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Frontiers of Economics in China

Front Econ Chin    2012, Vol. 7 Issue (4) : 499-518
Estimation of Censored Regression Model: A Simulation Study
Chunrong Ai1(), Qiong Zhou2()
1. School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai 200433, China; Department of Economics, University of Florida, Gainesville, FL 32611, USA; 2. School of International Business Administration, Shanghai University of Finance and Economics, Shanghai 200433, China
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We investigate the finite sample performance of several estimators proposed for the panel data Tobit regression model with individual effects, including Honoré estimator, Hansen’s best two-step GMM estimator, the continuously updating GMM estimator, and the empirical likelihood estimator (ELE). The latter three estimators are based on more conditional moment restrictions than the Honoré estimator, and consequently are more efficient in large samples. Although the latter three estimators are asymptotically equivalent, the last two have better finite sample performance. However, our simulation reveals that the continuously updating GMM estimator performs no better, and in most cases is worse than Honoré estimator in small samples. The reason for this finding is that the latter three estimators are based on more moment restrictions that require discarding observations. In our designs, about seventy percent of observations are discarded. The insufficiently few number of observations leads to an imprecise weighted matrix estimate, which in turn leads to unreliable estimates. This study calls for an alternative estimation method that does not rely on trimming for finite sample panel data censored regression model.

Keywords panel data      censored regression      finite sample performance      Monte Carlo study     
Corresponding Author(s): Chunrong Ai,; Qiong Zhou,   
Issue Date: 05 December 2012
 Cite this article:   
Chunrong Ai,Qiong Zhou. Estimation of Censored Regression Model: A Simulation Study[J]. Front Econ Chin, 2012, 7(4): 499-518.
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