Bandwidth Choice for Bias Estimators in Dynamic Nonlinear Panel Models

This paper considers bandwidth selection for spectral density estimators based on panel data sets. The spectral densities of greatest interest in this paper are the ones that appear in the bias expression for …xed e¤ects estimators in nonlinear dynamic panel models obtained by Hahn and Kuersteiner. The bias estimation problem is di¤erent from the usual HAC estimation problem because the need for positive de…niteness does not arise. As a consequence, the usual justi…cation for kernel smoothing of spectral estimators does not apply to this case. However, without kernel smoothing the bandwidth selection problem is signi…cantly more di¢ cult because in this case not only the usual proportionality constants are data-dependent but also the optimal rate at which the bandwidth parameter grows with sample size. In this paper an in…nite order VAR model is used to obtain an estimate of the approximate mean squared error of the spectral estimator. It is shown that selecting the bandwidth parameter based on the estimated mean squared error criterion is asymptotically equivalent to the optimal infeasible bandwidth choice. Monte Carlo simulations show that truncated spectral estimates signi…cantly outperform kernel weighted estimates in terms of their e¤ectiveness in reducing bias in the panel application.