We consider nonparametric identification in models of differentiated products markets, using only market level observables. On the demand side we consider a nonparametric random utility model nesting random coefficients discrete choice models widely used in applied work. We allow for...

We consider identification of nonparametric random utility models of multinomial choice using "micro data," i.e., observation of the characteristics and choices of individual consumers. Our model of preferences nests random coefficients discrete choice models widely used in practice with...

We develop tests for common values at first-price sealed-bid auctions. Our tests are nonparametric, require observation only of the bids submitted at each auction, and are based on the fact that the winner's curse' arises only in common values auctions. The tests build on recently developed...

This paper studies the bidding behavior of firms in U.S. Forest Service timber auctions in 1976--1990. When conducting timber auctions, the Forest Service publicly announces its estimates of the tract characteristics before the auction, and each bidder additionally has an opportunity to inspect...

A policy debate centers around the question how news aggregators such as Google News affect traffic to online news sites. Many publishers view aggregators as substitutes for traditional news consumption while aggregators view themselves as complements because they make news discovery easier. We...

We develop new semiparametric methods for estimating treatment effects. We focus on a setting where the outcome distributions may be thick tailed, where treatment effects are small, where sample sizes are large and where assignment is completely random. This setting is of particular interest in...

We present a new perspective on the Synthetic Control (SC) method as a weighted least squares regression estimator with time fixed effects and unit weights. This perspective suggests a generalization with two way (both unit and time) fixed effects, and both unit and time weights, which can be...

In many prediction problems researchers have found that combinations of prediction methods ("ensembles") perform better than individual methods. A simple example is random forests, which combines predictions from many regression trees

A common challenge in estimating the long-term impacts of treatments (e.g., job training programs) is that the outcomes of interest (e.g., lifetime earnings) are observed with a long delay. We address this problem by combining several short-term outcomes (e.g., short-run earnings) into a...

When researchers develop new econometric methods it is common practice to compare the performance of the new methods to those of existing methods in Monte Carlo studies. The credibility of such Monte Carlo studies is often limited because of the freedom the researcher has in choosing the design....