# Nobel for Sargent and Sims

The Nobel in Economics goes to Thomas Sargent and Christopher Sims, for empirical macroeconomics.

Let’s go back to 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 Nobel for Christopher Sims and Thomas Sargent is for work each did in their quite different ways to develop ideas and techniques to address the Lucas Critique. Sargent’s (1973, 1976) early work showed how models incorporating rational expectations could be tested empirically. In many of these early models, Sargent showed that including rational expectations in a model could lead to invariance results, nominal shocks caused by changes in the money supply, for example, wouldn’t matter.

Sargent’s name thus became connected with rational expectations and new-classical invariance results. Sargent himself, however, has long moved past rational expectations models towards models that incorporate learning. What will people do when they don’t know the true model of the economy? How will they update their model of the economy based on observations? In these learning models the goal is to look for a self-confirming equilibrium. The interesting thing about a self-confirming equilibrium is that people’s expectations and learning can converge on a false model of the economy! Sargent has thus evolved in a very different direction than one might have imagined in 1976.

Sargent is also a very good economic historian, having written important pieces on monetary history (and also here on America) that combine history with theory.

Sims was also unsatisfied with the standard econometric models of the 1970s. In response, he developed vector auto regressions. In its simplest form a VAR is just a regression of a variable on its past values and the past values of other related variables. It’s easy to run a VAR on unemployment, inflation and output, for example. Such a VAR doesn’t tell you much about structural parameters but surprisingly even very simple VARs have quite good forecasting ability relative to the macro models of the 1970s, this was another reason why those models declined in importance.

Sims, however, took the models a step further by showing that you could identify fundamental shocks in these models by making assumptions about the dynamics or ordering of the shocks. Interest rates respond to government spending, for example, before government spending responds to interest rates. Note that these ordering assumptions tend to be quite neutral with respect to different economic models so VARs could be used to test different theories and could also be used by practioniers of many different stripes. Thus VAR models caught on very quickly and have come to dominate macro-economic modelling.

VAR models can also be identified in different ways, instead of identifying based on ordering, for example, one can identify based on what economic theory predicts about long-run relationships. For example, a monetary shock should affect the price level but not the output level in the long-run. More generally, modern macro models are dynamic models–they make predictions about how variables evolve over time–so relating a VAR to a model thus creating a structural or identified VAR has been the natural way to examine the data and to test modern models.

With identification in hand one can then use these models to plot impulse response functions. How does a shock to oil prices work its way through the economy? When does GDP begin to fall and by how much? How long does it take the economy to recover? What about a shock to monetary policy? Sims (1992), for example, looks at monetary shocks in five modern economies. Understanding these dynamics has played an important role in recent debates over the importance of money, government spending and real shocks.