A0515
Title: An empirical likelihood goodness-of-fit test for panel data models with interactive fixed effects
Authors: Luis Antonio Arteaga Molina - Universidad de Cantabria (Spain) [presenting]
Juan Manuel Rodriguez-Poo - Universidad de Cantabria (Spain)
Abstract: The empirical likelihood device for a panel data model is employed with interactive fixed effects to formulate a test statistic that measures the goodness of fit of a parametric regression model. The asymptotic distribution of the test statistic is derived, and a Bootstrap procedure is also proposed to obtain the critical values. The technique is based on a comparison with kernel smoothing estimators. The empirical likelihood formulation of the test has two attractive features. One is its automatic consideration of the variation that is associated with the nonparametric fit due to empirical likelihood's ability to Studentize internally. The other is that the asymptotic distribution of the test statistic is free of unknown parameters, avoiding plug-in estimation. To show the feasibility of the technique and to analyze its small sample properties, a Monte Carlo simulation exercise is implemented, and the proposed technique is also illustrated in an empirical analysis of the environmental Kuznets curve hypothesis.
