The first chapter of this dissertation develops a two-stage inference method for structural parameters in the linear instrumental variables model. In the first stage, a new statistic is used to detect whether the correlation between the structural error and the reduced form error is small. In the second stage, a...
This dissertation is composed of three chapters, each contributing to different aspects of the literature of partially identified econometric models.
In the first chapter, I introduce a bootstrap procedure to perform inference in the class of partially identified econometric models defined by finitely many moment equalities and inequalities. I provide...
Last two decades have seen a surge of interests in approaches that leverage network structure in machine learning models. For many networks, not only the connections of the network but also the network attributes, such as node attributes and dyadic attributes, are observed. This heterogeneity in networks raises new challenges...