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How far can we forecast? Statistical tests of the predictive content

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  • Breitung, Jörg
  • Knüppel, Malte

Abstract

Forecasts are useless whenever the forecast error variance fails to be smaller than the unconditional variance of the target variable. This paper develops tests for the null hypothesis that forecasts become uninformative beyond some limiting forecast horizon h. Following Diebold and Mariano (DM, 1995) we propose a test based on the comparison of the mean-squared error of the forecast and the sample variance. We show that the resulting test does not possess a limiting normal distribution and suggest two simple modifications of the DM-type test with different limiting null distributions. Furthermore, a forecast encompassing test is developed that tends to better control the size of the test. In our empirical analysis, we apply our tests to macroeconomic forecasts from the survey of Consensus Economics. Our results suggest that forecasts of macroeconomic key variables are barely informative beyond 2-4 quarters ahead.

Suggested Citation

  • Breitung, Jörg & Knüppel, Malte, 2018. "How far can we forecast? Statistical tests of the predictive content," Discussion Papers 07/2018, Deutsche Bundesbank.
    • Handle: RePEc:zbw:bubdps:072018
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    References listed on IDEAS

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    Cited by:

    1. Cepni, Oguzhan & Clements, Michael P., 2024. "How local is the local inflation factor? Evidence from emerging European countries," International Journal of Forecasting, Elsevier, vol. 40(1), pages 160-183.
    2. Jörg Breitung & Malte Knüppel, 2021. "How far can we forecast? Statistical tests of the predictive content," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(4), pages 369-392, June.
    3. Jorge Abad & Javier Suarez, 2018. "The Procyclicality of Expected Credit Loss Provisions," Working Papers wp2018_1806, CEMFI.
    4. Günter Bamberg & Sebastian Heiden & Michael Krapp, 2023. "Langfrist-Prognose von Performance-Indizes: Vergleich einiger Verfahren [Long-term forecast of performance indices: comparison of some procedures]," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 17(2), pages 161-181, June.
    5. Stamer, Vincent, 2022. "Thinking Outside the Container: A Sparse Partial Least Squares Approach to Forecasting Trade Flows," VfS Annual Conference 2022 (Basel): Big Data in Economics 264096, Verein für Socialpolitik / German Economic Association.
    6. Marc-Oliver Pohle, 2020. "The Murphy Decomposition and the Calibration-Resolution Principle: A New Perspective on Forecast Evaluation," Papers 2005.01835, arXiv.org.
    7. Gibbs, Christopher G. & Vasnev, Andrey L., 2024. "Conditionally optimal weights and forward-looking approaches to combining forecasts," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1734-1751.
    8. M. Chudý & S. Karmakar & W. B. Wu, 2020. "Long-term prediction intervals of economic time series," Empirical Economics, Springer, vol. 58(1), pages 191-222, January.
    9. repec:ags:aaea22:335690 is not listed on IDEAS
    10. Jörg Döpke & Karsten Müller & Lars Tegtmeier, 2023. "Moments of cross‐sectional stock market returns and the German business cycle," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 52(2), July.
    11. Stamer, Vincent, 2024. "Thinking outside the container: A sparse partial least squares approach to forecasting trade flows," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1336-1358.
    12. Regmi, Hari & Kuethe, Todd H. & Foster, Kenneth A., 2022. "Evaluation of USDA's Agricultural Exports Projections," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322363, Agricultural and Applied Economics Association.
    13. Daniel Borup & Bent Jesper Christensen & Yunus Emre Ergemen, 2019. "Assessing predictive accuracy in panel data models with long-range dependence," CREATES Research Papers 2019-04, Department of Economics and Business Economics, Aarhus University.
    14. Jean-Yves Pitarakis, 2023. "Direct Multi-Step Forecast based Comparison of Nested Models via an Encompassing Test," Papers 2312.16099, arXiv.org.
    15. Zhu, Tiantian & Haugen, Stein & Liu, Yiliu & Yang, Xue, 2023. "A value of prediction model to estimate optimal response time to threats for accident prevention," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    16. Carola Binder & Tucker S. Mcelroy & Xuguang S. Sheng, 2022. "The Term Structure of Uncertainty: New Evidence from Survey Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 54(1), pages 39-71, February.
    17. Fabian Kruger & Hendrik Plett, 2022. "Prediction intervals for economic fixed-event forecasts," Papers 2210.13562, arXiv.org, revised Mar 2024.

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    More about this item

    Keywords

    Hypothesis Testing; Predictive Accuracy; Informativeness;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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