Interactive, Grouped and Non-separable Fixed Effects: A Practitioner's Guide to the New Panel Data Econometrics

TL;DR

This paper reviews recent advances in panel data fixed effects models, including interactive, grouped, and non-separable fixed effects, highlighting their theoretical foundations, estimation methods, and empirical relevance.

Contribution

It provides an accessible overview of new fixed effects models, estimation techniques, and diagnostics, demonstrating their practical importance through empirical examples.

Findings

Empirical support for new fixed effects models

Significant differences from traditional fixed effects results

Guidance for empirical implementation

Abstract

The past 20 years have brought fundamental advances in modeling unobserved heterogeneity in panel data. Interactive Fixed Effects (IFE) proved to be a foundational framework, generalizing the standard one-way and two-way fixed effects models by allowing the unit-specific unobserved heterogeneity to be interacted with unobserved time-varying common factors, allowing for more general forms of omitted variables. The IFE framework laid the theoretical foundations for other forms of heterogeneity, such as grouped fixed effects (GFE) and non-separable two-way fixed effects (NSTW). The existence of IFE, GFE or NSTW has significant implications for identification, estimation, and inference, leading to the development of many new estimators for panel data models. This paper provides an accessible review of the new estimation methods and their associated diagnostic tests, and offers a guide to empirical practice. In two separate empirical investigations we demonstrate that there is empirical support for the new forms of fixed effects and that the results can differ significantly from those obtained using traditional fixed effects estimators.