arXiv 3 May 2021 · Econometrics · publishedJournal of Business and Economic Statistics (2023) · 3 citations (OpenAlex)
arXiv:2105.01008 · PDF · DOI · OpenAlex · Extracted main text
Suppose a researcher observes individuals within a county within a state. Given concerns about correlation across individuals, it is common to group observations into clusters and conduct inference treating observations across clusters as roughly independent. However, a researcher that has chosen to cluster at the county level may be unsure of their decision, given knowledge that observations are independent across states. This paper proposes a modified randomization test as a robustness check for the chosen level of clustering in a linear regression setting. Existing tests require either the number of states or number of counties to be large. Our method is designed for settings with few states and few counties. While the method is conservative, it has competitive power in settings that may be relevant to empirical work.
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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 | MacKinnon, J. G., M. A. Nielsen, and M. D. Webb (2020) Testing for the Appropriate Level of Clustering in Linear Regression Models | 1.000 | 8 | 3 | 100% |
| 2 | Ibragimov, R. and U. K. Müller (2016) Inference with Few heterogeneous Clusters | 1.000 | 7 | 3 | 100% |
| 3 | Gneezy, U., J. A. List, J. A. Livingston, X. Qin, S. Sadoff, and Y. Xu (2019) Measuring Success in Education: The Role of Effort on the Test Itself | 0.969 | 11 | 5 | 91% |
| 4 | Canay, I. A., J. P. Romano, and A. M. Shaikh (2017) Randomization Tests under an Approximate Symmetry Assumption | 0.928 | 5 | 4 | 80% |
| 5 | Cameron, A. C., J. B. Gelbach, and D. L. Miller (2008) Bootstrap-Based Improvements for Inference with Clustered Errors | 0.737 | 3 | 3 | 67% |
| 6 | Angrist, J. D. and J.-S. Pischke (2008) Mostly Harmless Econometrics | 0.644 | 2 | 2 | 100% |
| 7 | Abadie, A., S. Athey, G. Imbens, and J. Wooldridge (2017) When Should You Adjust Standard Errors for Clustering? | 0.511 | 2 | 1 | 100% |
| 8 | Bester, C. A., T. G. Conley, and C. B. Hansen (2011) Inference with dependent data using cluster covariance estimators | 0.405 | 1 | 1 | 100% |
| 9 | Bertrand, M., E. Duflo, and M. Sendhil (2004) How Much Should We Trust Differences-in-Differences Estimates? | 0.405 | 1 | 1 | 100% |
| 10 | Bell, R. and D. E. McCaffrey (2002) Bias Reduction in Standard Errors for Linear Regression with Multi-Stage Samples | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 21 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Testing for the appropriate level of clustering in linear regression models | 1.000 | 6 | 3 |
| 2 | 1 Panel Data with Unknown Clusters | 0.511 | 2 | 1 |
| 3 | 1 Some Finite Sample Properties of the Sign Test | 0.405 | 1 | 1 |
| 4 | Cluster-Robust Inference: A Guide to Empirical Practice | 0.405 | 1 | 1 |
| 5 | When Can We Trust Cluster-Robust Inference? | 0.405 | 1 | 1 |