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


  • Elena Rusticelli



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

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    References listed on IDEAS

    1. Carmen M. Reinhart & Kenneth S. Rogoff, 2009. "The Aftermath of Financial Crises," American Economic Review, American Economic Association, vol. 99(2), pages 466-472, May.
    2. Hansen, Bruce E., 1999. "Threshold effects in non-dynamic panels: Estimation, testing, and inference," Journal of Econometrics, Elsevier, vol. 93(2), pages 345-368, December.
    3. Matteo Cacciatore & Romain Duval & Giuseppe Fiori, 2012. "Short-Term Gain or Pain? A DSGE Model-Based Analysis of the Short-Term Effects of Structural Reforms in Labour and Product Markets," OECD Economics Department Working Papers 948, OECD Publishing.
    4. Carmen M. Reinhart & Kenneth S. Rogoff, 2010. "Growth in a Time of Debt," American Economic Review, American Economic Association, vol. 100(2), pages 573-578, May.
    5. Romain Bouis & Romain Duval, 2011. "Raising Potential Growth After the Crisis: A Quantitative Assessment of the Potential Gains from Various Structural Reforms in the OECD Area and Beyond," OECD Economics Department Working Papers 835, OECD Publishing.
    6. Thomas Laubach, 2009. "New Evidence on the Interest Rate Effects of Budget Deficits and Debt," Journal of the European Economic Association, MIT Press, vol. 7(4), pages 858-885, June.
    7. Tenhofen Jörn & Wolff Guntram B. & Heppke-Falk Kirsten H., 2010. "The Macroeconomic Effects of Exogenous Fiscal Policy Shocks in Germany: A Disaggregated SVAR Analysis," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 230(3), pages 328-355, June.
    8. Fabian Valencia & Luc Laeven, 2008. "Systemic Banking Crises; A New Database," IMF Working Papers 08/224, International Monetary Fund.
    9. Romain Bouis & Romain Duval & Fabrice Murtin, 2011. "The Policy and Institutional Drivers of Economic Growth Across OECD and Non-OECD Economies: New Evidence from Growth Regressions," OECD Economics Department Working Papers 843, OECD Publishing.
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    More about this item


    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

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