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

TL;DR

This work generalizes the classical Vuong test to panel data by using modified profile likelihoods and KL information to address incidental-parameter biases that arise in high-dimensional panel settings. It develops both infeasible and feasible bias- and variance-corrected quasi-likelihood ratio statistics, enabling valid non-nested model comparisons across panel specifications with group-time fixed effects. The framework is extended to compare heterogeneous time effects against TWFE, with tailored estimation, asymptotic results, and practical algorithms for bias/variance estimation. The resulting generalized Vuong test provides size control and power under null and alternative hypotheses, facilitating reliable model selection in complex panel data environments with grouped heterogeneity.

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.

Theorems & Definitions (100)

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