Normal Approximation in Large Network Models

Fuente: arXiv
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Autores principales: Leung, Michael P., Moon, Hyungsik Roger
Formato: Preprint
Publicado: 2019
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author Leung, Michael P.
Moon, Hyungsik Roger
author_facet Leung, Michael P.
Moon, Hyungsik Roger
contents We prove a central limit theorem for network formation models with strategic interactions and homophilous agents. Since data often consists of observations on a single large network, we consider an asymptotic framework in which the network size diverges. We argue that a modification of ``stabilization'' conditions from the literature on geometric graphs provides a useful high-level formulation of weak dependence which we utilize to establish an abstract central limit theorem. Using results in branching process theory, we derive interpretable primitive conditions for stabilization. The main conditions restrict the strength of strategic interactions and equilibrium selection mechanism. We discuss practical inference procedures justified by our results.
format Preprint
id arxiv_https___arxiv_org_abs_1904_11060
institution arXiv
publishDate 2019
record_format arxiv
spellingShingle Normal Approximation in Large Network Models
Leung, Michael P.
Moon, Hyungsik Roger
Econometrics
Statistics Theory
We prove a central limit theorem for network formation models with strategic interactions and homophilous agents. Since data often consists of observations on a single large network, we consider an asymptotic framework in which the network size diverges. We argue that a modification of ``stabilization'' conditions from the literature on geometric graphs provides a useful high-level formulation of weak dependence which we utilize to establish an abstract central limit theorem. Using results in branching process theory, we derive interpretable primitive conditions for stabilization. The main conditions restrict the strength of strategic interactions and equilibrium selection mechanism. We discuss practical inference procedures justified by our results.
title Normal Approximation in Large Network Models
topic Econometrics
Statistics Theory
url https://arxiv.org/abs/1904.11060