This paper studies parametric estimation and inference in a dyadic network formation model with nontransferable utilities, incorporating observed covariates and unobservable individual fixed effects. We address both theoretical and computational challenges of maximum likelihood estimation in this complex network model by proposing a new bootstrap aggregating (bagging) estimator, which is asymptotically normal, unbiased, and efficient. We extend the approach to estimating average partial effects and analyzing link function misspecification. Simulations demonstrate strong finite-sample performance. Two empirical applications to Nyakatoke risk-sharing networks and Indian microfinance data find insignificant roles of wealth differences in link formation and the strong influence of caste in Indian villages, respectively.
Quantitative mode stability for the wave equation on the Kerr-Newman spacetime
Risk-Aware Objective-Based Forecasting in Inertia Management
Chainalysis: Geography of Cryptocurrency 2023
Periodicity in Cryptocurrency Volatility and Liquidity
Impact of Geometric Uncertainty on the Computation of Abdominal Aortic Aneurysm Wall Strain
Simulation-based Bayesian inference with ameliorative learned summary statistics -- Part I