Nonparametric Regression with Dyadic Data

Abstract: This paper studies the identification and estimation of a nonparametric nonseparable dyadic model where the structural function and the distribution of the unobservable random terms are assumed to be unknown. The identification and the estimation of the distribution of the unobservable random term are also proposed. I assume that the structural function is continuous and strictly increasing in the unobservable heterogeneity. I propose suitable normalization for the identification by allowing the structural function to have some desirable properties such as homogeneity of degree one in the unobservable random term and some of its observables. The consistency and the asymptotic distribution of the estimators are proposed. The finite sample properties of the proposed estimators in a Monte-Carlo simulation are assessed.