is is my third piece on dynamic stochastic general equilibrium models (DSGEs). The first, a PIIE Policy Brief, was triggered by a project, led by David Vines, to assess how DSGEs had performed during the financial crisis (namely, badly) and how they could be improved. That brief went nearly viral (by the standards of blogs on DSGEs ☺). The many comments I received led me to write a second piece, a PIIE RealTime blog, which again led to a further round of reactions, prompting me to write this blog (which I hope and fully expect will be the last on this topic). In this blog I want to make one main point:
Different classes of macro models are needed for different tasks.
Let me focus on two main classes.
Theory models, aimed at clarifying theoretical issues within a general equilibrium setting. Models in this class should build on a core analytical frame and have a tight theoretical structure. They should be used to think, for example, about the effects of higher required capital ratios for banks, or the effects of public debt management, or the effects of particular forms of unconventional monetary policy. The core frame should be one that is widely accepted as a starting point and that can accommodate additional distortions. In short, it should facilitate the debate among macro theorists.
Policy models, aimed at analyzing actual macroeconomic policy issues. Models in this class should fit the main characteristics of the data, including dynamics, and allow for policy analysis and counterfactuals. They should be used to think, for example, about the quantitative effects of a slowdown in China on the United States, or the effects of a US fiscal expansion on emerging markets.
It would be nice if a model did both, namely have a tight, elegant, theoretical structure and fit the data well. But this is a pipe dream. Perhaps one of the main lessons of empirical work (at least in macro, and in my experience) is how messy the evidence typically is, how difficult aggregate dynamics are to rationalize, and how unstable many relations are over time. This may not be too surprising. We know, for example, that aggregation can make aggregate relations bear little resemblance to underlying individual behavior.