A Least Squares Estimation in Truncated Linear Regression

Abstract

We investigate least squares estimation for regression coefficients of the covariates in the multiple linear regression model with truncated data and propose a consistent least squares estimator alternative to the existing ones. The estimator is proved to be asymptotic normal distribution with the same asymptotic variance matrix of the estimator proposed by Lai and Ying (1992b). However, the estimator is much simpler in computation than Lai and Ying's estimator. The estimation procedure does not require to calculate the nonparametric estimate of the error distribution. A simulation study shows that the estimator performs well even with moderate sample size.