Bhagwan: It is a great opportunity to talk to Lars after many years. So let me start by talking about your Nobel Prize. When we heard about the news, of course we were delighted but not surprised that you got that prize. And the students asked me, why Gene Fama, Rob Shiller and Lars Hansen? And my answer was maybe I will ask Lars to see if I have actually butchered it or not? My answer was that Gene Fama says stock prices must be volatile because they are efficient. Bob Shiller said they are too volatile to be efficient and Lars said I will tell you how you can find out who is right? Is that right?
Lars Hansen: I look at it in a different way. Of course after I got the Nobel Prize I was asked by many people who is right Bob Shiller or Gene Fama, and where I stood on the spectrum? And my response was that you should stop thinking about things as some type of line segment; a point here and drawing a line and trying to put me somewhere in between. Think of it as a triangle. My view is that they are both wrong. Let me elaborate a little bit on that. Market efficiency is really an abstract benchmark construct. In some sense of course it is wrong. It makes a bunch of simplifying assumptions that can’t be right. It is a simplified model describing a complex world. So in that sense Bob Shiller has to be correct; markets are not efficient. But the truth is that any time an economist writes down a model, it is wrong. So the question is, is it wrong in a way that is insightful or not. I kind of found the correct discussion should be when do we use for the efficient markets model as a benchmark? Often, even if you take the Schiller perspective on things it is still a useful benchmark because you want to characterise how good or bad the behaviour is. And the truth is we really haven’t produced an alternative Bob Schiller model asset price other than the markets are inefficient or maybe there are behavioural biases. My own research was influenced a lot by Schiller’s characterisation of excess volatility. We have to remember that Bob Shiller’s characterisations of excess volatility were not just efficient markets, it is efficient markets plus a bunch of other assumptions at the same time. And he got many of us to think more creatively about models of asset markets.
Market efficiency is really an abstract benchmark construct. In some sense of course it is wrong. It makes a bunch of simplifying assumptions that can’t be right. It is a simplified model describing a complex world.
So in some sense, what you are saying is that all models are wrong because they are models . After all, they are simplified versions of reality. So I look at your work and I think you are studying connection between financial markets and macro economy. You are looking at characterising the models and then looking into data to see what we understand from data. Is that a fair representation and then maybe could you give us an example?
I think of myself somewhere between the intersection of three different areas; macro economics, finance and statistics. So it is interesting that I never took a course in Finance, though I have taught courses in finance. I learnt finance the same time I was writing papers with Scott Richard. I was able to draw on insights that I learnt from graduate school on economic dynamics but it allowed me and others to think about fi nance a little bit different than the way that was being thought and taught in finance classes. So it was almost an advantage that I was able to come at it from the outside. But it is this connection between macro economics, macro economics got me interested in asset price anyway, as well as how do we use statistical methods in an insightful way.
So, one of the things with your work is you hear the word generalised a lot. What it means is that there is a lot of maths in there because it is really general. But in your Nobel address you defined it very simply and very elegantly that this is really a method that allows you to say something, do something without having to do everything. That is what makes it general. Could you elaborate on that?
Suppose you want to look at interactions between financial markets in the macro economy, one kind of strategy would be to build a rather elaborate model of the macro economy to embed in that a bunch of various different market structures with national markets inside of it, try to build a resulting complex venture. And so, some statistical methods basically presume that you can actually built it right from the ground up and just do everything. You can build a fully specified model of the macro economy with all the financial markets inside of it and everything. And for a lot of purposes especially in the early stages of model development it is just asking for way too much to be really very insightful. So the aim here was to think of some of these linkages between the macro economy and financial markets without having to have a million specifications of macro economy with having very special equations connected to the role of financial markets and the like. Can we somehow do this in a way that doesn’t really compel us to do all that simultaneously? Can we avoid having to write down all the details of both the macro economy, what are the investors’ information sense, exactly what they observe, what they trade on and the like? And so part of why I think this kind of approach took off was the fact that it allowed one to study that linkage without having to really simultaneously have a full blown model of everything. Because if you have a full-blown model of everything then yes, that is the end goal. Once we think we understand all the linkages correctly then of course you want to build the full-fl edged model. But on the way as you try to think of these linkages it is very handy to have methods that allow you to think through those linkages without having to handle all the others. So I think that was the aim and part of the attractiveness of it.
Do you think governments should decide when markets are too volatile and clamp on, to do something?
It is a challenge for the policy makers in the sense that if they start setting prices, what prices do they set? Prices are allocative, they are indicators, I suppose we understand why prices are moving before we start jumping in and pretending that we can do a better job of setting up. So I think it is a very dangerous business for us to be squashing volatility for the sake of squashing it. Sometimes volatility is there for a good reason; for when information comes in, prices have to move to adjust to it. So you don’t want to get rid of the market signals and its impact on prices. I am a fi rm believer that decentralised markets, for all their flaws, are better off letting them, whenever possible, to do the work in terms of the determination of prices via forces of supply and demand.
If you look at medicine for example we have come a long way but economists seem to be goofing up making big mistakes all the time. And with all the fire-power that we have, why aren’t economists doing as well as medicine?
Maybe we don’t have enough fire power yet in economics. So one of the interesting things about getting a Nobel Prize in economics is that many other scientists think that there should not be a Nobel Prize in economics because it is not really a science. My response is that economics is a truly hard science, it is hard to do, and it is difficult to do. And the difficulty in doing it is that it is sometimes going to make progress very, very slow as compared to other disciplines. Medicine doesn’t have everything sorted out. Maybe it looks like we are making progress more quickly. But if we look at our country’s economic sets, economic systems over the last century there has been a fairly substantial amount of progress. But there is a long way to go, I agree. Our models are primitive, they are crude. We are trying to get better ones. We have many qualitative insights. The challenges to make it a quantitative discipline are apparently grounded. We have made some progress, and I hope that we continue to make more progress. After all policy makers including central banks, fiscal policy makers and the like, they need quantitative models. So if we are going to change the tax policy, we need some way to predict what the consequences are going to be before you do it. Now we don’t have the ability to run experiments of the whole economy and so models become very important. So, economics is hard, it is challenging to do it well, and I always welcome more horsepower to come into the field. But I also think that we should not underestimate how difficult it is to do economics well.
I think of myself somewhere between the intersection of three different areas; macro economics, finance and statistics. So it is interesting that I never took a course in Finance, though I have taught courses in finance.
So is Brexit idiosyncratic risk to Britain?
Brexit is one of those situations with which I am having trouble assigning probabilities to the outcome. We are going to imagine that Brexit actually has fairly benign consequences. Britain goes and renegotiates a bunch of trade agreements, preserving a lot of openness and free trade across not only European countries but elsewhere. Maybe it makes the Europeans rethink the whole European Union in ways that makes it more palatable, and in the long run more stable. Alternatively, what it does is that it leads Britain to become much more protectionist. Scotland decides to pull from the United Kingdom, northern Ireland decides to link up with Ireland – with this the United Kingdom falls apart as well as becoming more isolated. I could imagine a range of possible outcomes. So I certainly hope that it is the more benign consequence that emerges here. One could even imagine that the Europeans give some token concessions and the British engineer another vote, and they decide to stay in. The range of possibilities is just substantial. So this is a form of uncertainty. I am not sure I can call it a risk because I don’t quite know how to assign probabilities to all these different types of outcomes that are taking place. But it certainly is a very important source of uncertainty. And I think this is one that has a rather substantial drag over our world economy.
I would like to end with something that many of us worry about. We are looking at you as a role model. Many of us who are here have been very successful. That is why we are in this room. But many of us have children and grandchildren who are not doing quite so well in school, and I was remembering a story that you were quite a rebel in school yourself and yet you ended up getting a Nobel prize. So do we have hope? Tell us something, tell us what you were like when you were in high school.
So this is the case where I informed my son that he should not imitate his father. So it is true that when I was in high school at the age of 16 years my parents moved me from one part of the U.S. to another and it was a very dramatic move. They moved me from Michigan, a university town to a community in northern Utah which is actually a gorgeous town but it was a very hard move for me. And so at high school I started bringing home double check marks for not respecting authority. Now for academics this is not necessarily a bad attribute. But I also started bringing home some pretty erratic grades as well at the same time. One of the things I did to earn this is a bit of an interesting story. My English teacher hands out this homework assignment. Punctuations are a pain in the English language. This was a punctuation assignment. One strategy is to punctuate correctly, I decided a better strategy which was that I recognised the source of the paragraph, it came from a Pulitzer Prize winning author, it was the first paragraph from one of his books. So I literally go to the book and I copy the punctuation of this author and I get a C+ when it was handed back. I just very politely told the teacher that she has given a C+ to a Pulitzer Prize winning author and she did not like it very well. It turns out that she did not know the source of the paragraph. So that was what got me into some trouble at high school. I was very lucky that I had tolerant parents and I was very lucky that when I went to university, I went to a local university, that there were some faculty members who took some keen interest in me and figured out how to get me to accelerate to a top Ph.D. programme from a somewhat awkward starting point.
Lars, thank you very much for coming here and thank you Grace for coming to India because Lars would not have come without Grace. So thank you for your honest and insightful conversation and I hope that this is your first but only the beginning of many visits to India and ISB.