The recent financial crisis and the Great Recession of 2007–2009 posed new challenges to the current macroeconomic models designed to provide guidance for monetary policies. On the one hand, central banks have actively taken unconventional monetary measures, and the financial sector becomes a crucial channel for those monetary transmissions. On the other hand, common macro models lack of analytical specificity to account for important financial sector influences on the aggregate economy.
Policymakers and researchers are demanding a new generation of enhanced models and advanced empirical and quantitative methodologies to better study the impact of shocks that are initially large or build endogenously over time through the financial sector.
In this session, Winston Wei Dou of MIT provided a critical survey on the macro models for monetary policy from a finance perspective. Dou and coauthor Andrew Lo first review the history of the monetary policy modeling. Then, they set forth a simple canonical framework that incorporates the key up-to-date theoretical advances. This surfaces the challenges for the existing models and quantitative methodologies. Finally, they review the current core monetary models employed by the major central banks.
The authors conducted a cross-sectional comparison among the models currently popular across major central banks. The comparison focuses on three classes of models: Large-scale macroeconometric (LSM) models, structural vector autoregressive (SVAR) models, and dynamic stochastic general equilibrium (DSGE) models.
While SVAR models can be viewed as linear econometric approximations of the fully-specified DSGE models, the LSM models have several important disadvantages relative to the DSGE models. First, LSM models depend on ad-hoc short-run adjustment dynamics. As discussant Lars Peter Hansen pointed out, the short-run dynamics in DSGE models are also “ad hoc” to some extent, even though the individual optimization is explicitly specified there ( i.e. “micro-founded”). But the explicit modeling of individual optimization is useful because it allows the linkage between data and deep structural parameters.
Second, LSM models are more subject to the Lucas Critique and the Sims Critique, while at the same time they have similar flexibility, extensibility and capacity of estimation and prediction compared to DSGE models.
Given the advantages of DSGE models, the authors study a canonical New Keynesian DSGE model. Using this model as a concrete example, the authors demonstrate the limitations of current DSGE models and provide insights into what the models lack could be important.
Their proposed model starts with a traditional New Keynesian DSGE framework with several typical components: monopolistic competitive firms, Cobb-Douglas production, Calvo nominal rigidity in prices and wages, capital accumulation, and external habit formation in consumption. This follows the seminal works by Christiano, Eichenbaum, and Evans (2005) and Smets and Wouters (2003, 2007).
Then the authors incorporate some new components, include a stylized financial sector, time-varying disaster risks, and a government credit policy. This model shows the importance of the imperfect financial sector in generating the nonlinearity and skewness in risk premia and, in turn, causing dramatic consequences in the real economy. Log-linearization suppresses those key features of nonlinearity and skewness in quantitative analysis of the model.
Key ingredients missing from this model include: 1) government balance sheet and active fiscal policies; 2) heterogeneity and reallocation; 3) macroeconomic impact of sizable and nonlinear risk premium dynamics; 4) time-varying uncertainty; 5) financial sector and systemic risks; 6) imperfect product market and markups dynamics; and 7) solution methods for dynamic quantitative structural models which allow for nonlinearity and skewness.
The discussants, Lars Peter Hansen and Harald Uhlig, offered several insightful comments for further improvement. First, they pointed out that it is valuable to distinguish the price channel and the exposure channel of asset pricing in the model. Second, it is important to further clarify or demonstrate why the financial sector is relevant for monetary policy analysis when confronting financial market fluctuations, particularly for the unconventional credit policies.
Discussants also noted that it would be useful to show in the model that the connection between the key financial market phenomena and real quantities is not appropriately accounted for. Finally, they called for a serious understanding of the sources of uncertainty shocks and more specific discussion of the redistribution channel.