation of a censored regression model with an unknown transformation of the dependent variable

Tue Gørgens, Joel L. Horowitz*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

This paper presents a method for estimating the model Λ(Y) = min(β′X + U, C), where Y is a scalar, A is an unknown increasing function, X is a vector of explanatory variables, β is a vector of unknown parameters, U has unknown cumulative distribution function F, and C is a censoring threshold. It is not assumed that A and F belong to known parametric families; they are estimated nonparametrically. This model includes many widely used models as special cases, including the proportional hazards model with unobserved heterogeneity. The paper develops n1/2-consistent, asymptotically normal estimators of Λ and F. Estimators of β that are n1/2-consistent and asymptotically normal already exist. The results of Monte Carlo experiments illustrate the finite-sample behavior of the estimators.

Original languageEnglish (US)
Pages (from-to)155-191
Number of pages37
JournalJournal of Econometrics
Volume90
Issue number2
DOIs
StatePublished - Jun 1999

Keywords

  • Empirical process
  • Kaplan-meier estimator
  • Proportional hazards model
  • Semiparametric estimation
  • Transformation model

ASJC Scopus subject areas

  • Economics and Econometrics

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