Real Business Cycle Models: Past, Present, and Future∗
Sergio Rebelo† March 2005
Abstract In this paper I review the contribution of real business cycles models to our understanding of economic ﬂuctuations, and discuss open issues in business cycle research.
I thank Martin Eichenbaum, Nir Jaimovich, Bob King, and Per Krusell for their comments, Lyndon Moore and Yuliya Meshcheryakova for research assistance, and the National Science Foundation for ﬁnancial support. † Northwestern University, NBER, and CEPR.
Finn Kydland and Edward Prescott introduced not one, but three, revolutionary ideas in their 1982 paper, “Time to Build and Aggregate Fluctuations.” The ﬁrst idea, which builds on prior work by Lucas and Prescott (1971), is that business cycles can be studied using dynamic general equilibrium models. These models feature atomistic agents who operate in competitive markets and form rational expectations about the future. The second idea is that it is possible to unify business cycle and growth theory by insisting that business cycle models must be consistent with the empirical regularities of long-run growth. The third idea is that we can go way beyond the qualitative comparison of model properties with stylized facts that dominated theoretical work on macroeconomics until 1982. We can calibrate models with parameters drawn, to the extent possible, from microeconomic studies and long-run properties of the economy, and we can use these calibrated models to generate artiﬁcial data that we can compare with actual data. It is not surprising that a paper with so many new ideas has shaped the macroeconomics research agenda of the last two decades. The wave of models that ﬁrst followed Kydland and Prescott’s (1982) work were referred to as “real business cycle” models because of their emphasis on the role of real shocks, particularly technology shocks, in driving business ﬂuctuations. But real business cyle (RBC) models also became a point of departure for many theories in which technology shocks do not play a central role. In addition, RBC-based models came to be widely used as laboratories for policy analysis in general and for the study of optimal ﬁscal and monetary policy in particular.1 These policy applications reﬂected the fact that RBC models See Chari and Kehoe (1999) for a review of the literature on optimal ﬁscal and monetary policy in RBC models. 1
represented an important step in meeting the challenge laid out by Robert Lucas (Lucas (1980)) when he wrote that “One of the functions of theoretical economics is to provide fully articulated, artiﬁcial economic systems that can serve as laboratories in which policies that would be prohibitively expensive to experiment with in actual economies can be tested out at much lower cost. [...] Our task as I see it [...] is to write a FORTRAN program that will accept speciﬁc economic policy rules as ‘input’ and will generate as ‘output’ statistics describing the operating characteristics of time series we care about, which are predicted to result from these policies.” In the next section I brieﬂy review the properties of RBC models. It would have been easy to extend this review into a full-blown survey of the literature. But I resist this temptation for two reasons. First, King and Rebelo (1999) already contains a discussion of the RBC literature. Second, and more important, the best way to celebrate RBC models is not to revel in their past, but to consider their future. So I devote section III to some of the challenges that face the theory ediﬁce that has built up on the foundations laid by Kydland and Prescott in 1982. Section IV concludes.
2. Real Business Cycles
Kydland and Prescott (1982) judge their model by its ability to replicate the main statistical features of U.S. business cycles. These features are summarized in Hodrick and Prescott (1980) and are revisited in Kydland and Prescott (1990). Hodrick and Prescott detrend U.S....
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