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ON THE FINITE SAMPLE PERFORMANCE OF SOME ESTIMATORS FOR LEFT-CENSORED REGRESSION MODEL
Keywords:
left-censored data, Tobit, CLAD, multiple imputation.DOI:
https://doi.org/10.17654/0972361722084Abstract
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.
Received: October 3, 2022
Accepted: November 5, 2022
References
T. Amemiya, Regression analysis when the dependent variable is truncated normal, Econometrica 41(6) (1973), 997-1016.
T. Amemiya, Tobit models: a survey, J. Econometrics 24(1-2) (1984), 3-61.
A. Arabmazar and P. Schmidt, An investigation of the robustness of the Tobit estimator to non-normality, Econometrica 50(4) (1982), 1055-1063.
M. Buchinsky, Changes in the U.S. wage structure 1963-1987: application of quantile regression, Econometrica 62 (1994), 405-458.
T. Büttner, Multiple imputation of censored variables, PhD dissertation, University of Bamberg, 2010. Available at:
http://www.opus-bayern.de/uni-bamberg/volltexte/2010/273/
[Accessed 05 May 2011].
K. Y. Chay and J. L. Powell, Semiparametric censored regression models, Journal of Economic Perspectives 15(4) (2001), 29-42.
D. R. Helsel, Statistics for Censored Environmental Data Using Minitab and R, John Wiley & Sons, Vol. 77, 2011.
B. R. Humphreys, Dealing with zeros in economic data, University of Alberta, Department of Economics, 2013, pp. 1-27.
R. Koenker, Censored quantile regression redux, Journal of Statistical Software 27(6) (2008), 1-25.
J. B. McDonald and H. Nguyen, Heteroscedasticity and distributional assumptions in the censored regression model, Comm. Statist. Simulation Comput. 44(8) (2015), 2151-2168.
J. L. Powell, Least absolute deviations estimation for the censored regression model, J. Econometrics 25(3) (1984), 303-325.
P. Royston, Multiple imputation of missing values, The Stata Journal 4(3) (2004), 227-241.
D. B. Rubin, Multiple Imputation for Nonresponse in Surveys, John Wiley & Sons, Vol. 81, 2004.
J. Tobin, Estimation of relationships for limited dependent variables. Econometrica 26(1) (1958), 24-36.
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