DSGE Models and Their Use in Monetary Policy

Article excerpt

The past 10 years or so have seen the development of a new class of models that are proving useful for monetary policy: dynamic stochastic general equilibrium (DSGE) models. Many central banks around the world, including the Swedish central bank, the European Central Bank, the Norwegian central hank, and the Federal Reserve, use these models in formulating monetary policy. In this article, Mike Dotsey discusses the major features of DSGE models and why these models are useful to monetary policymakers. He outlines the general way in which they are used in conjunction with other tools commonly employed by monetary policymakers and points out the promise of using these models as well as the pitfalls.

The past 10 years or so have witnessed the development of a new class of models that are proving useful for monetary policy: dynamic stochastic general equilibrium (DSGE) models. The pioneering central bank, in terms of using these models in the formulation of monetary policy, is the Sveriges Riksbank, the central bank of Sweden. (1) Following in the Riksbank's foot-steps, a number of other central banks have incorporated DSGE models into the monetary policy process, among them the European Central Bank, the Norge Bank (Norwegian central bank), and the Federal Reserve. (2)

This article will discuss the major features of DSGE models and why these models are useful to monetary policymakers. It will indicate the general way in which they are used in conjunction with other tools commonly employed by monetary policymakers. These other tools include purely statistical models, often not tied to any particular economic theory, but instead are solely based on historical regularities found in the data. Such tools also include large macroeconomic models that contain many sectors of the economy but generally do not place many theoretical restrictions on the interrelationships between the various economic sectors. Other tools include economic surveys of consumers, firms, or forecasters, as well as policymakers' own expertise.

These other tools provide valuable insights into the state of the economy that complement the insights derived from explicit theoretical models, which account for important interactions between sectors of the economy. Together, the various modeling approaches comprise the toolkit that policymakers commonly rely on. This article will concentrate on DSGE models, which share the strengths of many theoretically grounded models but are designed with the intention of providing forecasts and identifying the key drivers of current economic activity. In doing so, I will point out the promise of this modeling strategy as well as its pitfalls.

Economic models, in general, provide valuable guidance when formulating monetary policy. Because the economy is so complex and key economic components are intertwined, it is necessary to develop frameworks that capture these interrelationships. In order to capture, say, the effect that an increase in productivity has on consumption, we must have a model that incorporates the behavior of many variables, such as income, investment, labor supply, and consumption, if we are to understand this effect. Simply looking at one equation that attempts to only model consumption is likely to produce an incomplete and misleading interpretation. Thus, a model that integrates many economic components is necessary for understanding and predicting economic behavior.

However, because all models are approximations of actual economic behavior, it is often useful to combine the insights from a number of models along with statistical forecasts and the individual experience of policymakers. That is generally what many central banks do, and DSGE models are increasingly becoming a part of policymakers' toolkits.

AN OVERVIEW OF DSGE MODELS

DSGE models are small to medium size economic models that incorporate the major sectors of the economy into a coherent and interrelated whole. …