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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.
Forward citations
Cited by 2 Pith papers
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Robust Inference Methods for Latent Group Panel Models under Possible Group Non-Separation
Selective conditional inference gives Wald tests on estimated latent panel groups a truncated chi-square null distribution, valid even without group separation.
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K-Means Panel Data Clustering in the Presence of Small Groups
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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