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Empirical likelihood based inference for fixed effects varying coefficient panel data models

Abstract: In this paper local empirical likelihood-based inference for non-parametric varying coefficient panel data models with fixed effects is investigated. First, we show that the naive empirical likelihood ratio is asymptotically standard chi-squared when undersmoothing is employed. The ratio is self-scale invariant and the plug-in estimate of the limiting variance is not needed. Second, mean-corrected and residual-adjusted empirical likelihood ratios are proposed. The main interest of these techniques is that without undersmoothing, both also have standard chi-squared limit distributions. As a by product, we propose also two empirical maximum likelihood estimators of the varying coefficient models and their derivatives. We also obtain the asymptotic distribution of these estimators. Furthermore, a non parametric version of the Wilk?s theorem is derived. To show the feasibility of the technique and to analyse its small sample properties, using empirical likelihood-based inference we implement a Monte Carlo simulation exercise and we also illustrated the proposed technique in an empirical analysis about the production efficiency of the European Union?s companies.

Otras publicaciones de la misma revista o congreso con autores/as de la Universidad de Cantabria

 Autoría: Arteaga-Molina L., Rodriguez-Poo J.,

 Fuente: Journal of Statistical Planning and Inference, 2018, 196, 144-162

Editorial: Elsevier

 Fecha de publicación: 01/08/2018

Nº de páginas: 33

Tipo de publicación: Artículo de Revista

 DOI: 10.1016/j.jspi.2017.11.003

ISSN: 0378-3758,1873-1171

 Proyecto español: ECO2016-76203-C2-1-P

Url de la publicación: https://doi.org/10.1016/j.jspi.2017.11.003