James G. MacKinnon, Morten Ørregaard Nielsen, Matthew D. Webb
arXiv 2 Jun 2024 · Econometrics · publishedEconometric Reviews (2025) · 1 citations (OpenAlex)
arXiv:2406.00650 · PDF · DOI · OpenAlex · Extracted main text
We study cluster-robust inference for logistic regression (logit) models. Inference based on the most commonly-used cluster-robust variance matrix estimator (CRVE) can be very unreliable. We study several alternatives. Conceptually the simplest of these, but also the most computationally demanding, involves jackknifing at the cluster level. We also propose a linearized version of the cluster-jackknife variance matrix estimator as well as linearized versions of the wild cluster bootstrap. The linearizations are based on empirical scores and are computationally efficient. Our results can readily be generalized to other binary response models. We also discuss a new Stata software package called logitjack which implements these procedures. Simulation results strongly favor the new methods, and two empirical examples suggest that it can be important to use them in practice.
appendix boundary found by appendix_command · 87% of the source is main text. Read the extracted text to check this.
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 and reliable jackknife and bootstrap methods for cluster-robust inference self | 0.983 | 20 | 9 | 95% |
| 2 | Djogbenou, A. A., J. G. Mac\-Kinnon, and M. . Nielsen (2019) Asymptotic theory and wild bootstrap inference with clustered errors | 0.928 | 4 | 4 | 100% |
| 3 | Mac\-Kinnon, J. G., M. . Nielsen, and M. D. Webb (2023) Leverage, influence, and the jackknife in clustered regression models: Reliable inference using summclust self | 0.928 | 4 | 4 | 100% |
| 4 | Mac\-Kinnon, J. G., M. . Nielsen, and M. D. Webb (2023) Cluster-robust inference: A guide to empirical practice self | 0.843 | 3 | 3 | 100% |
| 5 | Hansen, B. E. and S. Lee (2019) Asymptotic theory for clustered samples | 0.811 | 4 | 2 | 100% |
| 6 | Bell, R. M. and D. F. McCaffrey (2002) Bias reduction in standard errors for linear regression with multi-stage samples | 0.737 | 3 | 2 | 100% |
| 7 | Hansen, B. E (2024) Jackknife standard errors for clustered regression | 0.737 | 3 | 2 | 100% |
| 8 | Mac\-Kinnon, J. G. and M. D. Webb (2017) 1Wild bootstrap inference for wildly different cluster sizes | 0.737 | 3 | 2 | 100% |
| 9 | Mac\-Kinnon, J. G. and M. D. Webb (2018) The wild bootstrap for few (treated) clusters | 0.644 | 2 | 2 | 100% |
| 10 | Mac\-Kinnon, J. G. and H. White (1985) Some heteroskedasticity consistent covariance matrix estimators with improved finite sample properties | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 30 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | When Can We Trust Cluster-Robust Inference? | 0.811 | 4 | 2 |
| 2 | Improved Inference for CSDID Using the Cluster Jackknife | 0.405 | 1 | 1 |