A Semiparametric Network Formation Model with Unobserved Linear Heterogeneity

Luis Candelaria

The Warwick Economics Research Paper Series (TWERPS) from University of Warwick, Department of Economics

Abstract: This paper analyzes a semiparametric model of network formation in the presence of unobserved agent-specific heterogeneity. The objective is to identify and estimate the preference parameters associated with homophily on observed attributes when the distributions of the unobserved factors are not parametrically specified. This paper offers two main contributions to the literature on network formation. First, it establishes a new point identification result for the vector of parameters that relies on the existence of a special regressor. The identification proof is constructive and characterizes a closed-form for the parameter of interest. Second, it introduces a simple two-step semiparametric estimator for the vector of parameters with a first-step kernel estimator. The estimator is computationally tractable and can be applied to both dense and sparse networks. Moreover, I show that the estimator is consistent and has a limiting normal distribution as the number of individuals in the network increases. Monte Carlo experiments demonstrate that the estimator performs well in finite samples and in networks with different levels of sparsity.

Keywords: Network formation; Unobserved heterogeneity; Semiparametrics; Special regressor; Inverse weighting (search for similar items in EconPapers)
Date: 2020
New Economics Papers: this item is included in nep-cdm, nep-ecm, nep-net and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:wrk:warwec:1279

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