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A Large Canadian Database for Macroeconomic Analysis

Author

Listed:
  • Olivier Fortin-Gagnon

    (Groupe Desjardins)

  • Maxime Leroux

    (University of Quebec in Montreal)

  • Dalibor Stevanovic

    (University of Quebec in Montreal)

  • Stephane Surprenant

    (University of Quebec in Montreal)

Abstract

This paper provides a large-scale Canadian macroeconomic database and shows its usefulness for empirical macroeconomic analysis. The dataset contains hundreds of Canadian and provincial economic indicators. It is designed to be updated regularly and real-time vintages are publicly available. It relieves users to deal with data changes and methodological revisions. We show four useful features of this dataset for macroeconomic research. First, the factor structure explains a sizeable part of the variation of the dataset and appears as an appropriate means of dimension reduction. Second, the dataset is useful to capture turning points of the Canadian business cycle. Third, it has substantial predictive power when forecasting key macroeconomic indicators. Fourth, the richness of the panel is used to study the effectiveness of monetary policy across regions and sectors.

Suggested Citation

  • Olivier Fortin-Gagnon & Maxime Leroux & Dalibor Stevanovic & Stephane Surprenant, 2020. "A Large Canadian Database for Macroeconomic Analysis," Working Papers 20-07, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    • Handle: RePEc:bbh:wpaper:20-07
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    File URL: https://chairemacro.esg.uqam.ca/wp-content/uploads/sites/146/LSS_CAN_MC_Draft_March2021.pdf
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    Cited by:

    1. Philippe Goulet Coulombe, 2021. "The Macroeconomy as a Random Forest," Working Papers 21-05, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    2. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    3. Foroni, Claudia & Marcellino, Massimiliano & Stevanovic, Dalibor, 2022. "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," International Journal of Forecasting, Elsevier, vol. 38(2), pages 596-612.
    4. Ardia, David & Bluteau, Keven & Kassem, Alaa, 2021. "A century of Economic Policy Uncertainty through the French–Canadian lens," Economics Letters, Elsevier, vol. 205(C).
    5. Julien Champagne & Guillaume Poulin-Bellisle & Rodrigo Sekkel, 2018. "Evaluating the Bank of Canada Staff Economic Projections Using a New Database of Real-Time Data and Forecasts," Staff Working Papers 18-52, Bank of Canada.
    6. Michael W. McCracken & Serena Ng, 2021. "FRED-QD: A Quarterly Database for Macroeconomic Research," Review, Federal Reserve Bank of St. Louis, vol. 103(1), pages 1-44, January.
    7. Philippe Goulet Coulombe, 2020. "The Macroeconomy as a Random Forest," Papers 2006.12724, arXiv.org, revised Mar 2021.
    8. Kevin Moran & Adam Abdel Kader Touré & Dalibor Stevanovic, 2020. "Incertitude et effets macroéconomiques : mise à jour dans le contexte de la pandémie COVID-19," CIRANO Papers 2020pe-33, CIRANO.
    9. Manuel Paquette-Dupuis & Dalibor Stevanovic & Rachidi Kotchoni, 2019. "Prévisions de l’activité économique en temps de crise," CIRANO Project Reports 2019rp-04, CIRANO.
    10. Kevin Moran & Simplice Aimé Nono & Imad Rherrad, 2018. "Forecasting with Many Predictors: How Useful are National and International Confidence Data?," Cahiers de recherche 1814, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.

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