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01521nam a2200265Ia 4500 |
| 001 |
10.1016-j.jeconom.2019.02.001 |
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220511s2019 CNT 000 0 und d |
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|a 03044076 (ISSN)
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| 245 |
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|a Asymptotic theory for clustered samples
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| 260 |
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|b Elsevier Ltd
|c 2019
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| 856 |
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|z View Fulltext in Publisher
|u https://doi.org/10.1016/j.jeconom.2019.02.001
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|a We provide a complete asymptotic distribution theory for clustered data with a large number of independent groups, generalizing the classic laws of large numbers, uniform laws, central limit theory, and clustered covariance matrix estimation. Our theory allows for clustered observations with heterogeneous and unbounded cluster sizes. Our conditions cleanly nest the classical results for i.n.i.d. observations, in the sense that our conditions specialize to the classical conditions under independent sampling. We use this theory to develop a full asymptotic distribution theory for estimation based on linear least-squares, 2SLS, nonlinear MLE, and nonlinear GMM. © 2019 Elsevier B.V.
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|a Asymptotic analysis
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| 650 |
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|a Asymptotic distribution theories
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| 650 |
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|a Asymptotic theories
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| 650 |
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|a Central limit theory
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| 650 |
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|a Classical conditions
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|a Cluster sizes
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|a Clustered datum
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| 650 |
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|a Covariance matrix
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| 650 |
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|a Covariance matrix estimation
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| 650 |
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|a Linear least squares
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| 700 |
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|a Hansen, B.E.
|e author
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|a Lee, S.
|e author
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| 773 |
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|t Journal of Econometrics
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