  We study the identification and estimation of a multidimensional screening
model, where a monopolist sells a multi-attribute product to consumers with
private information about their multidimensional preferences. Under optimal
screening, the seller designs product and payment rules that exclude "low-type"
consumers, bunches the "medium types" at "medium-quality" products, and
perfectly screens the "high types." We determine sufficient conditions to
identify the joint distribution of preferences and the marginal costs from data
on optimal individual choices and payments. Then, we propose estimators for
these objects, establish their asymptotic properties, and assess their
small-sample performance using Monte Carlo experiments.
