A distribution-free least squares estimator for censored linear regression models

Joel L. Horowitz*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

38 Scopus citations

Abstract

This paper describes a method for estimating simultaneously the parameter vector of the systematic component and the distribution function of the random component of a censored linear regression model. The estimator is obtained by minimizing the sum of the squares of the differences between the observed values of the dependent variable and the corresponding expected values of this variable according to the estimated parameter vector and distribution function. The resulting least squares parameter estimator incorporates information on the distribution of the random component of the regression model that is available from the estimation sample. Hence, it may often be more efficient than are parameter estimators that do not use such information. The results of numerical experiments with the least squares estimator tend to support this hypothesis.

Original languageEnglish (US)
Pages (from-to)59-84
Number of pages26
JournalJournal of Econometrics
Volume32
Issue number1
DOIs
StatePublished - Jun 1986

ASJC Scopus subject areas

  • Economics and Econometrics

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