Harold D Chiang, Yukitoshi Matsushita, Taisuke Otsu
arXiv 10 Aug 2021 · Statistics — Methodology · publishedJournal of Econometrics (2024) · 1 citations (OpenAlex)
arXiv:2108.04852 · PDF · DOI · OpenAlex · Extracted main text
This paper develops a general methodology to conduct statistical inference for observations indexed by multiple sets of entities. We propose a novel multiway empirical likelihood statistic that converges to a chi-square distribution under the non-degenerate case, where corresponding Hoeffding type decomposition is dominated by linear terms. Our methodology is related to the notion of jackknife empirical likelihood but the leave-out pseudo values are constructed by leaving columns or rows. We further develop a modified version of our multiway empirical likelihood statistic, which converges to a chi-square distribution regardless of the degeneracy, and discover its desirable higher-order property compared to the t-ratio by the conventional Eicker-White type variance estimator. The proposed methodology is illustrated by several important statistical problems, such as bipartite network, generalized estimating equations, and three-way observations.
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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 | Liang, K.-Y. and S. L. Zeger (1986) Longitudinal data analysis using generalized linear models | 0.928 | 4 | 3 | 100% |
| 2 | Efron, B. and C. Stein (1981) The jackknife estimate of variance | 0.874 | 6 | 2 | 100% |
| 3 | Graham, B. S (2020) Sparse network asymptotics for logistic regression | 0.855 | 8 | 4 | 62% |
| 4 | Searle, S. R., G. Casella, and C. E. McCulloch (2009) Variance Components | 0.843 | 3 | 3 | 100% |
| 5 | Cameron, C. A., J. B. Gelbach, and D. L. Miller (2011) Robust inference with multiway clustering | 0.811 | 4 | 2 | 100% |
| 6 | MacKinnon, J. G., M. . Nielsen, and M. D. Webb (2021) Wild bootstrap and asymptotic inference with multiway clustering | 0.811 | 4 | 2 | 100% |
| 7 | Bickel, P. J., A. Chen, and E. Levina (2011) The method of moments and degree distributions for network models | 0.737 | 3 | 2 | 100% |
| 8 | Thompson, S. B (2011) Simple formulas for standard errors that cluster by both firm and time | 0.737 | 3 | 2 | 100% |
| 9 | Bhattacharyya, S. and P. J. Bickel (2015) Subsampling bootstrap of count features of networks | 0.644 | 2 | 2 | 100% |
| 10 | Choi, D. and P. J. Wolfe (2014) Co-clustering separately exchangeable network data | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 51 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Inference in Linear Dyadic Data Models with Network Spillovers | 0.405 | 1 | 1 |
| 2 | Jackknife Inference with Two0.04167em–0.08333em Way Clustering | 0.405 | 1 | 1 |
| 3 | Empirical Likelihood for Random Forests and Ensembles | 0.405 | 1 | 1 |
| 4 | Cross-Fitting-Free Debiased Machine Learning with Multiway Dependence | 0.405 | 1 | 1 |