SMU SOE Seminar Series (March 5, 2025): Estimation and Inference in Dyadic Network Formation Models with Nontransferable Utilities

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ABSTRACT

This paper studies estimation and inference in a dyadic network formation model with observed covariates, unobserved heterogeneity, and nontransferable utilities. With the presence of the high dimensional fixed effects, the maximum likelihood estimator is numerically difficult to compute and suffers from the incidental parameter bias. We propose an easy-to-compute one-step estimator for the homophily parameter of interest, which is further refined to achieve √N-consistency via split-network jackknife and efficiency by the bootstrap aggregating (bagging) technique. We establish consistency for the estimator of the fixed effects and prove asymptotic normality for the unconditional average partial effects. Simulation studies show that our method works well with finite samples. We provide two empirical applications, one using the Nyakatoke risk-sharing network dataset and the other using the India micro-finance network dataset, and obtain economically meaningful results.

Keywords: Dyadic Network Formation, Unobserved Heterogeneity, Nontransferable Utilities, Bagging, Machine Learning, Cramér-Rao Lower Bound.

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