Model Selection in Panel Data Models: A Generalization of the Vuong Test

TL;DR

This paper extends the Vuong test to panel data models using modified profile likelihoods and the Kullback-Leibler criterion, accommodating complex fixed effects structures.

Contribution

It introduces a generalized Vuong test for panel data that handles non-nested models with group fixed effects, improving model comparison accuracy.

Findings

Successfully generalizes Vuong test for panel data.

Handles non-nested models with complex fixed effects.

Provides a framework for model selection in advanced panel data settings.

Abstract

This paper generalizes the classical Vuong (1989) test to panel data models by employing modified profile likelihoods and the Kullback-Leibler information criterion. Unlike the standard likelihood function, the profile likelihood lacks certain regular properties, making modification necessary. We adopt a generalized panel data framework that incorporates group fixed effects for time and individual pairs, rather than traditional individual fixed effects. Applications of our approach include linear models with non-nested specifications of individual-time effects.