A Penalty Function Approach to Bias Reduction in Nonlinear Panel Models with Fixed Effects

Abstract

We consider estimation of nonlinear panel data models with individual specific fixed effects. Estimation of these models is complicated since estimation of the fixed effects when the time dimension is short generally results in inconsistent estimates of all model parameters. We present a penalized objective function that reduces the bias in the resulting point estimates. The penalty function is simple to construct and requires no modification for models with multiple individual specific parameters. We illustrate the approach through a series of simulations that suggest the approach is effective in reducing bias and in an empirical study of insider trading activity.