arXiv 2 Mar 2021 · Econometrics · publishedEconometrica (2023) · 14 citations (OpenAlex)
arXiv:2103.01470 · PDF · DOI · OpenAlex · Extracted main text
Since network data commonly consists of observations from a single large network, researchers often partition the network into clusters in order to apply cluster-robust inference methods. Existing such methods require clusters to be asymptotically independent. Under mild conditions, we prove that, for this requirement to hold for network-dependent data, it is necessary and sufficient that clusters have low conductance, the ratio of edge boundary size to volume. This yields a simple measure of cluster quality. We find in simulations that when clusters have low conductance, cluster-robust methods control size better than HAC estimators. However, for important classes of networks lacking low-conductance clusters, the former can exhibit substantial size distortion. To determine the number of low-conductance clusters and construct them, we draw on results in spectral graph theory that connect conductance to the spectrum of the graph Laplacian. Based on these results, we propose to use the spectrum to determine the number of low-conductance clusters and spectral clustering to construct them.
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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, Conley and Hansen (2011) Inference with Dependent Data Using Cluster Covariance Estimators | 1.000 | 10 | 4 | 100% |
| 2 | Zacchia (2020) Knowledge Spillovers Through Networks of Scientists | 1.000 | 6 | 4 | 100% |
| 3 | Canay, Romano and Shaikh (2017) Randomization Tests Under an Approximate Symmetry Assumption | 1.000 | 6 | 3 | 100% |
| 4 | Ibragimov and Müller (2010) $t$-Statistic Based Correlation and Heterogeneity Robust Inference | 1.000 | 6 | 3 | 100% |
| 5 | Leung (2022) Causal Inference Under Approximate Neighborhood Interference self | 0.971 | 12 | 4 | 92% |
| 6 | Cameron and Miller (2015) A Practitioner's Guide to Cluster-Robust Inference | 0.928 | 4 | 3 | 100% |
| 7 | Canay, Santos and Shaikh (2021) The Wild Bootstrap with a “Small | 0.928 | 4 | 3 | 100% |
| 8 | Kojevnikov, Marmer and Song (2021) Limit Theorems for Network Dependent Random Variables | 0.920 | 9 | 4 | 78% |
| 9 | Aral and Nicolaides (2017) Exercise Contagion in a Global Social Network | 0.843 | 3 | 3 | 100% |
| 10 | von Luxburg (2007) A Tutorial on Spectral Clustering | 0.811 | 4 | 2 | 100% |
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