We propose a demand estimation method that allows researchers to estimate substitution patterns from unstructured image and text data. We first employ a series of machine learning models to measure product similarity from products' images and textual descriptions. We then estimate a nested logit...

Demand estimates are essential for addressing a wide range of positive and normative questions in economics that are known to depend on the shape-and notably the curvature-of the true demand functions. The existing frontier approaches, while allowing flexible substitution patterns, typically...

An oil lease auction is the classic example motivating a common values model. However, formal testing for common values has been hindered by unobserved auction-level heterogeneity, which is likely to affect both participation in an auction and bidders' willingness to pay. We develop and apply an...

We present new results on the identifiability of a class of nonseparable nonparametric simultaneous equations models introduced by Matzkin (2008). These models combine exclusion restrictions with a requirement that each structural error enter through a "residual index". Our identification...

Empirical models of demand for - and, often, supply of - differentiated products are widely used in practice, typically employing parametric functional forms and distributions of consumer heterogeneity. We re view some recent work studying identification in a broad class of such models. This...