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Forecasting GDP during and after the Great Recession: A contest between small-scale bridge and large-scale dynamic factor models

Author

Listed:
  • Patrice Ollivaud

    (OECD)

  • Pierre-Alain Pionnier

    (OECD)

  • Elena Rusticelli

    (OECD)

  • Cyrille Schwellnus

    (OECD)

  • Seung-Hee Koh

    (OECD)

Abstract

This paper compares the short-term forecasting performance of state-of-the-art large-scale dynamic factor models (DFMs) and the small-scale bridge models routinely used at the OECD. Pseudo-real time out-of-sample forecasts for France, Germany, Italy, Japan, United Kingdom and the United States during and after the Great Recession (2008-2014) suggest that large-scale DFMs are not systematically more accurate than small-scale bridge models, especially at short forecast horizons. Moreover, DFM parameters appear to be highly unstable during the Great Recession (2008-2009), making forecast revisions between successive vintages difficult to explain as revisions cannot be fully attributed to news on specific groups of indicators. The implication for OECD forecasting practice is that there would be no gain from switching from the current small-scale bridge models to large-scale DFMs. Prévoir le PIB pendant et après la Grande Récession : Une comparaison des modèles d'étalonnage de petite taille et des modèles à facteurs dynamiques de grande taille Cet article compare les performances en prévision à court terme de modèles à facteurs dynamiques (DFMs) de grande taille standard dans la littérature à celles des modèles d’étalonnage de petite taille couramment utilisés à l’OCDE pour les exercices de prévision. Des prévisions hors échantillon en pseudo temps réel pour la France, l’Allemagne, l’Italie, le Japon le Royaume-Uni et les États-Unis pendant et après la Grande Récession (2008-2014) montrent que les DFMs de grande taille ne sont pas plus performants, en moyenne, que les modèles d’étalonnage de petite taille, notamment aux horizons les plus courts. De plus, les paramètres des DFMs sont très instables pendant la Grande Récession, ce qui rend les révisions des prévisions d’un exercice à l’autre plus difficiles à expliquer et à relier à différents groupes d’indicateurs. En pratique, nous en concluons que l’OCDE n’aurait pas intérêt, pour ses exercices de prévision, à abandonner les modèles d’étalonnage de petite taille pour les DFMs de grande taille.

Suggested Citation

  • Patrice Ollivaud & Pierre-Alain Pionnier & Elena Rusticelli & Cyrille Schwellnus & Seung-Hee Koh, 2016. "Forecasting GDP during and after the Great Recession: A contest between small-scale bridge and large-scale dynamic factor models," OECD Economics Department Working Papers 1313, OECD Publishing.
  • Handle: RePEc:oec:ecoaaa:1313-en
    DOI: 10.1787/5jlv2jj4mw40-en
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    Keywords

    big data; bridge models; dynamic factor models; modèle d’étalonnage; modèles à facteurs dynamiques; mégadonnées; nowcasting; prévision en temps réel;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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