Jeremy Fox

About

Jeremy Fox is a Professor of Economics at Rice University, specializing in empirical industrial organization. His research also encompasses econometrics and labor economics. Fox has previously worked at the University of Chicago and the University of Michigan. His work has addressed industries such as mobile phones, automobile manufacturing, and venture capital, and includes studies on firm productivity and labor market issues. He is known for his work on estimating models of two-sided matching games and demand. Fox holds a Ph.D. and M.A. in Economics from Stanford University, obtained in 2003, and a B.A. in Economics, Political Science, and Statistics from Rice University, earned in 1998.

Research topics

• Econometrics
• Economics
• Mathematics
• Computer science
• Mathematical economics

Selected publications

• Estimating matching games with transfers

  Quantitative Economics · 2018-03-01 · 98 citations

  articleOpen access1st authorCorresponding

  I explore the estimation of transferable utility matching games, encompassing many-to-many matching, marriage, and matching with trading networks (trades). Computational issues are paramount. I introduce a matching maximum score estimator that does not suffer from a computational curse of dimensionality in the number of agents in a matching market. I apply the estimator to data on the car parts supplied by automotive suppliers to estimate the valuations from different portfolios of parts to supp…

  PublisherOA PDFDOI

• Unobserved Heterogeneity in Matching Games

  Journal of Political Economy · 2018-03-16 · 54 citations

  article1st authorCorresponding

  Agents in two-sided matching games vary in characteristics that are unobservable in typical data on matching markets. We investigate the identification of the distribution of unobserved characteristics using data on who matches with whom. In full generality, we consider many-to-many matching and matching with trades. The distribution of match-specific unobservables cannot be fully recovered without information on unmatched agents, but the distribution of a combination of unobservables, which we…

  PublisherDOI

• Nonparametric identification and estimation of random coefficients in multinomial choice models

  The RAND Journal of Economics · 2016-01-08 · 53 citations

  article1st authorCorresponding

  We show how to nonparametrically identify the distribution of unobservables, such as random coefficients, that characterizes the heterogeneity among consumers in multinomial choice models. We provide general identification conditions for a class of nonlinear models and then verify these conditions using the primitives of the multinomial choice model. We require that the distribution of unobservables lie in the class of all distributions with finite support, which under our most general assumptio…

  PublisherDOI

• A simple nonparametric approach to estimating the distribution of random coefficients in structural models

  Journal of Econometrics · 2016-09-19 · 47 citations

  article1st authorCorresponding
  PublisherDOI

• A note on identification of discrete choice models for bundles and binary games

  Quantitative Economics · 2017-11-01 · 34 citations

  articleOpen access1st authorCorresponding

  We study nonparametric identification of single-agent discrete choice models for bundles (without requiring bundle-specific prices) and of binary games of complete information. We show that these two models are quite similar from an identification standpoint. Moreover, they are mathematically equivalent when we restrict attention to the class of potential games and impose a specific equilibrium selection mechanism in the data generating process. We provide new identification results for the two…

  PublisherOA PDFDOI

Frequent coauthors

• Patrick Bajari

  24 shared

• Stephen Ryan

  Washington University in St. Louis

  13 shared

• Kyoo il Kim

  Abbott (Switzerland)

  11 shared

• Amit Gandhi

  8 shared

• Valérie Smeets

  6 shared

• Chenyu Yang

  Peking University

  5 shared

• Jacob K. Goeree

  UNSW Sydney

  5 shared

• Michelle S. Caird

  Orthopaedic Research Laboratories

  4 shared

Similar researchers at Rice University

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