This much we know – Lars Hansen’s work has had a profound impact on our understanding of economics. And while even within the field of econometrics there is general agreement that Hansen’s work is the most technical and most difficult to explain to a layperson, his brilliance is spoken of in almost reverential terms.
Hansen, the David Rockefeller Distinguished Service Professor in Economics, Statistics, & the College and the Director of the Becker Friedman Institute, both at the University of Chicago, was recognized in December 2013, with the Nobel Prize in Economics.
"USU was an environment that gave me the opportunity to transition from an erratic high school student to someone who would be prepared to go to a top Ph.D. program."
What is well-known by now is that this giant in his field is a graduate of Logan High School and Utah State University, where he studied Math, Political Science, and Economics.
So what was the path from Logan to Stockholm, by way of Chicago? We sat down with Dr. Hansen to find out about that, the Nobel ceremony, the Generalized Method of Moments, his seminal work in econometrics, and how today’s Aggies can continue to dare mighty things.
My particular experience was a bit unusual, although I’m sure they all are. I was actually sick in bed for the whole day until about 3 p.m. My wife was also sick. With appropriate medication, we got ready for the event and it ended up working out nicely for us.
The event itself was truly exciting. It was most enjoyable to share it with friends and family. It was also nice to share the podium with other distinguished scholars and receive the recognition. I wish sickness hadn’t gone along with the festivities, but with help of proper medical care it was still a remarkable experience.
I grew up as a so-called faculty brat. My father was a biochemist who subsequently became a college administrator. Thus, during my youth, I was aware of the role of scholarship and the academic lifestyle. On other hand, my last two years of high school were a bit rough on me, and I did not do so well. So, when I went to Utah State, I looked at this experience as, “Well, here’s my chance. If I’m really going to have an opportunity to do something special down the road, I’d better crank it up a notch.”
What was very nice about my experience at Utah State was that it allowed me to explore various different fields and determine what really excited me. I made contact with some very helpful professors who gave me some important career advice and one-on-one attention that I remember to this day. For example, Doug Alder, who taught European history, advised me to figure out my talents, figure out what fascinated me, and do something special with those skills and interests. That turned out to be very valuable advice.
It is hard for me to pinpoint how I changed during all this, except that I became more energetic and a bit more self-confident.
USU was an environment that gave me the opportunity to transition from an erratic high school student to someone who would be prepared to go to a top PhD program. My experience at USU worked out tremendously well for me.
I started off in chemistry, influenced in part by my dad. While I could handle theoretical parts fine, I wasn’t much into lab work. I explored the field of political science because at the time I liked political questions. I was in college during the Vietnam War era, and I had a desire to be socially relevant. I also took some honors history classes I enjoyed.
But I liked mathematics. Mike Windham, a very gifted teacher, introduced me to the beauty of mathematics. Given my interest in social sciences and mathematics, by my junior year, economics seemed like the way to go. My first economics classes were intermediate level microeconomics and macroeconomics classes I took in my junior year.
Then, upon the advice of Bartell Jensen, I shifted to PhD-level economics classes in my senior year. He gave me some very useful advice on how to design a more advanced curriculum to be ready for graduate school, so I had some very accelerated training in economics. Certainly, using first mathematics and then statistics to think about social problems turned out to be a natural lead-in to econometrics.
The field of econometrics uses statistical methods to better understand and assess economic models—what the models are good for and what their limitations are. It approaches this using some of the formal tools of mathematics and statistics. In part, my experience at USU made me think about connections between mathematics, statistics and eventually economics.
Once I went to graduate school in Minnesota, my thesis adviser Christopher Sims and his colleague Thomas Sargent were writing very exciting papers about econometric methods and their applications to macroeconomic time series data. The research they were doing at that time was recognized in 2011 when they won the Nobel Prize. I found this and other related research to be fascinating, and my USU education helped me to appreciate it.
My father spent much of his career at public schools—the University of Wisconsin, University of Utah, University of Illinois, and Michigan State University, and then returning to USU. He was very committed to public schools and thought they were a vital component to education. Although my father didn’t push me directly to go to a public school for a PhD program, he was certainly pleased that I did. It turned out Minnesota was the great match for me.
Although I’ve spent much of my professional career at private schools, I certainly have to say that for someone like me, the public school system was just critical. When I didn’t have the stellar background to get into an elite college, a public university gave me the opportunity to do special things and excel very quickly, preparing me for an advanced education.
The nice thing about top public universities is that there are opportunities to design a wonderful and challenging curriculum that can prepare you for different and exciting career trajectories. I remember a few years back when my son was thinking about what college he wanted to go to. My message was always the same: it’s wonderful if you get into a great school but it’s even more important that once you get there, you take challenging courses in areas that interest you. The key thing in college is to design curricula that include courses on subjects that excite you that you are willing to work hard at to advance your understanding.
Let me talk about statistical models more generally: Good econometric research looks at models and figures out what they are good for, what their liabilities are, and suggests ways they might be improved. ?
The challenge when you do this is that models, at the end of the day, are just abstractions or simplifications. They are not a complete, full description of reality. You know they can’t explain everything. You have to figure out if they get close enough to answers you care about to be useful.
If someone says they can find evidence that makes a model false, by itself, that is not very exciting. What’s valuable is discovering when models can be corrected in insightful ways. That’s the more challenging part of doing good, well-constructed econometric work.
I personally believe that the formalism coming from statistics and econometrics is tremendously useful. It makes the discussions more disciplined. When you talk about successes and failures, with some mathematical formalism, you can be more precise about what you mean.
This formal type of empirical investigation has helped us expose gaps in models and has led to valuable follow-up research that explore ways to make the models more insightful. Suppose we change investor preferences concerning risk and uncertainty; suppose we change market structures. How do these changes alter the analysis? Where do we get big gains in terms of more empirical success?
We now know much more about so-called dynamic stochastic general equilibrium models, how they work, and their successes and failures than we did a couple of decades ago. Using formal methods has really helped get us to that point.
Many challenges in our understanding remain about the connections between macroeconomics and financial markets. I would say that the financial crisis exposed many gaps where we thought we understood better than we did. There’s a sense in which the crisis provided new data to rethink some important questions. I think we still have a way to go to repair or alter the models to make them very useful for the oversight of financial markets. ?I view that as one of the great challenges in my field at least for the next five to ten years.
I’m working in three different areas. First, I remain keenly interested in this topic of uncertainty and how we should think about decision-making in the presence of uncertainty.
I think of uncertainty broadly conceived. If we write down some model with a formal probability structure to it, the model can tell me how to assign probabilities to a whole bunch of future events. That’s what economists typically call “risk”—this notion that the model shows you how to assign probabilities to the uncertain future.
Going beyond risk, there are other forms of uncertainty that are important. One is when we don’t really know what the correct model is. Among the class of models we consider, we suspect that none are quite right, but which is better? If none are correct, are the points where they make mistakes important or not? How we make decisions in the face of this kind of model uncertainty is an area I find fascinating and important for both model-building and policy alike.
Another area I’ve worked on in recent years is asset pricing. We’ve built these dynamic models with many periods to them to think about investment problems. A lot of the way we’ve framed the empirical evidence is in terms of so-called risk-return relationships. These relationships are typically over short time periods. I am interested in how you can think through the risk-return implications over alternative investment horizons, and how uncertainty plays different roles for different time horizons.
In collaboration with others, I’m trying to develop new tools to think about this question. How do the market uncertainty prices behave over different investment horizons?
Finally, I’m interested in two applied areas—both critically important when it comes to uncertainty. One is this whole notion of how we want to think about financial oversight in environments where we really don’t fully understand some important concepts. There is a regulatory and popular mandate for financial market oversight. And there is this concept of “systemic risk” in the financial sector that is poorly understood, yet we’re trying to regulate it. So how do we design fruitful policy in areas where our knowledge is incomplete in important ways?
I’m also involved in a project on the economics of climate change. This is another case where models are only very crude guides toward thinking about uncertainty in the future. Again, how does that interact with the design of policy?
So those are plenty of problems to keep me going. Trying to think of ways to keep them tractable is a big challenge.
Economic questions are pervasive in everyday life. How we allocate resources across society is a fundamental question. We study economics to get a better handle on sensible ways to allocate resources across different types of ventures and activities. To me, economics is at the heart of a lot of important matters; there are opportunities to make big improvements and expand our limited knowledge.
It’s great to challenge yourself to be working on big problems, especially in scholarship.?The hard part for me is that when I’m doing research myself, I can’t tell which projects are going to turn out to be influential. I am often surprised at which ones receive attention and which ones are largely ignored.
A lot of research in academia has to do with taking some gambles, trying different things, and accepting the fact that some of the ventures are going to end up going nowhere and being ignored. Part of the path to success is to not be afraid of being wrong. Keep trying new things. Sometimes you’ll be successful, sometimes not.
One thing that helps is to try to be aware of what’s going on in complementary fields. As fields get more advanced over time, they necessarily get more and more specialized. While specialization is inevitable, it’s good to push yourself to have some breadth as well. It’s advantageous to have exposure to lots of different research areas, because ideas that have worked out well in one area might be fruitful in others.
People sometimes get frustrated with my papers because I draw on insights from statistics, macroeconomics, and finance, all in the same paper. They think it’s too much. Maybe so, but I don’t know how to do the research I do without drawing on insights from all three areas.
When it comes to exploring ideas, don’t be afraid of failure. ?Of course, if you’re failing all the time, that’s a bit of a problem! You have to approach research in ways where you also have a chance of success. And if you ask me how to do that, I wish I knew a simple answer. I wake up every morning and try to address that one!
"We sat next to each other in Ken Lyon’s graduate economic theory class. Lars was clearly the smartest guy in the room, but he never seemed interested in proving that. He was generous and fun, and one of the deepest thinkers I have ever met—genuinely curious about the way the world works, and not satisfied with simple-minded answers."
- Doug Anderson, Dean and Professor, Huntsman School of Business
"I remember Dr. Hansen as a brilliant professor who taught one of the most challenging classes in the University of Chicago’s Ph.D. program. What stands out is not only his commitment to connect with and inspire students inside the classroom, but also outside of the classroom by attending student functions and challenging us to make substantive academic contributions."
- Aspen Gorry, Assistant Professor, Economics & Finance, Huntsman School of Business Ph.D., University of Chicago, 2009
"In 1974, Lars and I were student directors of a statewide poll to predict the senate race. The raw data showed that Wayne Owens would defeat Jake Garn. Lars looked at the data and recognized a problem, so he wrote a program that corrected the data to Utah’s demographics. The new result predicted that Jake Garn would win the race. As usual, Lars was right."
- Randy Simmons, Charles G. Koch Professor of Political Economy, Huntsman School of Business