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I know the problems of there being 'too few' clusters when using clustered standard errors, but are there problems with using 'too many'?

For instance, I have 1million observations, and 2500 clusters.

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  • $\begingroup$ The number of clusters is built on your criterion. For example, one could use each observation as a cluster. If you have too many clusters, that mean how you build it can be optimised. $\endgroup$ – SmallChess Oct 27 '15 at 12:52
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If data highly sparse (with too much variance), you may have even grater number of clusters. One way is to normalize your data and see if you can find some accuracy.

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