Learning About the Long Run

58 Pages Posted: 23 Nov 2021 Last revised: 1 Jan 2022

See all articles by Leland Farmer

Leland Farmer

University of Virginia

Emi Nakamura

Columbia Business School - Finance and Economics; National Bureau of Economic Research (NBER)

Jón Steinsson

Columbia University

Date Written: November 2021

Abstract

Forecasts of professional forecasters are anomalous: they are biased, forecast errors are autocorrelated, and forecast revisions predict forecast errors. Sticky or noisy information models seem like unlikely explanations for these anomalies: professional forecasters pay attention constantly and have precise knowledge of the data in question. We propose that these anomalies arise because professional forecasters don’t know the model that generates the data. We show that Bayesian agents learning about hard-to-learn features of the data generating process (low frequency behavior) can generate all the prominent aggregate anomalies emphasized in the literature. We show this for two applications: professional forecasts of nominal interest rates for the sample period 1980-2019 and CBO forecasts of GDP growth for the sample period 1976-2019. Our learning model for interest rates also provides an explanation for deviations from the expectations hypothesis of the term structure that does not rely on time-variation in risk premia.

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Suggested Citation

Farmer, Leland and Nakamura, Emi and Steinsson, Jón, Learning About the Long Run (November 2021). NBER Working Paper No. w29495, Available at SSRN: https://ssrn.com/abstract=3968718

Leland Farmer (Contact Author)

University of Virginia ( email )

237 Monroe Hall
P.O. Box 400182
Charlottesville, VA 22904-418
United States

Emi Nakamura

Columbia Business School - Finance and Economics ( email )

3022 Broadway
New York, NY 10027
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Jón Steinsson

Columbia University ( email )

3022 Broadway
New York, NY 10027
United States

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