Estimation of Censored Regression Model: A Simulation Study

Chunrong Ai () and Qiong Zhou ()
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Chunrong Ai: School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai 200433, China; Department of Economics, University of Florida, Gainesville, FL 32611, USA
Qiong Zhou: School of International Business Administration, Shanghai University of Finance and Economics, Shanghai 200433, China

Frontiers of Economics in China-Selected Publications from Chinese Universities, 2012, vol. 7, issue 4, 499-518

Abstract: We investigate the finite sample performance of several estimators proposed for the panel data Tobit regression model with individual effects, including Honor¨¦ 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 Honor¨¦ 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 Honor¨¦ 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 (search for similar items in EconPapers)
JEL-codes: C24 C6 (search for similar items in EconPapers)
Date: 2012
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