First-differencing in panel data models with incidental functions

Abstract

I discuss the fixed-effect estimation of panel data models with time-varying excess heterogeneity across cross-sectional units. These latent components are not given a parametric form. A modification to traditional first-differencing is motivated which, asymptotically, removes the permanent unobserved heterogeneity from the differenced model. Conventional estimation techniques can then be readily applied. Distribution theory for a kernel-weighted GMM estimator under large-n and fixed-T asymptotics is developed. The estimator is put to work in a series of numerical experiments to static and dynamic models.jel classification: C13, C14, C33 keywords: dynamic panel data, GMM, incidental functions, local firstdifferencing, time-varying fixed effects, nonparametric heterogeneity.