Asymptotic Theory Under Network Stationarity

Julius Vainora

Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge

Abstract: This paper develops an asymptotic theory for network data based on the concept of network stationarity, explicitly linking network topology with the dependence be-tween network entities. Each pair of entities is assigned a class based on a bivariate graph statistic. Network stationarity assumes that conditional covariances depend only on the assigned class. The asymptotic theory, developed for a growing network, includes laws of large numbers, consistent autocovariance function estimation, and a central limit theorem. A significant portion of the assumptions concerns random graph regularity conditions, particularly those related to class sizes. Weak dependence assumptions use conditional –-mixing adapted to networks and based on the assigned classes instead of Euclidean space distances. A network-robust standard errors estimator is proposed. The framework is illustrated through an application to microfinance data from Indian villages.

Keywords: Network Dependence; Covariance; Random Graphs; Mixing; Robust Inference (search for similar items in EconPapers)
JEL-codes: C10 C18 C31 C55 D85 (search for similar items in EconPapers)
Date: 2026-01-03
New Economics Papers: this item is included in nep-ecm and nep-net
Note: jv429
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Persistent link: https://EconPapers.repec.org/RePEc:cam:camdae:2439

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