How Good Is What You’ve Got? DGSE-VAR as a Toolkit for Evaluating DSGE Models

E C O N O M I C R E V I E W Second Quarter 2006 Dynamic stochastic general equilibrium (DSGE) models are becoming increasingly popular in central banking circles. The number of central bank–sponsored conferences on DSGE modeling and the amount of staff resources devoted to DSGE model development and estimation have risen dramatically over the past five years. This trend has affected monetary policy authorities around the globe, including the Federal Reserve System, the Bank of Canada, the European Central Bank, the Sveriges Riksbank, and the Reserve Bank of New Zealand. While few central banks are currently using DSGE models to generate forecasts and policy scenarios that provide the basis for interest rate decisions, many are contemplating doing so in the near future. Part of the recent popularity of DSGE models is due to work by Smets and Wouters (2003), who document that a modified version of a New Keynesian model developed by Christiano, Eichenbaum, and Evans (2005) is able to track and forecast euro area time series as well as, if not better than, a vector autoregression (VAR) estimated with Bayesian techniques. While the empirical finding needs to be qualified, the results have had a considerable impact on how policymakers view DSGE models and have triggered efforts at many central banks to develop their own estimated DSGE model. In the 1990s the prevailing view among some policymakers was that DSGE models provide “good theory” to sharpen the understanding of business cycle fluctuations and to address fundamental policy questions: How is the stabilization of output and inflation through monetary policy actions related to the maximization of aggregate welfare? Should the central bank react to asset market fluctuations? How should monetary policy be conducted if the nominal interest rate is close to its zero lower bound? Should central banks of small open economies respond to exchange rate movements? Despite these benefits, many policymakers were skeptical that DSGE models could be used for quantitative data analysis, especially shortand medium-term forecasting and the projection of macroeconomic aggregates under alternative interest rate scenarios. How Good Is What You’ve Got? DGSE-VAR as a Toolkit for Evaluating DSGE Models