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Indicator Models of Real GDP Growth in Selected OECD Countries

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  • Franck Sédillot
  • Nigel Pain

Abstract

Accurate and timely information on the current state of economic activity is an important requirement for the policymaking process. Delays in the publication of official statistics mean that a complete picture of economic developments within a particular period emerges only some time after that period has elapsed. The research described in this paper develops a set of econometric models that provide estimates of GDP growth for a number of major OECD countries and zones in the two quarters following the last quarter for which official data have been published. These models exploit the considerable amount of monthly conjunctural information that becomes available before the release of official national accounts data. Information is incorporated from both ‘soft’ indicators, such as business surveys, and ‘hard’ indicators, such as industrial production and retail sales, and use is made of different frequencies of data and a variety of estimation techniques. An automated procedure is ... Modèles de prévision de la croissance du PIB réel dans certains pays de l'OCDE, à l'aide d'indicateurs conjoncturels Disposer d’information précise et à jour sur la situation courante de l’économie est une exigence fondamentale dans le processus de décision économique. Les délais dans la publication des statistiques officielles signifient qu’un tableau complet des évolutions économiques au cours d’une période particulière n’est seulement disponible que quelques temps après la fin de cette période. Les travaux décrits dans cet article présentent un ensemble de modèles économétriques permettant d’estimer la croissance du PIB dans certaines grandes économies ou zones de l’OCDE à un horizon de deux trimestres après le dernier trimestre publié. Ces modèles utilisent une masse importante d’information conjoncturelle mensuelle disponible avant la publication des données officielles de comptes nationaux. Cette information provient à la fois d’indicateurs ‘mous’ comme les enquêtes d’opinion ou d’indicateur ‘durs’ comme la production industrielle ou les ventes de détail. Différentes fréquences et méthodes ...

Suggested Citation

  • Franck Sédillot & Nigel Pain, 2003. "Indicator Models of Real GDP Growth in Selected OECD Countries," OECD Economics Department Working Papers 364, OECD Publishing.
    • Handle: RePEc:oec:ecoaaa:364-en
    DOI: 10.1787/275257320252
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    Cited by:

    1. Pete Richardson, 2018. "Nowcasting and the Use of Big Data in Short-Term Macroeconomic Forecasting: A Critical Review," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 505-506, pages 65-87.
    2. Antipa, Pamfili & Barhoumi, Karim & Brunhes-Lesage, Véronique & Darné, Olivier, 2012. "Nowcasting German GDP: A comparison of bridge and factor models," Journal of Policy Modeling, Elsevier, vol. 34(6), pages 864-878.
    3. Kai Carstensen & Steffen Henzel & Johannes Mayr & Klaus Wohlrabe, 2009. "IFOCAST: Methoden der ifo-Kurzfristprognose," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(23), pages 15-28, December.
    4. Golinelli, Roberto & Parigi, Giuseppe, 2008. "Real-time squared: A real-time data set for real-time GDP forecasting," International Journal of Forecasting, Elsevier, vol. 24(3), pages 368-385.
    5. Hanan Naser, 2015. "Estimating and forecasting Bahrain quarterly GDP growth using simple regression and factor-based methods," Empirical Economics, Springer, vol. 49(2), pages 449-479, September.
    6. Works, Richard Floyd, 2016. "Econometric modeling of exchange rate determinants by market classification: An empirical analysis of Japan and South Korea using the sticky-price monetary theory," MPRA Paper 76382, University Library of Munich, Germany.
    7. Qin, Duo & Cagas, Marie Anne & Ducanes, Geoffrey & Magtibay-Ramos, Nedelyn & Quising, Pilipinas, 2008. "Automatic leading indicators versus macroeconometric structural models: A comparison of inflation and GDP growth forecasting," International Journal of Forecasting, Elsevier, vol. 24(3), pages 399-413.
    8. Schwarzmüller, Tim, 2015. "Model pooling and changes in the informational content of predictors: An empirical investigation for the euro area," Kiel Working Papers 1982, Kiel Institute for the World Economy (IfW Kiel).
    9. Daniel Grenouilleau, 2006. "The Stacked Leading Indicators Dynamic Factor Model: A Sensitivity Analysis of Forecast Accuracy using Bootstrapping," European Economy - Economic Papers 2008 - 2015 249, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    10. Esteves, Paulo Soares, 2013. "Direct vs bottom–up approach when forecasting GDP: Reconciling literature results with institutional practice," Economic Modelling, Elsevier, vol. 33(C), pages 416-420.
    11. Olivier Darne, 2008. "Using business survey in industrial and services sector to nowcast GDP growth:The French case," Economics Bulletin, AccessEcon, vol. 3(32), pages 1-8.
    12. Daniel Grenouilleau, 2004. "A sorted leading indicators dynamic (SLID) factor model for short-run euro-area GDP forecasting," European Economy - Economic Papers 2008 - 2015 219, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    13. Guido Bulligan & Roberto Golinelli & Giuseppe Parigi, 2010. "Forecasting monthly industrial production in real-time: from single equations to factor-based models," Empirical Economics, Springer, vol. 39(2), pages 303-336, October.
    14. Enrico D’Elia & Francesca Faedda & Giacomo Giannone, 2020. "Un modello statistico per il monitoraggio delle entrate tributarie (MoME)," Working Papers wp2020-5, Ministry of Economy and Finance, Department of Finance.
    15. Claveria, Oscar & Pons, Ernest & Ramos, Raul, 2007. "Business and consumer expectations and macroeconomic forecasts," International Journal of Forecasting, Elsevier, vol. 23(1), pages 47-69.
    16. Barhoumi, K. & Brunhes-Lesage, V. & Darné, O. & Ferrara, L. & Pluyaud, B. & Rouvreau, B., 2008. "Monthly forecasting of French GDP: A revised version of the OPTIM model," Working papers 222, Banque de France.
    17. Works, Richard & Haan, Perry, 2017. "An Empirical Study of Japanese and South Korean Exchange Rates Using the Sticky-Price Monetary Theory," MPRA Paper 77235, University Library of Munich, Germany.
    18. Oliver Hülsewig & Johannes Mayr & Stéphane Sorbe, 2007. "Assessing the Forecast Properties of the CESifo World Economic Climate Indicator: Evidence for the Euro Area," ifo Working Paper Series 46, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    19. Gerit Vogt, 2004. "Prognose von Umsatz und Bruttowertschöpfung des verarbeitenden Gewerbes in Sachsen für das Jahr 2004 (Prognose der Bruttowertschöpfung des sächsischen verarbeitenden Gewerbes 2004)," ifo Dresden berichtet, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 11(04), pages 23-30, August.
    20. Gilles Mourre & Michael Thiel, 2006. "Monitoring short-term labour cost developments in the European Union: which indicators to trust?," European Economy - Economic Papers 2008 - 2015 258, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    21. Barhoumi, K. & Brunhes-Lesage, V. & Ferrara, L. & Pluyaud, B. & Rouvreau, B. & Darné, O., 2008. "OPTIM: a quarterly forecasting tool for French GDP," Quarterly selection of articles - Bulletin de la Banque de France, Banque de France, issue 13, pages 31-47, Autumn.
    22. Stéphanie Guichard & Elena Rusticelli, 2011. "A Dynamic Factor Model for World Trade Growth," OECD Economics Department Working Papers 874, OECD Publishing.
    23. Giuseppe Parigi & Roberto Golinelli, 2007. "The use of monthly indicators to forecast quarterly GDP in the short run: an application to the G7 countries," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(2), pages 77-94.

    More about this item

    Keywords

    bridge equations; données mensuelles; indicator models; modèle d'indicateurs conjoncturels; monthly data; prévisions économiques à court terme; short-term economic forecasts; équation de passage;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • 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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