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Title: Empirical likelihood based inference for fixed effects varying coefficient panel data models Authors:  Luis Antonio Arteaga Molina - Universidad de Cantabria (Spain) [presenting]
Juan Manuel Rodriguez-Poo - Universidad de Cantabria (Spain)
Abstract: 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 likehood estimators of the varying coefficient models and their derivatives. We obtain as well 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 analyze its small sample properties, using empirical likelihood-based inference we test for a conditional factor model in the CAPM setting and we implement a Monte Carlo simulation exercise.