Macroeconomic Shocks and Economic Growth in Asia

The Macro Finance Society organizes two conferences annually where researchers present recent work at the intersection of financial economics and macroeconomics. The methodological focus is on dynamic structural models that are grounded in data.

Machine learning techniques are being actively pursued in the private sector and have been widely adopted in fields such as computational biology and computer vision. However, the role of machine learning in economics has so far been limited. This workshop was organized to provide a forum to discuss how ideas and techniques from machine learning could be applied to economic questions. The workshop will bring together researchers from computer science, statistics, econometrics and applied economics to foster interactions and discuss different perspectives on statistical learning and its potential impact on economics.

The workshop began with overview talks on machine learning and statistics by researchers from outside of economics. Three following sessions were organized around the themes of causal inference, prediction, and networks and complex data. Each session included the presentation of papers in economics that make use of machine learning methodology, followed by a discussion by researchers from multiple communities.

Downers Grove North High students earned first place in the Northwest Suburban Fall 2016 session of The Stock Market Game program after competing against 860 other high school teams. Students Owen Wahlgren, Eric Marcanio, Jacob Ridderhoff and Tyler Mertes, supported by their AP Macroeconomics Teacher Mr. Tom Saam, created and managed their virtual portfolio from September to December 2016.