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- W3125651330 abstract "We develop a new class of random-graph models for the statistical estimation of network formation -- subgraph generated models (SUGMs) -- that allow for substantial correlation in links. Various subgraphs (e.g., links, triangles, cliques, stars) are generated and their union results in a network. We show that all SUGMs are identified and, further, establish the consistency and asymptotic distribution of parameter estimates in empirically relevant cases. We show that a simple four-parameter SUGM matches basic patterns in observed networks more closely than four standard models (with many more dimensions): (i) stochastic block models; (ii) models with node-level unobserved heterogeneity; (iii) latent space models; (iv) exponential random graphs. We illustrate the framework's value further via several applications using networks from rural India. We study whether network structure helps enforce risk-sharing, whether cross-caste interactions are more likely to be private, and how the introduction of microcredit changes network formation incentives. We also develop a new central limit theorem for correlated random variables, which is required to prove our results and is of independent interest." @default.
- W3125651330 created "2021-02-01" @default.
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- W3125651330 date "2016-11-23" @default.
- W3125651330 modified "2023-09-27" @default.
- W3125651330 title "A Network Formation Model Based on Subgraphs" @default.
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