I explore the estimation of transferable utility matching games, encompassing many-to-many matching, marriage and matching with trading networks (trades). I introduce a matching maximum score estimator that does not suffer from a computational curse of dimensionality in the number of agents in a...

We study nonparametric identification of single-agent discrete choice models for bundles and binary games of complete information. We provide conditions under which we can recover both the interaction effects and the distributions of potentially correlated unobservables across goods in...

We develop empirical models of hedonic prices and derive hedonic indices for measuring changes in customer welfare based upon deep learning. We first generate abstract product attributes, or "features," from text descriptions and images using deep neural networks, and then use these attributes...

Accurate, real-time measurements of price index changes using electronic records are essential for tracking inflation and productivity in today's economic environment. We develop empirical hedonic models that can process large amounts of unstructured product data (text, images, prices,...