Joshua Cherry
Joshua Cherry

MANAGERIAL ECONOMICS & DECISION SCIENCES
Visiting Assistant Professor of Managerial Economics & Decision Sciences

Print Overview

Joshua Cherry is a Visiting Assistant Professor of Managerial Economics and Decision Sciences. He joined the faculty in 2011, after completing his PhD in Economics at the University of Michigan. His research interests include game theory, microeconomic theory, information economics and experimental economics. His most recent work has studied the role of information in long term strategic interactions.

Print Vita
Education
Ph.D., 2011, Economics, University of Michigan
M.A., 2007, Economics, University of Michigan
B.A. , 2004, Economics , University of California at Berkeley , High Honors

Academic Positions
Visiting Assistant Professor , Department of Managerial Economics and Decision Sciences, Kellogg School of Management, Northwestern University, 2011-present
Graduate Student Instructor,, Ph.D. Microeconomic Theory, University of Michigan , 2006-2008

Other Professional Experience
Referee, Econometrica, American Economic Review, Theoretical Economics, Review of Economic Studies, Journal of Economic Theory, Journal of Industrial Economics, Economic Inquiry National Economic Research Associates, Research Associate , 2004-2005
Summer Analyst - Research Group , Dimensional Fund Advisors, 2003-2004

 
Print Research
Working Papers
Cherry, Joshua, Steve Salant and Neslihan Uhler. 2010. Size Matters (In Output-Sharing Groups): Voting to End the Tragedy of the Commons.
Cherry, Joshua. Comparing Mediators.
Cherry, Joshua, Dmitry Lubensky and Barbur De Los Santos. Partial Information Disclosure in Search Markets.
Cherry, Joshua and Lones Smith. Strategically Valuable Information.
Cherry, Joshua and Lones Smith. Unattainable Payoffs for Repeated Games of Private Monitoring.
Cherry, Joshua. 2004. The Limits of Arbitrage Evidence from Exchange Traded Funds.

 
Print Teaching
Full-Time / Part-Time MBA
Business Analytics II (DECS-431-0)
This sequel to DECS-430 extends the statistical techniques learned in that course to allow for the exploration of relationships between variables, primarily through multivariate regression. In addition to learning basic regression skills, including modeling and estimation, students will deepen their understanding of hypothesis testing and how to make inferences and predictions from data. Students will also learn new principles such as identification and robustness. The course has an intense focus on managerially relevant applications, cases and interpretations.