Grouped Fixed-Effects Estimation Is More Robust Than You Think

The grouped fixed-effects (GFE) approach of Bonhomme and Manresa (2015) has become an increasingly popular tool in applied economics for modeling latent heterogeneity in panel data, yet its implementation raises two recurring concerns: how many groups to select, and whether groups are sufficiently distinct to be meaningfully recovered. It is shown here that both concerns may be largely immaterial for inference on common parameters. First, the GFE estimator remains asymptotically equivalent to the oracle that knows the true groups even when the number of groups is overspecified, provided the additional groups fit idiosyncratic noise rather than latent structure in the covariates. Second, oracle equivalence continues to hold under weak group separation provided the cross-section does not grow too fast relative to the time dimension, at a rate that depends on the degree of separation. Simulations confirm that the robustness properties emerge quickly in finite samples, and identify the situations in which they fail. The paper concludes with updated guidance for applied researchers, revisiting the income-democracy debate as an illustration. Overall, the findings reframe GFE as a more reliable and user-friendly tool for applied work than previously recognized.