arXiv 7 Mar 2022 · Econometrics · publishedPolitical Analysis (2023) · 1 citations (OpenAlex)
arXiv:2203.03497 · PDF · DOI · OpenAlex · Extracted main text
When using dyadic data (i.e., data indexed by pairs of units), researchers typically assume a linear model, estimate it using Ordinary Least Squares and conduct inference using “dyadic-robust" variance estimators. The latter assumes that dyads are uncorrelated if they do not share a common unit (e.g., if the same individual is not present in both pairs of data). We show that this assumption does not hold in many empirical applications because indirect links may exist due to network connections, generating correlated outcomes. Hence, “dyadic-robust” estimators can be biased in such situations. We develop a consistent variance estimator for such contexts by leveraging results in network statistics. Our estimator has good finite sample properties in simulations, while allowing for decay in spillover effects. We illustrate our message with an application to politicians' voting behavior when they are seating neighbors in the European Parliament.
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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 | Harmon, N., R. Fisman, and E. Kamenica (2019) Peer effects in legislative voting | 0.980 | 17 | 4 | 94% |
| 2 | Tabord-Meehan, M (2019) Inference with dyadic data: Asymptotic behavior of the dyadic-robust t-statistic | 0.894 | 14 | 5 | 71% |
| 3 | Aronow, P. M., C. Samii, and V. A. Assenova (2015) Cluster–robust variance estimation for dyadic data | 0.890 | 17 | 6 | 71% |
| 4 | Erikson, R. S., P. M. Pinto, K. T. Rader, et al (2014) Dyadic analysis in international relations: A cautionary tale | 0.811 | 4 | 2 | 100% |
| 5 | Kojevnikov, D., V. Marmer, and K. Song (2021) Limit theorems for network dependent random variables | 0.741 | 27 | 7 | 41% |
| 6 | Cameron, A. C., J. B. Gelbach, and D. L. Miller (2011) Robust inference with multiway clustering | 0.737 | 3 | 2 | 100% |
| 7 | Poast, P (2016) Dyads are dead, long live dyads! the limits of dyadic designs in international relations research | 0.737 | 3 | 2 | 100% |
| 8 | Anderson, J. E. and E. van Wincoop (2003) Gravity with gravitas: A solution to the border puzzle | 0.644 | 2 | 2 | 100% |
| 9 | Mansfield, E. D., H. V. Milner, and B. P. Rosendorff (2000) Free to trade: Democracies, autocracies, and international trade | 0.644 | 2 | 2 | 100% |
| 10 | Minhas, S., P. D. Hoff, and M. D. Ward (2019) Inferential approaches for network analysis: Amen for latent factor models | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 54 scored citations.