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Measuring Uncertainty about Long-Run Predictions

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  • Ulrich K. Müller
  • Mark W. Watson

Abstract

Long-run forecasts of economic variables play an important role in policy, planning, and portfolio decisions. We consider forecasts of the long-horizon average of a scalar variable, typically the growth rate of an economic variable. The main contribution is the construction of prediction 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 prediction sets for 10- to 75-year average growth rates of U.S. real per capita GDP and consumption, productivity, price level, stock prices, and population.

Suggested Citation

  • Ulrich K. Müller & Mark W. Watson, 2016. "Measuring Uncertainty about Long-Run Predictions," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 83(4), pages 1711-1740.
    • Handle: RePEc:oup:restud:v:83:y:2016:i:4:p:1711-1740.
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    File URL: http://hdl.handle.net/10.1093/restud/rdw003
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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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