arXiv 23 Aug 2019 · Econometrics · 23 citations (OpenAlex)
arXiv:1908.09029 · PDF · DOI · OpenAlex · Extracted main text
Dyadic data, where outcomes reflecting pairwise interaction among sampled units are of primary interest, arise frequently in social science research. Regression analyses with such data feature prominently in many research literatures (e.g., gravity models of trade). The dependence structure associated with dyadic data raises special estimation and, especially, inference issues. This chapter reviews currently available methods for (parametric) dyadic regression analysis and presents guidelines for empirical researchers.
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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 | Fafchamps, M. & Gubert, F (2007) The formation of risk sharing networks | 1.000 | 7 | 4 | 100% |
| 2 | Menzel, K (2017) Bootstrap with clustering in two or more dimensions | 1.000 | 5 | 4 | 100% |
| 3 | Santos Silva, J. & Tenreyro, S (2006) The log of gravity | 1.000 | 5 | 3 | 100% |
| 4 | Davezies, L., d'Haultfoeuille, X., & Guyonvarch, Y (2019) Empirical process results for exchangeable arrayes | 0.843 | 3 | 3 | 100% |
| 5 | Aronow, P. M., Samii, C., & Assenova, V. A (2017) Cluster-robust variance estimation for dyadic data | 0.737 | 3 | 2 | 100% |
| 6 | Graham, B. S. (TBD) Handbook of Econometrics, volume 7, chapter The econometric analysis of networks self | 0.737 | 3 | 2 | 100% |
| 7 | Graham, B. S., Niu, F., & Powell, J. L (2019) Kernel density estimation for undirected dyadic data self | 0.737 | 3 | 2 | 100% |
| 8 | Tinbergen, J (1962) Shaping the World Economy: Suggestions for an International Economic Policy | 0.737 | 3 | 2 | 100% |
| 9 | Aldous, D. J (1981) Representations for partially exchangeable arrays of random variables | 0.644 | 2 | 2 | 100% |
| 10 | Bickel, P. J., Chen, A., & Levina, E (2011) The method of moments and degree distributions for network models | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 44 scored citations.