Econometrics of Networks with Machine Learning

Abstract

AbstractGraph structured data, called networks, can represent many economic activities and phenomena. Such representations are not only powerful for developing economic theory but are also helpful in examining their applications in empirical analyses. This has been particularly the case recently as data associated with networks are often readily available. While researchers may have access to real-world network structured data, in many cases, their volume and complexities make analysis using traditional econometric methodology prohibitive. One plausible solution is to embed recent advancements in computer science, especially machine learning algorithms, into the existing econometric methodology that incorporates large networks. This chapter aims to cover a range of examples where existing algorithms in the computer science literature, machine learning tools, and econometric practices can complement each other. The first part of the chapter provides an overview of the challenges associated with high-dimensional, complex network data. It discusses ways to overcome them by using algorithms developed in computer science and econometrics. The second part of this chapter shows the usefulness of some machine learning algorithms in complementing traditional econometric techniques by providing empirical applications in spatial econometrics.