This paper develops new limit theory for data that are generated by networks or more generally display cross-sectional dependence structures that are governed by observable and unobservable characteristics. Strategic network formation models are an example. Whether two data points are highly correlated or not depends on draws from underlying characteristics distributions. The paper defines a measure of closeness and primitive conditions on the distribution of observable characteristics, unobservables and functional forms determining interaction between data points. A summability condition over the probability distribution of observable characteristics is shown to be a critical ingredient in establishing limit results. The paper establishes weak and strong laws of large numbers as well as a stable central limit theorem for a class of statistics that include as special cases network statistics such as average node degrees or average peer characteristics.
Limit Theorems for Data with Network Structure by Guido Kuersteiner
Date & Time
23 April 2019
The Institute for Fiscal Studies
7 Ridgmount Street,