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A Comparison of the Performance of Various Estimators of Parametric Type1 Tobit Model
University of Mohammed first (FS), Oujda, Morocco
University of Mohammed first (EST), Oujda, Morocco
University of Mohammed first (FSJES), Oujda, Morocco
- 1 University of Mohammed first (FS), Oujda, Morocco
- 2 University of Mohammed first (EST), Oujda, Morocco
- 3 University of Mohammed first (FSJES), Oujda, Morocco
Open Journal of Statistics·Volume 03 (2013)·Pages 1–4·Published 20 February 2013·DOI10.4236/ojs.2013.31001
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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.
KeywordsType 1 Tobit ModelParametric EstimationMonte Carlo Simulation
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