Estimation of an unbalanced panel data Tobit model with interactive effects

Unbalanced panel data or panel data with missing observations are common in empirical research. In this paper, we consider an unbalanced panel data Tobit model with interactive effects, and provide an estimator based on the iteration of Tobit factor analysis and maximum likelihood estimation. Monte Carlo studies are carried out to investigate the finite sample performance of the proposed method in comparison with other candidate methods. The results show that the finite sample performance of the proposed method is satisfactory under different Mont Carlo designs. We also apply our method to study female labor supply using an unbalanced panel data set from the Chinese Family Panel Studies (CFPS).