Heteroskedasticity and Distributional Assumptions in the Censored Regression Model

James McDonald and Hieu Nguyen ()
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Hieu Nguyen: Department of Economics, Brigham Young University

No 2012-09, BYU Macroeconomics and Computational Laboratory Working Paper Series from Brigham Young University, Department of Economics, BYU Macroeconomics and Computational Laboratory

Abstract: Data censoring causes ordinary least squares estimators of linear models to be biased and inconsistent. The Tobit estimator yields consistent estimators in the presence of data censoring if the errors are normally distributed. However, non-normality or heteroskedasticity results in the Tobit estimators being inconsistent. Various estimators have been proposed for circumventing the normality assumption. Some of these estimators include censored least absolute deviations (CLAD), symmetrically censored least squares (SCLS), and partially adaptive estimators. CLAD and SCLS will be consistent in the presence of heteroskedasticity; however, SCLS performs poorly in the presence of asymmetric errors. This paper extends the partially adaptive estimation approach to accommodate possible heteroskedasticity as well as non-normality. A simulation study is used to investigate the estimators’ relative efficiency in these settings. The partially adaptive censored regression estimators have little efficiency loss for censored normal errors and appear to outperform the Tobit and semiparametric estimators for non-normal error distributions and be less sensitive to the presence of heteroskedasticity. An empirical example is considered which supports these results.

Keywords: censored regression; Tobit; partially adaptive estimators; heteroskedasticity; non-normality (search for similar items in EconPapers)
JEL-codes: C34 (search for similar items in EconPapers)
Pages: 24 pages
Date: 2012-08
New Economics Papers: this item is included in nep-ecm and nep-ore
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