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.
Document Object Identifier (DOI): 10.3386/w18870
Published: Ulrich K. Müller & Mark W. Watson, 2016. "Measuring Uncertainty about Long-Run Predictions," The Review of Economic Studies, vol 83(4), pages 1711-1740.
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