IDEAS home Printed from https://ideas.repec.org/p/oec/ecoaaa/979-en.html
   My bibliography  Save this paper

Non-Parametric Stochastic Simulations to Investigate Uncertainty around the OECD Indicator Model Forecasts

Author

Listed:
  • Elena Rusticelli

    (OECD)

Abstract

The forecasting uncertainty around point macroeconomic forecasts is usually measured by the historical performance of the forecasting model, using measures such as root mean squared forecasting errors (RMSE). This measure, however, has the major drawback that it is constant over time and hence does not convey any information on the specific source of uncertainty nor the magnitude and balance of risks in the immediate conjuncture. Moreover, specific parametric assumptions on the probability distribution of forecasting errors are needed in order to draw confidence bands around point forecasts. This paper proposes an alternative time-varying simulated RMSE, obtained by means of non-parametric stochastic simulations, which combines the uncertainty around the model’s parameters and the structural errors term to construct asymmetric confidence bands around point forecasts. The procedure is applied, by way of example, to the short-term real GDP growth forecasts generated by the OECD Indicator Model for Germany. The empirical probability distributions of the GDP growth forecasts, derived through the bootstrapping technique, allow the ex ante probability of, for example, a negative GDP growth forecast for the current quarter to be estimated. The results suggest the presence of peaks of higher uncertainty related to economic recession events, with a balance of risks which became negative in the immediate aftermath of the global financial crisis. Simulations stochastiques non-paramétriques pour étudier l'incertitude autour des prévisions du modèle d'indicateurs de l'OCDE L’incertitude entourant les prévisions macro-économiques ponctuelles est généralement mesurée par la performance historique du modèle de prévision, à l’aide de mesures telles que la moyenne au carré des erreurs de prévisions (EQM). Cette mesure, a cependant l’inconvénient majeur d’être constante dans le temps et donc de ne transmettre aucune information ni sur la source spécifique de l’incertitude, ni sur l’ampleur et la balance des risques liée à la conjoncture immédiate. Par ailleurs, des hypothèses paramétriques spécifiques sur la distribution de probabilité des erreurs de prévision sont nécessaires afin de dessiner des bandes de confiance autour des prévisions ponctuelles. Cet article propose une erreur quadratique moyenne simulé variant dans le temps et obtenue au moyen de simulations stochastiques nonparamétriques, combinent l’incertitude autour des paramètres du modèle et le terme d’erreurs structurelles pour construire des bandes de confiance asymétrique autour des prévisions ponctuelles. La procédure est appliquée, à titre d'exemple, aux prévisions à court terme de la croissance du PIB réel générées par le modèle d’indicateurs de l’OCDE pour l’Allemagne. Les distributions empiriques de probabilité des prévisions de croissance du PIB, obtenues par la technique de bootstrap, permettent d’estimer la probabilité ex ante d’une croissance négative du PIB pour le trimestre en cours. Les résultats suggèrent la présence de pics d’incertitude liée aux événements de la récession économique, avec une balance des risques qui est devenue négative au lendemain de la crise financière mondiale.

Suggested Citation

  • Elena Rusticelli, 2012. "Non-Parametric Stochastic Simulations to Investigate Uncertainty around the OECD Indicator Model Forecasts," OECD Economics Department Working Papers 979, OECD Publishing.
  • Handle: RePEc:oec:ecoaaa:979-en
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1787/5k94kq50b2jd-en
    Download Restriction: no

    More about this item

    Keywords

    distribution empirique de probabilité; empirical probability distribution; Forecasting uncertainty; GDP; Incertitude entourant des prévisions; PIB; simulations stochastiques; stochastic simulations;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:oec:ecoaaa:979-en. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (). General contact details of provider: http://edirc.repec.org/data/edoecfr.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.