A Two-Step First Difference Estimator for a Panel Data Tobit Model under Conditional Mean Independence Assumptions

This study develops a two-step estimator for a panel data Tobit model based on taking first-differences of the equation of interest, under conditional mean independence assumptions.The necessary correction terms are non-standard and a substantial part is therefore devoted to the formal derivation of these correction terms.The main advantage of this estimator is that it yields estimates that are far less sensitivity to misspecification of the conditional mean independence assumption than an estimation procedure set up in levels.Monte Carlo simulations are provided in support of this.