Laurent Davezies, Xavier D'Haultfoeuille, Yannick Guyonvarch
arXiv 20 Jul 2018 · Econometrics · 4 citations (OpenAlex)
arXiv:1807.07925 · PDF · DOI · OpenAlex · Extracted main text
If multiway cluster-robust standard errors are used routinely in applied economics, surprisingly few theoretical results justify this practice. This paper aims to fill this gap. We first prove, under nearly the same conditions as with i.i.d. data, the weak convergence of empirical processes under multiway clustering. This result implies central limit theorems for sample averages but is also key for showing the asymptotic normality of nonlinear estimators such as GMM estimators. We then establish consistency of various asymptotic variance estimators, including that of Cameron et al. (2011) but also a new estimator that is positive by construction. Next, we show the general consistency, for linear and nonlinear estimators, of the pigeonhole bootstrap, a resampling scheme adapted to multiway clustering. Monte Carlo simulations suggest that inference based on our two preferred methods may be accurate even with very few clusters, and significantly improve upon inference based on Cameron et al. (2011).
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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 | Cameron, Gelbach \ Miller (2011) `Robust inference with multiway clustering', Journal of Business & Economic Statistics 29(2), 238–249 | 1.000 | 13 | 4 | 100% |
| 2 | Menzel (2017) Bootstrap with clustering in two or more dimensions | 0.874 | 7 | 2 | 100% |
| 3 | MacKinnon, Nielsen \ Webb (2017) Bootstrap and asymptotic inference with multiway clustering | 0.874 | 6 | 2 | 100% |
| 4 | van der Vaart \ Wellner (1996) Weak Convergence of Empirical Processes: with Applications to Statistics, Springer-Verlag New York | 0.836 | 12 | 6 | 58% |
| 5 | Carter, Schnepel \ Steigerwald (2017) `Asymptotic behavior of at-test robust to cluster heterogeneity', Review of Economics and Statistics 99(4), 698–709 | 0.737 | 3 | 2 | 100% |
| 6 | Aldous (1981) `Representations for partially exchangeable arrays of random variables', Journal of Multivariate Analysis 11(4), pp | 0.644 | 2 | 2 | 100% |
| 7 | Arcones \ Giné (1993) `Limit theorems for U-processes', The Annals of Probability 21(3), pp | 0.644 | 2 | 2 | 100% |
| 8 | Hoover (1979) Relations on probability spaces and arrays of random variables | 0.644 | 2 | 2 | 100% |
| 9 | Bertrand, Duflo \ Mullainathan (2004) `How much should we trust differences-in-differences estimates?', The Quarterly Journal of Economics 119(1), 249–275 | 0.644 | 2 | 2 | 100% |
| 10 | McCullagh et al (2000) `Resampling and exchangeable arrays', Bernoulli 6(2), 285–301 | 0.644 | 2 | 2 | 100% |
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