arXiv 8 Oct 2020 · Econometrics · publishedThe Review of Economic Studies (2025) · 9 citations (OpenAlex)
arXiv:2010.04076 · PDF · DOI · OpenAlex · Extracted main text
I introduce a generic method for inference about a scalar parameter in research designs with a finite number of heterogeneous clusters where only a single cluster received treatment. This situation is commonplace in difference-in-differences estimation but the test developed here applies more generally. I show that the test controls size and has power under asymptotics where the number of observations within each cluster is large but the number of clusters is fixed. The test combines weighted, approximately Gaussian parameter estimates with a rearrangement procedure to obtain its critical values. The weights needed for most empirically relevant situations are tabulated in the paper. Calculation of the critical values is computationally simple and does not require simulation or resampling. The rearrangement test is highly robust to situations where some clusters are much more variable than others. Examples and an empirical application are provided.
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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 | Conley, T. G. and C. R. Taber (2011) difference in differences | 1.000 | 20 | 3 | 100% |
| 2 | Garthwaite, C., T. Gross, and M. J. Notowidigdo (2014) Public health insurance, labor supply, and employment lock | 0.874 | 14 | 2 | 100% |
| 3 | Canay, I., J. P. Romano, and A. M. Shaikh (2017) Randomization tests under an approximate symmetry assumption | 0.843 | 3 | 3 | 100% |
| 4 | Bester, C. A., T. G. Conley, and C. B. Hansen (2011) Inference with dependent data using cluster covariance estimators | 0.644 | 2 | 2 | 100% |
| 5 | Ferman, B (2020) Inference in differences-in-differences with few treated units and spatial correlation | 0.644 | 2 | 2 | 100% |
| 6 | Ferman, B. and C. Pinto (2019) Inference in differences-in-differences with few treated groups and heteroskedasticity | 0.644 | 2 | 2 | 100% |
| 7 | Hagemann, A (2019) Permutation inference with a finite number of heterogeneous clusters self | 0.644 | 2 | 2 | 100% |
| 8 | El Machkouri, M., D. Volný, and W. B. Wu (2013) A central limit theorem for stationary random fields | 0.511 | 2 | 1 | 100% |
| 9 | Bertrand, M., E. Duflo, and S. Mullainathan (2004) How much should we trust differences-in-differences estimates? | 0.405 | 1 | 1 | 100% |
| 10 | Cameron, A. C., J. B. Gelbach, and D. L. Miller (2008) Bootstrap-based improvements for inference with clustered errors | 0.405 | 1 | 1 | 100% |
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