Measuring Uncertainty About Long-Run Prediction

64 Pages Posted: 9 Mar 2013 Last revised: 23 Jan 2022

See all articles by Ulrich K. Müller

Ulrich K. Müller

Princeton University - Department of Economics

Mark W. Watson

Princeton University - Princeton School of Public and International Affairs; National Bureau of Economic Research (NBER)

Date Written: March 2013

Abstract

Long-run forecasts of economic variables play an important role in policy, planning, and portfolio decisions. We consider long-horizon forecasts of average growth of a scalar variable, assuming that first differences are second-order stationary. The main contribution is the construction of predictive sets with asymptotic coverage over a wide range of data generating processes, allowing for stochastically trending mean growth, slow mean reversion and other types of long-run dependencies. We illustrate the method by computing predictive sets for 10 to 75 year average growth rates of U.S. real per-capita GDP, consumption, productivity, price level, stock prices and population.

Suggested Citation

Müller, Ulrich K. and Watson, Mark W., Measuring Uncertainty About Long-Run Prediction (March 2013). NBER Working Paper No. w18870, Available at SSRN: https://ssrn.com/abstract=2230760

Ulrich K. Müller (Contact Author)

Princeton University - Department of Economics ( email )

Princeton, NJ 08544-1021
United States
609-258-3216 (Phone)
609-258-4026 (Fax)

HOME PAGE: http://www.princeton.edu/~umueller

Mark W. Watson

Princeton University - Princeton School of Public and International Affairs ( email )

Princeton University
Princeton, NJ 08544-1021
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

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