This is a prize given to time series econometrics and how to deal with imperfect data and changing variances for variables being estimated. Can you say “Generalized Method of Moments” (GMM)? Hansen teaches at the economics department of the University of Chicago.
For years now journalists have asked me if Hansen might win, and if so, how they might explain his work to the general reading public. Good luck with that one.
Unlike maximum likelihood estimation (MLE), GMM does not require complete knowledge of the distribution of the data. Only specified moments derived from an underlying model are needed for GMM estimation. In some cases in which the distribution of the data is known, MLE can be computationally very burdensome whereas GMM can be computationally very easy. The log-normal stochastic volatility model is one example. In models for which there are more moment conditions than model parameters, GMM estimation provides a straightforward way to test the specification of the proposed model. This is an important feature that is unique to GMM estimation.
If you read this piece with Hodrick, you will see that Hansen’s work is instrumental for testing the advanced versions of the propositions of Fama and Shiller. In this critical sense, the three prizes are quite tightly unified. And see this paper too with Singleton. Here’s the most concrete sentence you are going to squeeze out of me on this one: if you want to do serious analysis of whether changing risk premia can help rationalize observed asset price movements, Hansen’s contributions will prove essential.