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

2026-01-29

AI Summary

This study addresses the challenge of non-nested model selection in panel data settings involving individual-by-time fixed effects, where the conventional Vuong test fails due to the lack of regularity in the standard profile likelihood. By introducing a modified profile likelihood combined with the Kullback–Leibler information criterion, this work extends the Vuong test for the first time to a generalized panel data framework, effectively overcoming the technical obstacles posed by fixed effects. The proposed method delivers a rigorous and computationally feasible statistical inference tool for linear panel models featuring non-nested specifications of individual–time interaction effects, thereby substantially broadening the applicability of non-nested hypothesis testing in panel data analysis.

Technology Category

• Search and Optimization: Non-convex Optimization
• Knowledge Representation and Reasoning: Nonmonotonic Reasoning
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• model selection
• panel data models
• Vuong test
• non-nested models
• fixed effects

Innovation

• Vuong test
• panel data models
• profile likelihood
• Kullback-Leibler information criterion
• group fixed effects