In this paper, I calculate the semiparametric information bound
in two dynamic panel data logit models with individual specific
effects. In such a model without any other regressors, it is
well known that the conditional maximum likelihood estimator
yields a √n-consistent estimator. In the case
where the model includes strictly exogenous continuous regressors,
Honoré and Kyriazidou (2000, Econometrica 68,
839–874) suggest a consistent estimator whose rate of
convergence is slower than √n. Information bounds
calculated in this paper suggest that the conditional maximum
likelihood estimator is not efficient for models without
any other regressor and that √n-consistent estimation
is infeasible in more general models.
Asymptotic properties of conditional maximum likelihood estimator in a certain exponential model
