We investigate the estimation of models of dynamic discrete-choice games of incomplete information, formulating the maximum-likelihood estimation exercise as a constrained optimization problem which can be solved using state-of-the-art constrained optimization solvers. Under the assumption that...

We investigate the estimation of models of dynamic discrete-choice games of incomplete information, formulating the maximum-likelihood estimation exercise as a constrained optimization problem that can be solved using state-of-the-art constrained optimization solvers. Under the assumption that...

The authors examine prominent placement of search engines’ own services and effects on users’ choices. Evaluating a natural experiment in which different results were shown to users who performed similar searches, they find that Google’s prominent placement of its Flight Search service...

We propose a model for demand estimation in multi-agent, differentiated product settings and present an estimation algorithm that uses reversible jump MCMC techniques to classify agents' types. Our model extends the popular setup in Berry, Levinsohn and Pakes (1995) to allow for the data-driven...

We introduce a new class of matching mechanisms - quantile stable mechanisms - that generate stable matchings that can be seen as a compromise between sides of a two-sided market. We show that responsiveness is a sufficient condition for the existence of such mechanisms and that all such...

This paper offers new empirical evidence about actual Airbnb usage patterns and how they vary across neighborhoods in New York City. We combine unique, census-tract level data from Airbnb with neighborhood asking rent data from Zillow and administrative, census, and social media data on...