Abstract :
We study a nonlinear panel data model in which the fixed effects are assumed to
have finite support. The fixed effects estimator is known to have the incidental parameters
problem.We contribute to the literature by making a qualitative observation
that the incidental parameters problem in this model may not be not as severe as in
the conventional case. Because fixed effects have finite support, the probability of
correctly identifying the fixed effect converges to one even when the cross sectional
dimension grows as fast as some exponential function of the time dimension. As a
consequence, the finite sample bias of the fixed effects estimator is expected to be
small.
