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Forecast-error-based estimation of forecast uncertainty when the horizon is increased

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  • Knüppel, Malte

Abstract

Past forecast errors are employed frequently in the estimation of the unconditional forecast uncertainty, and several institutions have increased their forecast horizons in recent times. This work addresses the question of how forecast-error-based estimation can be performed if there are very few errors available for the new forecast horizons. It extends the results of Knüppel (2014) in order to relax the condition on the data structure that is required for the SUR estimator to be independent of unknown quantities. It turns out that the SUR estimator of the forecast uncertainty, which estimates the forecast uncertainty for all horizons jointly, tends to deliver large efficiency gains relative to the OLS estimator (i.e., the sample mean of the squared forecast errors for each individual horizon) in the case of increased forecast horizons. The SUR estimator is applied to the forecast errors of the Bank of England, the US Survey of Professional Forecasters, and the FOMC.

Suggested Citation

  • Knüppel, Malte, 2018. "Forecast-error-based estimation of forecast uncertainty when the horizon is increased," International Journal of Forecasting, Elsevier, vol. 34(1), pages 105-116.
  • Handle: RePEc:eee:intfor:v:34:y:2018:i:1:p:105-116
    DOI: 10.1016/j.ijforecast.2017.08.006
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    Cited by:

    1. Travis J. Berge, 2020. "Time-varying Uncertainty of the Federal Reserve’s Output Gap Estimate," Finance and Economics Discussion Series 2020-012, Board of Governors of the Federal Reserve System (U.S.).

    More about this item

    Keywords

    Multi-step-ahead forecasts; Forecast error variance; SUR;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: 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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