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2007), “Learning and shifts in long-run productivity growth

By Rochelle M. Edge, Thomas Laubach, John C. Williams, Spencer Krane, Stephanie Schmitt-grohe, Michael Woodford and Raf Wouters


for comments on earlier versions of this paper. We also thank Kirk Moore for excellent research assistance and Judith Goff for editorial assistance. The views expressed herein are those of the authors and do not necessarily reflect those of the Board of Governors of the Federal Reserve System or their staff, the Shifts in the long-run rate of productivity growth—such as those experienced by the U.S. economy in the 1970s and 1990s—are difficult, in real time, to distinguish from transitory fluctuations. In this paper, we analyze the evolution of forecasts of longrun productivity growth during the 1970s and 1990s and examine in the context of a dynamic general equilibrium model the consequences of gradual real-time learning on the responses to shifts in the long-run productivity growth rate. We find that a simple updating rule based on an estimated Kalman filter model using real-time data describes economists ’ long-run productivity growth forecasts during these periods extremely well. We then show that incorporating this process of learning has profound implications for the effects of shifts in trend productivity growth and can dramatically improve the model’s ability to generate responses that resemble historical experience

Year: 2014
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