W. H. Greene, “On the Asymptotic Bias of the Ordinary Least Squares Estimator of the Tobit Model,” Econometrica, Vol. 49, No. 2, 1981, pp. 505-514. doi:10.2307/1913323

has been cited by the following article:

• TITLE: A Comparison of the Performance of Various Estimators of Parametric Type1 Tobit Model

  AUTHORS: El Ouali Rahmani, Abdelali Kaaouachi, Said El Melhaoui

  KEYWORDS: Type 1 Tobit Model; Parametric Estimation; Monte Carlo Simulation

  JOURNAL NAME: Open Journal of Statistics, Vol.3 No.1, February 20, 2013

  ABSTRACT: In this paper, we use Monte Carlo simulations to compare parametric estimators of Type 1 Tobit model. In particular, we examine the performance for finite samples of three different estimators of simple Tobit model: the least squares (LS) estimator, the Heckman (H) estimator and the maximum likelihood (ML) estimator. These three estimators are consistent and asymptotically normal in the case where the density error is specified. However, these properties are sensitive to the situation where the error distribution is not specified. The purpose of this article is to determine properties of the three estimators, namely bias and convergence, by using Monte Carlo simulations.