Dynamic Stochastic General-equilibrium Models of Fluctuations

Our analysis of macroeconomic fluctuations in the previous two chapters has developed two very incomplete pieces. In Chapter 5, we considered a full intertemporal macroeconomic model built from microeconomic foundations with explicit assumptions about the behavior of the underlying shocks. The model generated quantitative predictions about fluctuations, and is therefore an example of a quantitative dynamic stochastic generalequilibrium, or DSGE, model. The problem is that, as we saw in Section 5.10, the model appears to be an empirical failure. For example, it implies that monetary disturbances do not have real effects; it rests on large aggregate technology shocks for which there is little evidence; and its predictions about the effects of technology shocks and about business-cycle dynamics appear to be far from what we observe. To address the real effects of monetary shocks, Chapter 6 introduced nominal rigidity. It established that barriers to price adjustment and other nominal frictions can cause monetary changes to have real effects, analyzed some of the determinants of the magnitude of those effects, and showed how nominal rigidity has important implications for the impacts of other disturbances. But it did so at the cost of abandoning most of the richness of the model of Chapter 5. Its models are largely static models with one-time shocks; and to the extent their focus is on quantitative predictions at all, it is only on addressing broad questions, notably whether plausibly small barriers to price adjustment can lead to plausibly large effects of monetary disturbances. Researchers’ ultimate goal is to build a model of fluctuations that combines the strengths of the models of the previous two chapters. This chapter will not take us all the way to that goal, however. There are two reasons. First, there is no consensus about the ingredients that are critical to include in such a model. Second, the state-of-the-art models in this effort (for example, Erceg, Henderson, and Levin, 2000, Smets and Wouters, 2003, and Christiano, Eichenbaum, and Evans, 2005) are quite complicated. If there