Bias Reduction for Dynamic Nonlinear Panel Models with Fixed Effects
The fixed effects estimator of panel models can be severely biased because of well-known incidental parameter problems. It is shown that this 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, bias corrected estimators we propose are centered at the truth, whereas fixed effects estimators are not. Our methods are applicable to a wide variety of non-linear dynamic panel models. We discuss several examples and provide Monte Carlo evidence for the small sample performance of our procedure.