Limit Theorems for Network Dependent Random Variables

Abstract

This paper is concerned with cross-sectional dependence arising because observations are interconnected through an observed network. Following Doukhan and Louhichi (1999), we measure the strength of dependence by covariances of nonlinearly transformed variables. We provide a law of large numbers and central limit theorem for network dependent variables. We also provide a method of calculating standard errors robust to general forms of network dependence. For that purpose, we rely on a network heteroskedasticity and autocorrelation consistent (HAC) variance estimator, and show its consistency. The results rely on conditions characterized by tradeoffs between the rate of decay of dependence across a network and network's denseness. Our approach can accommodate data generated by network formation models, random fields on graphs, conditional dependency graphs, and large functional-causal systems of equations.

References in corpus (4)

• Estimating Average Causal Effects Under General Interference, with Application to a Social Network Experiment
• Normal approximation under local dependence
• Limit Theorems for Data with Network Structure
• Inference in Models of Discrete Choice with Social Interactions Using Network Data

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