Measuring Uncertainty about Long-Run Prediction
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.
We thank Graham Elliott, James Stock, and Jonathan Wright for useful comments and advice. Support was provided by the National Science Foundation through grants SES-0751056 and SES-1226464. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
Ulrich K. Müller & Mark W. Watson, 2016. "Measuring Uncertainty about Long-Run Predictions," The Review of Economic Studies, vol 83(4), pages 1711-1740. citation courtesy of