Please find a copy of my CV here.
Econometrics with a focus on Semiparametric Methods, Social Networks, and Panel Data Models.
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.
This paper considers a network formation model when links are potentially measured with error. We focus on a game-theoretical model of strategic network formation with incomplete information, in which the linking decisions depend on agents’ exogenous attributes and endogenous network characteristics. In the presence of link misclassiﬁcation, we derive moment conditions that characterize the identiﬁed set for the preference parameters associated with homophily and network externalities. Based on the moment equality conditions, we provide an inference method that is asymptotically valid when a single network of many agents is observed. Finally, we apply our proposed method to study trust networks in rural villages in southern India.
MRes in Economics