James G. MacKinnon, Morten Ørregaard Nielsen, Matthew D. Webb
arXiv 6 May 2022 · Econometrics · publishedThe Stata Journal Promoting communications on statistics and Stata (2023) · 24 citations (OpenAlex)
arXiv:2205.03288 · PDF · DOI · OpenAlex · Extracted main text
We introduce a new Stata package called summclust that summarizes the cluster structure of the dataset for linear regression models with clustered disturbances. The key unit of observation for such a model is the cluster. We therefore propose cluster-level measures of leverage, partial leverage, and influence and show how to compute them quickly in most cases. The measures of leverage and partial leverage can be used as diagnostic tools to identify datasets and regression designs in which cluster-robust inference is likely to be challenging. The measures of influence can provide valuable information about how the results depend on the data in the various clusters. We also show how to calculate two jackknife variance matrix estimators efficiently as a byproduct of our other computations. These estimators, which are already available in Stata, are generally more conservative than conventional variance matrix estimators. The summclust package computes all the quantities that we discuss.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Mac\-Kinnon, J. G., M. . Nielsen, and M. D. Webb (2023) Fast jackknife and bootstrap methods for cluster-robust inference self | 1.000 | 11 | 5 | 100% |
| 2 | Mac\-Kinnon, J. G., M. . Nielsen, and M. D. Webb (2023) Cluster-robust inference: A guide to empirical practice self | 1.000 | 5 | 4 | 100% |
| 3 | Belsley, D. A., E. Kuh, and R. E. Welsch (1980) Regression Diagnostics | 0.928 | 4 | 3 | 100% |
| 4 | Djogbenou, A. A., J. G. Mac\-Kinnon, and M. . Nielsen (2019) Asymptotic theory and wild bootstrap inference with clustered errors | 0.843 | 3 | 3 | 100% |
| 5 | Mac\-Kinnon, J. G. and M. D. Webb (2018) The wild bootstrap for few (treated) clusters | 0.843 | 3 | 3 | 100% |
| 6 | Chatterjee, S. and A. S. Hadi (1986) Influential observations, high-leverage points, and outliers in linear regression | 0.737 | 3 | 2 | 100% |
| 7 | Mac\-Kinnon, J. G. and H. White (1985) Some heteroskedasticity consistent covariance matrix estimators with improved finite sample properties | 0.644 | 4 | 1 | 100% |
| 8 | Cameron, A. C., J. B. Gelbach, and D. L. Miller (2008) Bootstrap-based improvements for inference with clustered errors | 0.644 | 2 | 2 | 100% |
| 9 | Cook, R. D. and S. Weisberg (1980) Characterizations of an empirical influence function for detecting influential cases in regression | 0.644 | 2 | 2 | 100% |
| 10 | Hansen, B. E (2022) Jackknife standard errors for clustered regression | 0.644 | 2 | 2 | 100% |
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