Abstract: Censoring of the dependent variable is a very common problem with micro data where under certain conditions, only the observable values of the dependent variables are the ones being recorded and not the true data points. Thus, ignoring censoring will generally lead to inconsistent estimators [2, 9]. As a result, the standard ordinary least squares regressions can come up with bias and inconsistent estimates [7-9]. In handling censored data, the commonly used estimators in the literatures were Tobit and CLAD. However, they were only tested for interval-censored data [8]. This paper extends the work to left-censored data and presents the finite sample performances of these estimators for left-censored regression model including the new estimator which is based from a new imputation approach. Simulation results showed that as in the case of normal errors in the univariate regression case, the Tobit, CLAD, and the new estimator were consistent estimators for left censored regression model. It is also showed that Tobit is superior among the others when the errors are normal. Results also showed that the CLAD estimator is consistent for normal errors. Meanwhile, the new imputation approach produced a consistent estimator for normal errors. Although, it did not out performed Tobit and CLAD, it showed a favourable results in terms of its variance as n gets higher.
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Keywords and phrases: left-censored data, Tobit, CLAD, multiple imputation.
Received: October 3, 2022; Accepted: November 5, 2022; Published: November 12, 2022
How to cite this article: Jun Mark Rey O. Nob and Kennet G. Cuarteros, On the finite sample performance of some estimators for left-censored regression model, Advances and Applications in Statistics 83 (2022), 27-39. http://dx.doi.org/10.17654/0972361722084
This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License
References:
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