Uncertainty in Economics: Why Should We Care?

Lars Peter Hansen is David Rockefeller Distinguished Service Professor in economics, statistics, Booth School of Business and the College at University of Chicago. He is a leading expert in economic dynamics who works at the forefront of economic thinking and modeling, drawing approaches from macroeconomics, finance, and statistics. He is a recipient of the 2013 Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel. Dr. Hansen has made fundamental advances in our understanding of how economic agents cope with changing and risky environments. He has contributed to the development of statistical methods designed to explore the interconnections between macroeconomic indicators and assets in financial markets. The Nobel Prize recognizes this work, which has been used to test theories and models that have shaped our modern understanding of asset pricing. His recent research explores how to quantify intertemporal risk-return tradeoffs and ways to model economic behavior when consumers and investors struggle with uncertainty about the future. Improving models that measure risk and uncertainty have important implications for financial markets, fiscal policy, and the macroeconomy.

Dr. Hansen joined the faculty of the University of Chicago’s Department of Economics in 1981. He currently directs the Macro Finance Research Program housed under the Becker Friedman Institute. In addition to the Nobel prize, he has also received many other awards and honors including 2010 BBVA Foundation Frontiers of Knowledge Award, the CME Group-MSRI Prize in 2008, the Erwin Plein Nemmers Prize in 2006, and the Frisch Medal in 1984. Dr. Hansen is a fellow of the National Academy of Sciences and the American Finance Association. He also is a member of the American Academy of Arts and Sciences and past president of the Econometric Society. Hansen holds a bachelor’s degree in mathematics and political science from Utah State University and a doctorate in economics from the University of Minnesota.



False pretenses of knowledge about complicated economic situations have become all too common in public policy debates. While we do know some things, we don’t know everything. We believe that prudent decision-making should acknowledge what we don’t know. Decision makers should strive to quantify dimensions of their ignorance and adjust their decisions accordingly. My recent research describes a tractable approach for acknowledging, characterizing, and responding to the limited understandings discovered by researchers’ efforts to interpret existing evidence by using theories and statistical methods available at any particular moment. An economic model tells how chance, occurrences and purposeful decisions influence future outcomes. Economic researchers use formal statistical models to describe and interpret data and to formulate policy advice for government and private decision-makers. Whether they acknowledge it explicitly or not, real world decision makers also use models or “views” about how their decisions affect future outcomes. Because they ignore some forces and oversimplify others, all models are just approximations to reality, some better than others depending on the purposes to which they are put. Furthermore, at any time we can choose among multiple models and are unsure how much credibility to assign to each of them. Data can surely help us assess the credibility of alternative models, but the real world is so complicated and data so limited that data can only tell us so much. Therefore, economic modelers and decision makers require ways to express their opinions about the plausibility and usefulness of alternative models for the problem at hand. Because data are only partially informative about a model’s plausibility, a decision maker’s purpose as well as his or her “subjective belief” play important roles too. The more complex is the situation, the bigger becomes the challenge of confronting uncertainty. in meaningful ways. Thus, when there are more environmental intricacies, there is greater scope of a broader approach to uncertainty that is important. This research aims to convert insights from formal mathematical analysis into operational tools of analysis for understanding, among other topics, how financial markets work and how alternative fiscal and monetary policies can be assessed.