Tom Cook is a Data Scientist in the Economic Research Department of the Federal Reserve Bank of Kansas City. He joined the bank in August 2016 after completing his PhD in Political Science at the University of Colorado. He also holds an MPA from DePaul University, and a BA in Philosophy and Political Science from the University of Iowa. His substantive research interests are the roles of time and information transmission in political and economic strategic behavior. Methodologically, his research at the bank focuses on the development of machine learning, neural networks, and advanced statistical models for use in economic research.
Professional Journals and Books
- Book Chapter: "Neural Networks" in Macroeconomic Forecasting in the Era of Big Data, Edited by Peter Fuleky, Springer: 2020.
- "The First Image Reversed: IGO Signals and Mass Political Attitudes" with David H. Bearce, Review of International Organizations, December 2018, 13(4): 595-619. DOI: 10.1007/s11558-017-9293-0
- "Local competition amplifies the corrosive effects of inequality" with DB Krupp, Psychological Science, 2018, 29(5): 824-833. DOI: 10.1177/0956797617748419
- "Using Linguistic Networks to Explain Strength of Intellectual Property Rights" with Amy H. Liu, World Development, 2016, 87(C):128-138. DOI: 10.1016/j.worlddev.2016.06.008
Research Working Papers
- Assessing Macroeconomic Tail Risks in a Data-Rich Environment
with Taeyoung Doh, RWP 19-12
- Macroeconomic Indicator Forecasting with Deep Neural Networks
with Aaron Smalter Hall, RWP 17-11
- Assessing the Risk of Extreme Unemployment Outcomes
with Taeyoung Doh, August 28, 2019
- Revamping the Kansas City Financial Stress Index Using the Treasury Repo Rate
with Taeyoung Doh, October 24, 2018
- How Much Would China's GDP Respond to a Slowdown in Housing Activity?
with Jun Nie and Aaron Smalter Hall, September 12, 2018