Estimating non-linear models with multiple fixed effects:a computational note

In this paper we consider estimation of nonlinear panel data models that include multiple individual fixed effects. Estimation of these models is complicated both by the diffi culty of estimating models with possibly thousands of coeffi cients and also by the incidental parameters problem; that is, noisy estimates of the fi xed effects when the time dimension is short contaminate the estimates of the common parameters due to the nonlinearity of the problem. We propose a simple variation of existing bias-corrected estimators, which can exploit the additivity of the effects for numerical optimization. We exhibit the performance of the estimators in simulations.