Bias Reduction for Dynamic Nonlinear Panel Models with Fixed Effects

The fixed effects estimator of panel models can be severely biased because of the well-known incidental parameter problems. It is shown that such bias can be reduced as T grows with n. We consider asymptotics where n and T grow at the same rate as an approximation that allows us to compare bias properties. Under these asymptotics the bias corrected estimators are centered at the truth, whereas the fixed effects estimator is not. We also show how our alternative asymptotics is related to the higher order “large T” asymptotics.