Hansen’s work is the most technical and most difficult to explain to a layperson. The brief version is that in 1982 Hansen developed the Generalized Method of Moments a new and elegant way to estimate many economic models that requires fewer assumptions and is often more powerful than other methods.

Here is the basic idea in a nutshell. The method of moments is an old and intuitive technique for estimating the parameters of a data generating process. A moment is an expectation of the form E(X^r), where r can be an integer. For example if r=1 then the first moment, E(X), is just the mean (you may also know that together the first and second moments, E(X^2), define the variance). If the true mean in the data generating process is M then we can write a moment condition, E(X)-M=0. Now the method of moments says to estimate M we should solve that condition by replacing, E(X), with the sample mean. In other words, a good estimator for the unknown population mean is the sample mean, i.e. the mean in the data that you have. Pretty obvious so far.

Now let’s start to generalize. First, there are many moments other than the mean and variance. Indeed economic theory often provides moment conditions that may be written E(f(X,M))=0 where M now stands for a moment not necessarily the mean and f can be a non-linear function. For example, rational expectation models often provide conditions that E(f(X))-M=0, i.e. that forecasts should equal true values, or macro models imply that various differences, such as in consumption levels should not be correlated and so forth. Indeed, finance and macroeconomic theory provided a surplus of moment conditions and many of these different conditions imply something about the same parameter. Now, and this is key, when we have more moment conditions than parameters we can’t choose the parameters to make all the moment conditions true, i.e. we can’t make all those moment conditions equal to zero. So what to do?

What Hansen did with the generalized method of moments is show that when we have more moment conditions than parameters we can best estimate those parameters by giving more weight to the conditions that we have better information about. In other words, if we have two conditions and we can’t force both of them to zero by a choice of parameter then choose the parameter such that the moment condition we know the most about (least variance) is closer to zero than the one we know less about. Again, the idea is intuitive, but Hansen showed how to make these choices and then he proved that when the parameters are chosen in this way they have good statistical properties such as consistency (they get closer to the true values as the sample size increases). Importantly, estimating a model using these moment conditions does not require untenable assumptions on the entire distribution of returns. Hansen then also showed, such as with Hansen and Hodrick (1980) and Hansen and Singleton (1982, 1983) how these methods could be applied to a large class of macro models and finance models including asset pricing, the latter of which links Hansen with the work of Fama and Shiller as does the important bound discovered by Hansen and Jaganatthan (1991).

Robert Shiller is best known for warning about the internet stock market bubble and later the housing bubble. What is most impressive to me, however, is that most people who think that markets can be inefficient are anti-market. Shiller’s solution to market problems, however, is more markets! The housing market, for example, has traditionally had two problems. Since each house is unique there has been no market index of housing prices so that people couldn’t easily see bubbles and if they could see them on the ground there was no easy way to short the market (to try to profit from the bubble in a way that would moderate the bubble). Moreover, because there haven’t been good housing indexes a very large amount of each average person’s wealth has been tied up with an asset that can fluctuate substantially in price. Most house buyers, in other words, are putting all their eggs in one basket and crossing their fingers that the basket doesn’t go bust. In recent years, that has been a very unfortunate bet.

Shiller’s solution to the problems in the housing market has been to make the market better—he created with Case and Weiss–the Case-Shiller Index. For the first time, it’s possible to see in real time housing prices and compare with averages over time and it possible to buy options and futures on the index which will help for forecasting. Moreover, it’s possible that in the future insurance products can be built based on local versions of the index–thus you could insure yourself against big declines in the price of housing in your neighborhood.

Shiller’s housing index is also a window into how macro markets could also be used to create livelihood insurance, a type of private unemployment insurance. Moral hazard and adverse selection make it difficult to protect any single individual from unemployment but indexes in the unemployment rate of dentists or construction workers could be used to provide some insurance for workers in these fields when conditions in their entire industry are poor.

I featured one of Shiller’s biggest ideas in Entrepreneurial Economics, markets in GDP. A GDP market would allow shares of GDP to be bought and sold, add to this Hansonian prediction markets and you are long way towards an ideal way to evaluate the effect of major policies. Moreover, a GDP market would allow the creation of many insurance products. We are all less diversified than is ideal. It would be optimal to trade some shares in US GDP for shares in World GDP which is more stable. We can do this if we create Shiller GDP markets throughout the world.

Shiller’s book Macro Markets is truly visionary and I hope the Nobel brings a lot of attention to these ideas.

As an undergraduate Fama worked for a stock forecasting service and he was tasked with coming up with rules to make money in the market. Time and time again he would find profitable rules only to find that they didn’t work in new data or out of sample. In graduate school he started talking to Merton Miller, Lester Telser, and Benoît Mandelbrot and finally hit on the idea that in an efficient market price changes would not be forecastable. The rest is history.

Fama’s dissertation and famous 1970 review article, Efficient Capital Markets: A Review of Theory and Empirical Work made efficient markets a touchstone for modern economists and finance theorists but practitioners hated and still hate the idea. Nevertheless, test after test showed that very few mutual fund managers beat the market and those that beat the market this year are not more likely to beat the market the next year. Chance and perhaps a few, very rare, geniuses explain the data. Eventually, hundreds of billions of dollars began to flow into index funds and today index funds manage over $7 trillion dollars worth of assets worldwide, making Fama the 7 trillion dollar man. Fama’s ideas have made an enormous contribution to how people invest, saving them billions in fees which generated beautiful homes for fortunate mutual fund managers but less than nothing for their customers.

The no free lunch principle is the most robust of the findings of the early Fama/efficient markets school. Other early findings such as non-forecastability of returns have been revised. The initial finding was that returns were not forecastable and that is true for short durations but it is now clear that returns can be forecastable over longer horizons! In particular, variables such as the dividend/price ratio can predict stock return variation years in advance! (Robert Shiller pioneered many of these kinds of studies as did Campbell and Cochrane).  Fama, however, contrary to how he is sometimes represented did not reject these findings. Indeed, the less well known part of the story is that Fama working with French (e.g. Fama and French (1988a,1988b, 1993) has been among the pioneers in documenting and explaining these findings. What Fama’s later work has shown is that many of the anomalies such as time varying returns and the higher return to so-called value firms are real but they are not anomalies they are better explained as variations in risk premia tied to changes in the business cycle.

The CAPM (for which Markowtiz and Sharpe won the Nobel) suggested that the only source of true (priced) risk was risk that varied with the market return. That is one important source of risk but it’s not the only one, other types of macro risk which appear to vary with the business cycle are also priced and they are correlated with markers like the dividend/price ratio and the prospects for value and small-cap stocks. Thus, Fama showed that many of the seemingly anomalies (not all, however!) of the early efficient market tests can be better explained by a market model that incorporates more sources of risk. All of this work has really been a tour de force. It’s not often that the same person creates the theory and then participates in the first revolution overturning (some of) that theory.

Fama also pioneered the first event study! Fama, Fisher, Jensen, and Roll (1969) studied something a bit prosaic, stock splits, but the methodology, looking at how the stock market reacts to unexpected events, has seen been used to study what happens when Senators die unexpectedly (firms they support fall), what happens in close elections, which part was responsible for the Challenger space shuttle crash and many other events.