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 between 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 α-mixing adapted to networks. The proposed 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: 2024-07-04
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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