A maximum likelihood estimator based on first differences for a panel data Tobit model with individual specific effects

This paper proposes a new estimator for a panel data Tobit model in which the unobserved individual specific effects are allowed to correlate with the explanatory variables. A maximum likelihood estimator is based on taking first differences of the equation of interest. This helps to alleviate the sensitivity of the estimates to a specific parameterization of the individual specific effects and some Monte Carlo evidence is provided in support of this.