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Measuring GDP Forecast Uncertainty Using Quantile Regressions

Author

Listed:
  • Thomas Laurent

    (OECD)

  • Tomasz Koźluk

    (OECD)

Abstract
Uncertainty is inherent to forecasting and assessing the uncertainty surrounding a point forecast is as important as the forecast itself. Following Cornec (2010), a method to assess the uncertainty around the indicator models used at OECD to forecast GDP growth of the six largest member countries is developed, using quantile regressions to construct a probability distribution of future GDP, as opposed to mean point forecasts. This approach allows uncertainty to be assessed conditionally on the current state of the economy and is totally model based and judgement free. The quality of the computed distributions is tested against other approaches to measuring forecast uncertainty and a set of uncertainty indicators is constructed in order to help exploiting the most helpful information. Mesure de l'incertitude sur les prévisions du PIB à l'aide de régressions quantiles L’incertitude est inhérente à la prévision, et évaluer l’incertitude autour d’une prévision est aussi important que la prévision elle-même. A la suite de Cornec (2010), une méthode pour évaluer l’incertitude autour des modèles d’indicateurs utilisés à l’OCDE pour prévoir la croissance des six plus grandes économies membres est développée, utilisant des régressions quantiles pour construire une distribution de probabilité du PIB future, plutôt qu’une prévision ponctuelle. Cette approche permet d’évaluer l’incertitude conditionnellement à l’état actuel de l’économie et est fondée sur le modèle, sans jugement. La qualité des distributions calculées est testée contre des approches alternatives de la mesure de l’incertitude, et un ensemble d’indicateurs d’incertitudes est construit pour aider à exploiter les informations les plus pertinentes.

Suggested Citation

  • Thomas Laurent & Tomasz Koźluk, 2012. "Measuring GDP Forecast Uncertainty Using Quantile Regressions," OECD Economics Department Working Papers 978, OECD Publishing.
    • Handle: RePEc:oec:ecoaaa:978-en
    DOI: 10.1787/5k95xd76jvvg-en
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    Citations

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    Cited by:

    1. Bec, Frédérique & De Gaye, Annabelle, 2016. "How do oil price forecast errors impact inflation forecast errors? An empirical analysis from US, French and UK inflation forecasts," Economic Modelling, Elsevier, vol. 53(C), pages 75-88.
    2. F. Bec & A. De Gaye, 2014. "How do oil price forecast errors impact inflation forecast errors? An empirical analysis from French and US inflation forecasts," Working papers 523, Banque de France.

    More about this item

    Keywords

    forecasting; GDP; incertitude; PIB; prévisions; quantile regression; régression quantile; uncertainty;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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