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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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    1. Clark, Todd E. & McCracken, Michael W., 2001. "Tests of equal forecast accuracy and encompassing for nested models," Journal of Econometrics, Elsevier, vol. 105(1), pages 85-110, November.
    2. John W. Galbraith & Greg Tkacz, 2007. "Forecast content and content horizons for some important macroeconomic time series," Canadian Journal of Economics, Canadian Economics Association, vol. 40(3), pages 935-953, August.
    3. West, Kenneth D, 1996. "Asymptotic Inference about Predictive Ability," Econometrica, Econometric Society, vol. 64(5), pages 1067-1084, September.
    4. Isiklar, Gultekin & Lahiri, Kajal, 2007. "How far ahead can we forecast? Evidence from cross-country surveys," International Journal of Forecasting, Elsevier, vol. 23(2), pages 167-187.
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    Cited by:

    1. 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.
    2. Jorge Abad & Javier Suarez, 2018. "The Procyclicality of Expected Credit Loss Provisions," Working Papers wp2018_1806, CEMFI.
    3. 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.

    More about this item

    Keywords

    Hypothesis Testing; Predictive Accuracy; Informativeness;

    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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