Government agencies and modern businesses, from banks and online retailers to social media platforms and search engines, are amassing an unprecedented wealth of data in terms of both size and level of detail. This rapidly growing wealth of “big data” provides new opportunities to improve the quality of economic analysis. This initiative explores the ability of big data to fulfill this promise, with the help of newly developed econometric tools.
BFI-MEBDI Machine Learning Competition 2024
Jointly organized by the Becker Friedman Institute Big Data Initiative and the Minnesota Economics Big Data Institute
OBJECTIVE: Design a Machine Learning algorithm that provides the best out-of-sample prediction (imputation) of individual earnings above a topcoding threshold using information from individuals’ (topcoded) earnings dynamics in years preceding the imputation.
ELIGIBILITY: To be eligible, as of September 2023, you must be in the 2nd year or later in the economics PhD programs at the University of Minnesota Department of Economics or University of Chicago (Kenneth C. Griffin Department of Economics, Booth School of Business, or Harris School of Public Policy) and be in good standing. Each student can enter the competition individually or form a team with one other eligible student from the same school (team of two). To enter the competition, teams must notify their decision to participate to organizers via email by October 31, 2023.
Further details and other competition rules are described in the document below.