We introduce sparse random projection, an important dimension-reduction tool from machine learning, for the estimation of discrete-choice models with high-dimensional choice sets. Initially, high-dimensional data are compressed into a lower-dimensional Euclidean space using random projections....

We introduce sparse random projection, an important tool from machine learning, for the estimation of discrete-choice models with high-dimensional choice sets. First, the high-dimensional data are compressed into a lower-dimensional Euclidean space using random projections. In the second step,...

This paper proposes a revealed preference test of network formation models. Specifically, I consider network formation models where agents are (1) strategic, (2) externalities are confined to within an agent's k-neighborhood, where k can be varied. I show that this model can be tested using...