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Learning and Shifts in Long-Run Growth

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  • Rochelle Edge
  • Thomas Laubach

Abstract

Shifts in the long-run rate of productivity growth--such as those 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 explore how economists' projections of trend productivity growth gradually evolved during the 1970s and 1990s, and examine the consequences of such real-time learning on the dynamic responses to shifts in trend growth in the context of a dynamic stochastic general equilibrium model. We find that a simple updating rule based on an estimated Kalman filter model using real-time data describes the evolution of economists' long-run growth expectations extremely well. We then show that incorporating learning in this fashion has profound implications for the dynamic effects of shifts in trend productivity growth, whether they are concentrated in the investment-goods sector or affect the entire economy. If immediately recognized, increases in the trend growth rate cause long-term interest rates to rise and produce a sharp decline in employment and investment. In contrast, with learning, a productivity acceleration sets off a sustained boom in employment and investment, and long-term interest rates rise only gradually in a pattern consistent with the experience of the 1990s

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Bibliographic Info

Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2004 with number 123.

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Date of creation: 11 Aug 2004
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Handle: RePEc:sce:scecf4:123

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Keywords: Kalman filter; real-time data; signal extraction;

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