Keywords: DGE models, Kalman-filter, Real-time data, Learning, Productivity growth.
Abstract: 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
long-run 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.
If immediately recognized, an increase in the long-run growth rate causes long-term
interest rates to rise and produces a sharp decline in employment and investment,
contrary to the experiences of the 1970s and 1990s. In contrast, with learning, a rise
in the long-run rate of productivity growth sets off a sustained boom in employment and
investment, with long-term interest rates rising only gradually. We find the
characterization of learning to be crucial regardless of whether shifts in long-run
productivity growth owe to movements in TFP growth concentrated in the investment goods sector
or economy-wide TFP.
Full paper (204 KB PDF)
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Last update: April 22, 2004