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Spectral and post-spectral estimators for grouped panel data models

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abstract

In this paper, we develop spectral and post-spectral estimators for grouped panel data models. Both estimators are consistent in the asymptotics where the number of observations $N$ and the number of time periods $T$ simultaneously grow large. In addition, the post-spectral estimator is $\sqrt{NT}$-consistent and asymptotically normal with mean zero under the assumption of well-separated groups even if $T$ is growing much slower than $N$. The post-spectral estimator has, therefore, theoretical properties that are comparable to those of the grouped fixed-effect estimator developed by Bonhomme and Manresa (2015). In contrast to the grouped fixed-effect estimator, however, our post-spectral estimator is computationally straightforward.

fields

econ.EM 1

years

2025 1

verdicts

CONDITIONAL 1

representative citing papers

K-Means Panel Data Clustering in the Presence of Small Groups

econ.EM · 2025-08-21 · conditional · novelty 6.0

In grouped panel data, tiny groups are hard to estimate and standard information criteria can pick the wrong number of groups; this paper derives when estimation works and proposes modified criteria.

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  • K-Means Panel Data Clustering in the Presence of Small Groups econ.EM · 2025-08-21 · conditional · none · ref 1279 · internal anchor

    In grouped panel data, tiny groups are hard to estimate and standard information criteria can pick the wrong number of groups; this paper derives when estimation works and proposes modified criteria.