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Measuring Uncertainty about Long-Run Predictions

Author

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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," Review of Economic Studies, Oxford University Press, 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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    Citations

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    Cited by:

    1. Rhys Bidder & Ian Dew-Becker, 2016. "Long-Run Risk Is the Worst-Case Scenario," American Economic Review, American Economic Association, vol. 106(9), pages 2494-2527, September.
    2. Giuliano Curatola & Michael Donadelli & Patrick Gruning & Christoph Meinerding, 2016. "Investment-Specific Shocks, Business Cycles, and Asset Prices," Bank of Lithuania Working Paper Series 36, Bank of Lithuania.
    3. Doh, Taeyoung, 2017. "Trend and Uncertainty in the Long-Term Real Interest Rate: Bayesian Exponential Tilting with Survey Data," Research Working Paper RWP 17-8, Federal Reserve Bank of Kansas City.
    4. repec:eee:econom:v:209:y:2019:i:1:p:18-34 is not listed on IDEAS
    5. repec:spr:empeco:v:53:y:2017:i:1:d:10.1007_s00181-017-1228-3 is not listed on IDEAS
    6. Pretis, Felix & Roser, Max, 2017. "Carbon dioxide emission-intensity in climate projections: Comparing the observational record to socio-economic scenarios," Energy, Elsevier, vol. 135(C), pages 718-725.
    7. Kaihua Deng & Chang-Jin Kim, 2015. "Predicting Stock Returns — The Information Content Of Predictors Across Horizons," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 10(02), pages 1-27, December.
    8. repec:pal:buseco:v:52:y:2017:i:3:d:10.1057_s11369-017-0042-4 is not listed on IDEAS
    9. Philip Barrett, 2018. "Interest-Growth Differentials and Debt Limits in Advanced Economies," IMF Working Papers 18/82, International Monetary Fund.
    10. repec:aea:jecper:v:31:y:2017:i:2:p:59-86 is not listed on IDEAS
    11. Lecznar, Jonathan & Sharp, Robert & Sarte, Pierre-Daniel G., 2013. "Characterizing the Unusual Path of U.S. Output During and After the Great Recession," Economic Quarterly, Federal Reserve Bank of Richmond, issue 3Q, pages 163-192.
    12. repec:eee:intfor:v:34:y:2018:i:1:p:1-16 is not listed on IDEAS

    More about this item

    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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