No 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 Wilk's 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.

This article proposes tests for constancy of coefficients in semi-varying coefficients models. The testing procedure resembles in spirit the union-intersection parameter stability tests in time series, where observations are sorted according to the explanatory variable responsible for the coefficients varying. The test can be applied to model specification checks of interactive effects in linear regression models. Because test statistics are not asymptotically pivotal, critical values and p-values are estimated using a bootstrap technique. The finite sample properties of the test are investigated by means of Monte Carlo experiments, where the new proposal is compared to existing tests based on smooth estimates of the unrestricted model. We also report an application to returns of education modeling.