THE Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel has been awarded to Thomas J. Sargent of New York University and Christopher A. Sims of Princeton University. Predictably, this blog proved useless at predicting the winner. The Academy of Sciences provides useful backgrounders on the prize, technical and non-technical. These interviews are also good on Mr Sargent as is this one on Mr Sims.
Both laureates spent the main portion of their careers together at the University of Minnesota—one of the bastions of the "freshwater" school of macroeconomics—in the 1970s and 1980s (though they only once co-authored a paper together). The prize is awarded "for their empirical research on cause and effect in the macroeconomy". It's difficult to imagine what might be more important.
Robert Lucas, a previous Nobel laureate, showed how incorporating expectations into macroeconomic models muddled the framework economists prior to the "rational expectations revolution" thought they saw so clearly. As (in an excellent summary) Alex Tabarrok puts it, their work separately grappled with different ways of addressing the Lucas Critique of 1976:
“Lucas looked at the large econometric models of the 1970s, models that contained hundreds of variables relating economic aggregates like income, consumption, unemployment and so forth. Lucas then asked whether these models could be used to predict the impact of new policies. One could certainly take the regression coefficients from these models and forecast but Lucas argued that such a method was invalid because the regression coefficients themselves would change with new policies.”
“If you wanted to understand the effects of a new policy you had to go deeper, you had to model the decision rules of individuals based on deep, invariant or 'structural' factors, factors such as how people value labor and leisure, that would not change as policy changed and you had to include in your macro model another deep factor, expectations.”
The 2011 laureates' work focuses on the implications of rational expectations for empirical and econometric work and on ways to uncover these structural parameters. Mr Sargent's enormous contributions to rational expectations econometrics were purely methodological and his methods are explicated in his three graduate textbooks: Macroeconomic Theory, Dynamic Macroeconomic Theory and Recursive Macroeconomic Theory, bibles of modern macroeconomics.
However, Mr Sargent's association with the rational expectations revolution and its extension to the efficient markets hypothesis, much demonised during the crisis, is somewhat misunderstood. Much of his work has focused on agents learning within models and less-than-fully-rational expectations. Much of the criticism of rational expectations is integrated into this work. Learning involves two elements: firstly that agents (consumers or businesses) have incomplete knowledge of some parameters in the model, and secondly a specification of how agents learn about these parameters, based on the observations of evolving time series.
Chris Sims' work upset the existing paradigm by arguing that the existing models for looking at causal relationships in macroeconomics relied on "incredible" identification assumptions. This meant that interpretations of "what causes what" were necessarily flawed. There is a nice explanation of the problem that Mr Sims' work addresses in the technical backgrounder from the Nobel Committee.
“To appreciate the problem of identification, suppose we consider the coffee market and try to explain movements in the quantity and price of coffee. A traditional approach is to isolate a variable that is believed to solely influence either supply or demand. One such variable is weather. Bad weather may reduce the amount of coffee produced at all prices, i.e., it shifts the supply curve inward. If the demand curve for coffee is not affected, a change in the weather will lower the equilibrium quantity of coffee and raise its price. Variations in weather therefore allow us to trace out - to identify - the shape of the demand curve. However, is the assumption that weather does not influence the demand curve plausible? Even if people's taste for coffee does not depend directly on the weather, as Sims pointed out coffee buyers know that weather is variable and may stock up when adverse weather variations arise. Thus, expectations about weather (and other varying determinants of supply and/or demand) are likely to affect both supply and demand, in such a way that weather changes may not have the expected consequences.”
He proposed a statistical tool, the vector autoregression (VAR), as a solution to this problem. As with Mr Sargent's work, VARs are now central to macroeconomics. This Nobel prize honours the ubiquity of their innovations.