Empirical likelihood based inference for a categorical varying-coefficient panel data model with fixed effects
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ABSTRACT: In this paper local empirical likelihood-based inference for nonparametric categorical varying coefficient panel data models with fixed effects under cross-sectional dependence is investigated. First, we show that the naive empirical likelihood ratio is asymptotically standard chi-squared using a nonparametric version of Wilks? theorem. The ratio is self-scale invariant and the plug-in estimate of the limiting variance is not needed. As a by product, we propose also an empirical maximum likelihood estimator of the categorical varying coefficient model and we obtain the asymptotic distribution of this estimator. We also illustrated the proposed technique in an application that reports estimates of strike activities from 17 OECD countries for the period 1951-85.
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