arXiv 1 Jul 2019 · Econometrics · publishedThe Review of Economics and Statistics (2023) · 10 citations (OpenAlex)
arXiv:1907.01049 · PDF · DOI · OpenAlex · Extracted main text
I introduce a simple permutation procedure to test conventional (non-sharp) hypotheses about the effect of a binary treatment in the presence of a finite number of large, heterogeneous clusters when the treatment effect is identified by comparisons across clusters. The procedure asymptotically controls size by applying a level-adjusted permutation test to a suitable statistic. The adjustments needed for most empirically relevant situations are tabulated in the paper. The adjusted permutation test is easy to implement in practice and performs well at conventional levels of significance with at least four treated clusters and a similar number of control clusters. It is particularly 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 | Bester, C. Alan, Timothy G. Conley, and Christian B. Hansen (2011) Inference with dependent data using cluster covariance estimators | 0.928 | 4 | 3 | 100% |
| 2 | Canay, Ivan A., Joseph P. Romano, and Azeem M. Shaikh (2017) Randomization tests under an approximate symmetry assumption | 0.894 | 7 | 4 | 71% |
| 3 | Angrist, Joshua and Victor Lavy (2009) The effects of high stakes high school achievement awards: Evidence from a randomized trial | 0.693 | 9 | 1 | 100% |
| 4 | Canay, Ivan A., Andres Santos, and Azeem M. Shaikh (2021) small | 0.644 | 2 | 2 | 100% |
| 5 | Hoeffding, Wassily (1952) The large-sample power of tests based on permutations of observations | 0.644 | 2 | 2 | 100% |
| 6 | Ibragimov, Rustam and Ulrich Müller (2016) Inference with few heterogenous clusters | 0.621 | 25 | 4 | 24% |
| 7 | Cameron, A. Colin, Jonah B. Gelbach, and Douglas L. Miller (2008) Bootstrap-based improvements for inference with clustered errors | 0.511 | 2 | 1 | 100% |
| 8 | Conley, Timothy G. and Christopher R. Taber (2011) difference in differences | 0.511 | 2 | 1 | 100% |
| 9 | El Machkouri, Mohamed, Dalibor Volný, and Wei Biao Wu (2013) A central limit theorem for stationary random fields | 0.511 | 2 | 1 | 100% |
| 10 | Székely, Gabor J (2006) Students t-test for scale mixture errors | 0.511 | 2 | 1 | 100% |
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