On DSGE Models

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Christiano L. J., Eichenbaum M. S., Trabandt M.(2018). On DSGE Models[J]. Journal of Economic Perspectives,32(3):113-140.

Abstract: The outcome of any important macroeconomic policy change is the net effect of forces operating on different parts of the economy. A central challenge facing policymakers is how to assess the relative strength of those forces. Economists have a range of tools that can be used to make such assessments. Dynamic stochastic general equilibrium (DSGE) models are the leading tool for making such assessments in an open and transparent manner. To be concrete, suppose we are interested in understanding the effects of a systematic change in policy, like switching from inflation targeting to price-level targeting. The most compelling strategy would be to do randomized control trials on actual economies, but that course of action is not available to us. So what are the alternatives? It is certainly useful to study historical episodes in which such a similar policy switch occurred or to use reduced-form time series methods, but these approaches also have obvious limitations. In the historical approach, the fact that no two episodes are exactly the same always raises questions about the relevance of a past episode for the current situation. In the case of reduced-form methods, it is not always clear which parameters should be changed and which should be kept constant across policy options. Inevitably, assessing the effects of a systematic policy change has to involve the use of a model.

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这篇文章的总结比较精彩，大家共赏： On DSGE Models，The enterprise of DSGE modeling is an organic process that involves the constant interaction of data and theory. Pre-crisis DSGE models had shortcomings that were highlighted by the financial crisis and its aftermath. Substantial progress has occurred since then. We have emphasized the incoporation of financial frictions and heterogeneity into DSGE models. However, we should also mention that other exciting work is being done in this area, like research on deviations from conventional rational expectations. These deviations include k-level thinking, robust control, social learning, adaptive learning, and relaxing the assumption of common knowledge. Frankly, we do not know which of these competing approaches will play a prominent role in the next generation of mainstream DSGE models.
Will the future generation of DSGE models predict the  time and nature of the next crisis? Frankly, we doubt it. As far as we know, there is no sure, time-tested way of foreseeing the future. The proximate cause for the financial crisis was a failure across the economics profession, policymakers, regulators, and financial market professionals to recognize and to react appropriately to the growing size and leverage of the shadow-banking sector. DSGE models are evolving in response to that failure as well as to the treasure trove of micro data available to economists. We don't know where that process will lead. But we do know that DSGE models will remain central to how macroeconomists think about aggregate phenomena and policy. There is simply no crediable alternative to policy analysis in a world of competing economic forces operating on different parts of the economy.

ahnulxy 发表于 2018-8-3 20:20
Christiano L. J., Eichenbaum M. S., Trabandt M.(2018). On DSGE Models[J]. Journal of Economic Perspe ...

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