Specification Testing for Dyadic Linear Regression Models
报告人： Jiahao Lin（ Fuyao University of Science and Technology）
时间：2026-11-05 13:30-14:50
地点：Room 217, Guanghua Building 2
Abstract:
This paper develops specification tests for linear conditional-mean models with undirected dyadic data, a setting that, to the best of our knowledge, has not previously been addressed in the literature. We establish a uniform projection theorem that reduces the dyadic process to its latent first-order node projections under shared-node dependence. We then show that a raw first-order node-multiplier bootstrap is valid when this node component is nondegenerate but double-counts dyad-specific variation when dyads are independent. An exact covariance decomposition motivates a corrected Gaussian bootstrap, whose validity is established separately under shared-node dependence and independent dyads. The continuum moment characterization is omnibus, while the implemented Kolmogorov-Smirnov and Cramér-von Mises tests are consistent against fixed alternatives captured by the evaluation grid. We also characterize their power against rate-appropriate local alternatives. Simulations show that the corrected Kolmogorov-Smirnov test provides the most stable size control while retaining substantial local power. An application to the Lazega law-firm network rejects additive linear and quadratic specifications but does not reject a richer specification containing an economically relevant interaction.
About the Speaker:
iahao Lin is a Research Assistant Professor at the School of Digital Economy and Management, Fuyao University of Science and Technology. His research focuses on econometric theory, particularly bootstrap and jackknife methods and robust inference for panel, time series, and dyadic data. He develops practical statistical methods that accommodate complex dependence and unobserved heterogeneity. He has received research funding from the National Natural Science Foundation of China (NSFC). His work has been published or accepted for publication in the Journal of Econometrics, Journal of Business & Economic Statistics, and Journal of Empirical Finance.
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