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Forecasting Monthly GDP for Canada

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  • Annabelle Mourougane

Abstract

The objective of this paper is to develop a short-term indicator-based model to predict quarterly GDP in Canada by efficiently exploiting all available monthly information. To this aim, monthly forecasting equations are estimated using the GDP series published every month by Statistics Canada as well as other monthly indicators. The procedures are automated and the model can be run whenever major monthly data are released, allowing the appropriate choice of the model according to the information set available. The most important gain from this procedure is for the current-quarter forecast when one or two months of GDP data are available, with all monthly models estimated in the paper outperforming a standard quarterly autoregressive model in terms of size of errors. The use of indicators also appears to improve forecasting performance, especially when an average of indicator-based models is used. Real-time forecasting performance of the average model appear to be good, with an apparent stability of the estimates from one update to the next, despite the extensive use of monthly data. The latter result should nonetheless be interpreted with caution and will need to be re-assessed when more data become available. Prévoir le PIB mensuel au Canada L’objectif de cet article est de développer un modèle d’indicateurs conjoncturels pour prédire le PIB trimestriel au Canada en utilisant de manière efficace toute l’information mensuelle disponible. À cette fin, des équations mensuelles de prévisions de court terme sont estimées en utilisant la série de PIB publiée chaque mois par Statistique Canada et d’autres indicateurs conjoncturels. Les procédures ont été automatisées et le modèle peut être mis à jour chaque fois qu’une donnée importante est publiée, la spécification du modèle variant ainsi en fonctions de l’ensemble des données disponibles. Le gain le plus important de la procédure développée est obtenue pour les prévisions du trimestre courant quand un ou deux mois de données du PIB mensuel sont disponibles. Dans ce cas, tous les modèles mensuels estimés dans cet article ont des erreurs de prévisions inférieures à celle d’un modèle trimestriel autorégressif standard. L’utilisation d’indicateurs conjoncturels améliore les performances en termes de prévisions, en particulier lorsqu’une moyenne de tous les modèles d’indicateurs conjoncturels est utilisée. Les prévisions réalisées en temps réel en faisant la moyenne des différents modèles d’indicateurs conjoncturels se sont avérées de qualité satisfaisante, avec une stabilité apparente des estimations successives, malgré l’utilisation extensive de données mensuelles. Ces résultats doivent toutefois être interprétés avec prudence et devront être vérifiés quand plus de données seront disponibles.

Suggested Citation

  • Annabelle Mourougane, 2006. "Forecasting Monthly GDP for Canada," OECD Economics Department Working Papers 515, OECD Publishing.
  • Handle: RePEc:oec:ecoaaa:515-en
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    File URL: http://dx.doi.org/10.1787/421416670553
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    References listed on IDEAS

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    1. Alfonso Arpaia & Giuseppe Carone, 2004. "Do labour taxes (and their composition) affect wages in the short and in the long run?," Public Economics 0411004, EconWPA.
    2. Isabelle Joumard, 2003. "Tax systems in European Union countries," OECD Economic Studies, OECD Publishing, vol. 2002(1), pages 91-151.
    3. Kwang-Yeol Yoo, 2003. "Corporate Taxation of Foreign Direct Investment Income 1991-2001," OECD Economics Department Working Papers 365, OECD Publishing.
    4. Francesco Daveri & Guido Tabellini, 2000. "Unemployment, growth and taxation in industrial countries," Economic Policy, CEPR;CES;MSH, vol. 15(30), pages 47-104, April.
    5. Katrin Millock & Céline Nauges & Thomas Sterner, 2004. "Environmental Taxes: A Comparison of French and Swedish Experience from Taxes on Industrial Air Pollution," ifo DICE Report, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 2(1), pages 30-34, 04.
    6. Calmfors, L. & Nymoen, R., 1990. "Real Wage Adjustment And Employment Policies In The Nordic Countries," Papers 461, Stockholm - International Economic Studies.
    7. Laurent Flochel & Thierry Madies, 2002. "Interjurisdictional Tax Competition in a Federal System of Overlapping Revenue Maximizing Governments," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 9(2), pages 121-141, March.
    8. Alfonso Arpaia & Giuseppe Carone, 2004. "Do labour taxes (and their composition) affect wages in the short and the long run? - Alfonso Arpaia and Giuseppe Carone," European Economy - Economic Papers 2008 - 2015 216, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    9. Ann Vourc'h & Patrick Lenain, 2001. "Comment encourager une croissance écologiquement durable en France ?," OECD Economics Department Working Papers 314, OECD Publishing.
    10. Willi Leibfritz & John Thornton & Alexandra Bibbee, 1997. "Taxation and Economic Performance," OECD Economics Department Working Papers 176, OECD Publishing.
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    Cited by:

    1. Bragoli, Daniela & Modugno, Michele, 2017. "A now-casting model for Canada: Do U.S. variables matter?," International Journal of Forecasting, Elsevier, vol. 33(4), pages 786-800.

    More about this item

    Keywords

    Canada; Canada; estimations en temps réel; indicator models; modèle d'indicateurs conjoncturels; monthly GDP; PIB mensuel; prévisions de court terme; real-time estimations; short-term forecasts;

    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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