Nonparametric Identification and Estimation of Distance Functions in Network Formation Models with Fixed Effects

Abstract

The second chapter of the dissertation discusses the non-parametric extension of the network formation model in Toth (2018), when the researcher does not assume the functional form of the distance function. An intuitive way for the non-parametric extension is to use the parametric estimator for linear indices coupled with a series expansion. While the technique is generally applicable, it comes with the caveat that the identification of the models must be assured a priori. After demonstrating the applicability of the method on classical models of Manski (1987) and Han (1987), we prove the nonparametric identification of the distance function for the network formation model, and define the corresponding series estimator. We give a proof for consistency, and also analyze the rate of convergence.