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Nonparametric multidimensional fixed effects panel data models

Abstract: Multidimensional panel datasets are routinely employed to identify marginal effects in empirical research. Fixed effects estimators are typically used 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 a nonparametric estimator that works for essentially any dimension fixed effects model, has a closed form solution and can be estimated in a single step. A cross-validation bandwidth selection procedure is proposed and asymptotic properties (for either a fixed or large time dimension) are given. Finite sample properties are shown via simulations, as well as with an empirical application, which further extends our model to the partially linear setting.

 Fuente: Econometric Reviews, 2022, 41( Issue 3), 321-358

 Editorial: Taylor & Francis

 Año de publicación: 2022

 Nº de páginas: 38

 Tipo de publicación: Artículo de Revista

 DOI: 10.1080/07474938.2021.1957283

 ISSN: 0747-4938,1532-4168

 Proyecto español: PID2019-105986GB-C22 ; APIE 1/2015-17

 Url de la publicación: https://doi.org/10.1080/07474938.2021.1957283