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A1632
Title: Nonparametric multi-dimensional fixed effects panel data models Authors:  Alexandra Soberon - Universidad de Cantabria (Spain) [presenting]
Daniel Henderson - University of Alabama (United States)
Juan Manuel Rodriguez-Poo - Universidad de Cantabria (Spain)
Abstract: Multi-dimensional panel data sets are routinely employed to identify marginal effects in empirical research. Fixed effects estimators are typically used in order to deal with potential correlation between unobserved effects and regressors. Nonparametric estimators for one-way fixed effects models exist, but are cumbersome to employ in practice as they typically require iteration, marginal integration or profile estimation. We develop nonparametric estimators for the gradient of the conditional mean that work for essentially any dimension fixed effects model, have closed-form solutions and can be estimated in a single-step. Cross-validation bandwidth selection procedures are proposed and the asymptotic properties (for a fixed or large time dimension) of our estimators are given. Finite sample properties are shown via simulations, as well as with an empirical application which further extends our estimators to the partially linear setting.