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This paper introduces a new fixed effects estimator for linear panel datamodels with clustered time patterns of unobserved heterogeneity. The methodavoids non-convex and combinatorial optimization by combining a preliminaryconsistent estimator of the slope coefficient, an agglomerativepairwise-differencing clustering of cross-sectional units, and a pooledordinary least squares regression. Asymptotic guarantees are established in aframework where $T$ can grow at any power of $N$, as both $N$ and $T$ approachinfinity. Unlike most existing approaches, the proposed estimator iscomputationally straightforward and does not require a known upper bound on thenumber of groups. As existing approaches, this method leads to a consistentestimation of well-separated groups and an estimator of common parametersasymptotically equivalent to the infeasible regression controlling for the truegroups. An application revisits the statistical association between income anddemocracy.

A Simple and Computationally Trivial Estimator for Grouped Fixed Effects Models