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Inference With Dyadic Data: Asymptotic Behavior of the Dyadic-Robust t-Statistic

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  • Max Tabord-Meehan

Abstract

This article is concerned with inference in the linear model with dyadic data. Dyadic data are indexed by pairs of “units;” for example, trade data between pairs of countries. Because of the potential for observations with a unit in common to be correlated, standard inference procedures may not perform as expected. We establish a range of conditions under which a t-statistic with the dyadic-robust variance estimator of Fafchamps and Gubert is asymptotically normal. Using our theoretical results as a guide, we perform a simulation exercise to study the validity of the normal approximation, as well as the performance of a novel finite-sample correction. We conclude with guidelines for applied researchers wishing to use the dyadic-robust estimator for inference.

Suggested Citation

  • Max Tabord-Meehan, 2019. "Inference With Dyadic Data: Asymptotic Behavior of the Dyadic-Robust t-Statistic," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(4), pages 671-680, October.
    • Handle: RePEc:taf:jnlbes:v:37:y:2019:i:4:p:671-680
    DOI: 10.1080/07350015.2017.1409630
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    Cited by:

    1. Nicola Campigotto & Chiara Rapallini & Aldo Rustichini, 2022. "School friendship networks, homophily and multiculturalism: evidence from European countries," Journal of Population Economics, Springer;European Society for Population Economics, vol. 35(4), pages 1687-1722, October.
    2. Harold D. Chiang & Kengo Kato & Yuya Sasaki, 2023. "Inference for High-Dimensional Exchangeable Arrays," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 118(543), pages 1595-1605, July.
    3. Luis E. Candelaria & Yichong Zhang, 2024. "Robust Inference in Locally Misspecified Bipartite Networks," Papers 2403.13725, arXiv.org.
    4. Hansen, Bruce E. & Lee, Seojeong, 2019. "Asymptotic theory for clustered samples," Journal of Econometrics, Elsevier, vol. 210(2), pages 268-290.
    5. Bryan S. Graham, 2019. "Network Data," Papers 1912.06346, arXiv.org.
    6. Nathan Canen & Ko Sugiura, 2022. "Inference in Linear Dyadic Data Models with Network Spillovers," Papers 2203.03497, arXiv.org, revised Jun 2023.
    7. Bryan S. Graham, 2019. "Dyadic Regression," Papers 1908.09029, arXiv.org.
    8. Harold D Chiang & Yuya Sasaki, 2023. "On Using The Two-Way Cluster-Robust Standard Errors," Papers 2301.13775, arXiv.org.
    9. Harold D Chiang & Yukun Ma & Joel Rodrigue & Yuya Sasaki, 2021. "Dyadic double/debiased machine learning for analyzing determinants of free trade agreements," Papers 2110.04365, arXiv.org, revised Dec 2022.
    10. Áureo de Paula, 2020. "Econometric Models of Network Formation," Annual Review of Economics, Annual Reviews, vol. 12(1), pages 775-799, August.
    11. Putman, Daniel S., 2020. "The Scope of Risk Pooling," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304480, Agricultural and Applied Economics Association.
    12. Bryan S. Graham, 2019. "Network Data," CeMMAP working papers CWP71/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    13. Konrad Menzel, 2021. "Bootstrap With Cluster‐Dependence in Two or More Dimensions," Econometrica, Econometric Society, vol. 89(5), pages 2143-2188, September.
    14. Fe, Hao, 2023. "Social networks and consumer behavior: Evidence from Yelp," Journal of Economic Behavior & Organization, Elsevier, vol. 209(C), pages 1-14.

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