Department of Economics
2211 Campus Dr
Evanston, IL 60208
Ph.D., Economics, Northwestern University, 2018 (expected)
MA, Economics, Northwestern University, 2013
M.Sc., Mathematics, Worcester Polytechnic Institute, 2010
B.Sc., Mathematics, Worcester Polytechnic Institute, 2009
Primary Fields of Specialization
Secondary Fields of Specialization
Job Market Paper
“Identification of auctions with incomplete bid data in the
presence of unobserved heterogeneity” (JMP)
This paper derives novel nonparametric identification results for auction models with incomplete bid data and finite unobserved heterogeneity (UH). By exploiting the Markov property of order statistics, I show that the joint distribution of bidders’ valuations and the UH is point identified from an incomplete set of bids. The result holds if the econometrician either observes (any) five order statistics of the bids in each auction or only three along with an instrument, and without imposing any functional form restriction on how the UH affects valuations. This data structure is encountered in many empirical settings, such as ascending auctions in which the winner’s bid is usually not observed. I establish these results under weak distributional assumptions. For second price auctions, the result holds generically over the space of possible distributions of valuations and UH, and for first price auctions, it holds when the conditional distribution of valuations varies monotonically with the UH in the reverse hazard rate order. I show that identification can be extended to settings where the number of potential bidders is unobserved, as is often the case in online auctions. Finally, I provide easily implementable nonparametric estimation procedures, and simulation results show that they perform well for samples of moderate size.
Model Selection for Treatment Choice: Penalized Welfare Maximization (last version)
Joint with Max Tabord-Meehan
This paper studies a new statistical decision rule for the treatment assignment problem. Consider a utilitarian policy maker who must use sample data to allocate one of two treatments to members of a population, based on their observable characteristics. In practice, it is often the case that policy makers do not have full discretion on how these covariates can be used, for legal, ethical or political reasons. Even in cases where policy makers have leeway in how to assign treatment, plausible assumptions may generate useful constraints on treatment assignment. We treat this constrained problem as a statistical decision problem, where we evaluate the performance of decision rules by their maximum regret. We adapt and extend results from statistical learning to develop a decision rule which we call the Penalized Welfare Maximization (PWM) rule. Our study of the PWM rule, which builds on the the Empirical Welfare Maximization (EWM) rule developed in Kitagawa and Tetenov (2015), differs from it in two aspects. First, by imposing additional regularity conditions on the data generating process, we derive bounds on the maximum regret of our rule for a broad set of classes of treatment allocations of infinite VC dimension. In particular, we show that our rule is well suited to deal with some allocations of infinite VC dimension that can arise in applications. Second, we argue that our rule can provide a reduction in point-wise regret in situations where sample size is small compared to the complexity of the constraints on assignment. We illustrate our method in a small simulation study where our rule is able to achieve smaller regret than EWM in an empirically relevant setting. We conclude by applying our rule to data from the Job Training Partnership Act (JTPA) study.