A Simple and Computationally Trivial Estimator for Grouped Fixed Effects Models

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This paper introduces a new fixed effects estimator for linear panel data models with clustered time patterns of unobserved heterogeneity.

• The method avoids non-convex and combinatorial optimization by combining a preliminary consistent estimator of the slope coefficient, an agglomerative pairwise-differencing clustering of cross-sectional units, and a pooled ordinary least squares regression.
• Asymptotic guarantees are established in a framework where $T$ can grow at any power of $N$, as both $N$ and $T$ approach infinity.
• Unlike most existing approaches, the proposed estimator is computationally straightforward and does not require a known upper bound on the number of groups.