arXiv 25 Jul 2025 · Econometrics · 1 citations (OpenAlex)
arXiv:2507.19099 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Bonhomme, S., T. Lamadon, and E. Manresa (2022) Discretizing Unobserved Heterogeneity | 1.000 | 9 | 3 | 100% |
| 2 | Freeman, H. and M. Weidner (2023) Linear panel regressions with two-way unobserved heterogeneity | 1.000 | 8 | 3 | 100% |
| 3 | Chudik, A., M. H. Pesaran, and E. Tosetti (2011) Weak and strong cross-section dependence and estimation of large panels | 1.000 | 6 | 3 | 100% |
| 4 | Ahn, S. C. and A. R. Horenstein (2013) Eigenvalue Ratio Test for the Number of Factors | 0.969 | 11 | 4 | 91% |
| 5 | Bai, J (2009) Panel Data Models With Interactive Fixed Effects | 0.961 | 9 | 5 | 89% |
| 6 | Bonhomme, S. and E. Manresa (2015) Grouped Patterns of Heterogeneity in Panel Data | 0.961 | 9 | 4 | 89% |
| 7 | Pesaran, M. H (2006) Estimation and inference in large heterogeneous panels with a multifactor error structure | 0.956 | 8 | 4 | 88% |
| 8 | Onatski, A (2010) Determining the Number of Factors from Empirical Distribution of Eigenvalues | 0.950 | 7 | 4 | 86% |
| 9 | Beyhum, J. and M. Mugnier (2024) Inference after discretizing time-varying unobserved heterogeneity | 0.950 | 7 | 3 | 86% |
| 10 | Gagliardini, P., E. Ossola, and O. Scaillet (2019) A diagnostic criterion for approximate factor structure | 0.941 | 6 | 4 | 83% |
Showing the top 10 of 144 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Threshold Regression in Heterogeneous Panel Data with Interactive Fixed Effects | 0.644 | 2 | 2 |