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A Large Canadian Database for Macroeconomic Analysis

Author

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  • Olivier Fortin-Gagnon
  • Maxime Leroux
  • Dalibor Stevanovic
  • Stéphane Surprenant

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 & Stéphane Surprenant, 2018. "A Large Canadian Database for Macroeconomic Analysis," CIRANO Working Papers 2018s-25, CIRANO.
    • Handle: RePEc:cir:cirwor:2018s-25
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    1. Stefano Micossi, 2015. "The Monetary Policy of the European Central Bank (2002-2015)," Bruges European Economic Policy Briefings 35, European Economic Studies Department, College of Europe.
    2. Nathan Bedock & Dalibor Stevanovic, 2017. "An empirical study of credit shock transmission in a small open economy," Canadian Journal of Economics, Canadian Economics Association, vol. 50(2), pages 541-570, May.
    3. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    4. Ilian Mihov, 2001. "Monetary policy implementation and transmission in the European Monetary Union," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 16(33), pages 370-406.
    5. Arturo Estrella & Frederic S. Mishkin, 1998. "Predicting U.S. Recessions: Financial Variables As Leading Indicators," The Review of Economics and Statistics, MIT Press, vol. 80(1), pages 45-61, February.
    6. Michael W. McCracken & Serena Ng, 2016. "FRED-MD: A Monthly Database for Macroeconomic Research," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 574-589, October.
    7. Kevin Moran & Dalibor Stevanovic & Adam Kader Touré, 2022. "Macroeconomic uncertainty and the COVID‐19 pandemic: Measure and impacts on the Canadian economy," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 55(S1), pages 379-405, February.
    8. Erceg, Christopher & Levin, Andrew, 2006. "Optimal monetary policy with durable consumption goods," Journal of Monetary Economics, Elsevier, vol. 53(7), pages 1341-1359, October.
    9. Robert B. Barsky & Christopher L. House & Miles S. Kimball, 2007. "Sticky-Price Models and Durable Goods," American Economic Review, American Economic Association, vol. 97(3), pages 984-998, June.
    10. 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.
    11. Marc-André Gosselin & Greg Tkacz, 2001. "Evaluating Factor Models: An Application to Forecasting Inflation in Canada," Staff Working Papers 01-18, Bank of Canada.
    12. John W. Galbraith & Greg Tkacz, 2007. "Forecast content and content horizons for some important macroeconomic time series," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 40(3), pages 935-953, August.
    13. Seung C. Ahn & Alex R. Horenstein, 2013. "Eigenvalue Ratio Test for the Number of Factors," Econometrica, Econometric Society, vol. 81(3), pages 1203-1227, May.
    14. Dedola, Luca & Lippi, Francesco, 2005. "The monetary transmission mechanism: Evidence from the industries of five OECD countries," European Economic Review, Elsevier, vol. 49(6), pages 1543-1569, August.
    15. Bai, Jushan & Ng, Serena, 2008. "Forecasting economic time series using targeted predictors," Journal of Econometrics, Elsevier, vol. 146(2), pages 304-317, October.
    16. Rachidi Kotchoni & Maxime Leroux & Dalibor Stevanovic, 2019. "Macroeconomic forecast accuracy in a data‐rich environment," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(7), pages 1050-1072, November.
    17. Champagne, Julien & Sekkel, Rodrigo, 2018. "Changes in monetary regimes and the identification of monetary policy shocks: Narrative evidence from Canada," Journal of Monetary Economics, Elsevier, vol. 99(C), pages 72-87.
    18. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2005. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 120(1), pages 387-422.
    19. Elliott, Graham & Gargano, Antonio & Timmermann, Allan, 2013. "Complete subset regressions," Journal of Econometrics, Elsevier, vol. 177(2), pages 357-373.
    20. Bai, Jushan & Ng, Serena, 2007. "Determining the Number of Primitive Shocks in Factor Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 52-60, January.
    21. Dennis W. Jansen & Ruby P. Kishan & Diego E. Vacaflores, 2013. "Sectoral Effects of Monetary Policy: The Evidence from Publicly Traded Firms," Southern Economic Journal, John Wiley & Sons, vol. 79(4), pages 946-970, April.
    22. Mao Takongmo, Charles Olivier & Stevanovic, Dalibor, 2015. "Selection Of The Number Of Factors In Presence Of Structural Instability: A Monte Carlo Study," L'Actualité Economique, Société Canadienne de Science Economique, vol. 91(1-2), pages 177-233, Mars-Juin.
    23. Christina D. Romer & David H. Romer, 2004. "A New Measure of Monetary Shocks: Derivation and Implications," American Economic Review, American Economic Association, vol. 94(4), pages 1055-1084, September.
    24. Kyle Jurado & Sydney C. Ludvigson & Serena Ng, 2015. "Measuring Uncertainty," American Economic Review, American Economic Association, vol. 105(3), pages 1177-1216, March.
    25. Alexei Onatski, 2010. "Determining the Number of Factors from Empirical Distribution of Eigenvalues," The Review of Economics and Statistics, MIT Press, vol. 92(4), pages 1004-1016, November.
    26. Edward E. Leamer, 2015. "Housing Really Is the Business Cycle: What Survives the Lessons of 2008–09?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(S1), pages 43-50, March.
    27. Gert Peersman & Frank Smets, 2005. "The Industry Effects of Monetary Policy in the Euro Area," Economic Journal, Royal Economic Society, vol. 115(503), pages 319-342, April.
    28. Mikkel Plagborg‐Møller & Christian K. Wolf, 2021. "Local Projections and VARs Estimate the Same Impulse Responses," Econometrica, Econometric Society, vol. 89(2), pages 955-980, March.
    29. Alessi, Lucia & Barigozzi, Matteo & Capasso, Marco, 2010. "Improved penalization for determining the number of factors in approximate factor models," Statistics & Probability Letters, Elsevier, vol. 80(23-24), pages 1806-1813, December.
    30. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    31. Dalibor Stevanovic, 2015. "Factor augmented autoregressive distributed lag models with macroeconomic applications," CIRANO Working Papers 2015s-33, CIRANO.
    32. Helena Dominguez‐Torres & Luis A. Hierro, 2019. "The Regional Effects Of Monetary Policy: A Survey Of The Empirical Literature," Journal of Economic Surveys, Wiley Blackwell, vol. 33(2), pages 604-638, April.
    33. Nicholas Bloom, 2009. "The Impact of Uncertainty Shocks," Econometrica, Econometric Society, vol. 77(3), pages 623-685, May.
    34. Jeremy Kronick & Steve Ambler, 2019. "Do demographics affect monetary policy transmission in Canada?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(2), pages 787-811, April.
    35. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
    36. Dennis W. Jansen & Ruby P. Kishan & Diego E. Vacaflores, 2013. "Sectoral Effects of Monetary Policy: The Evidence from Publicly Traded Firms," Southern Economic Journal, John Wiley & Sons, vol. 79(4), pages 946-970, April.
    37. Cantelmo, Alessandro & Melina, Giovanni, 2018. "Monetary policy and the relative price of durable goods," Journal of Economic Dynamics and Control, Elsevier, vol. 86(C), pages 1-48.
    38. Amengual, Dante & Watson, Mark W., 2007. "Consistent Estimation of the Number of Dynamic Factors in a Large N and T Panel," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 91-96, January.
    39. John Galbraith & Greg Tkacz, 2007. "How Far Can Forecasting Models Forecast? Forecast Content Horizons for Some Important Macroeconomic Variables," Staff Working Papers 07-1, Bank of Canada.
    40. Hallin, Marc & Liska, Roman, 2007. "Determining the Number of Factors in the General Dynamic Factor Model," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 603-617, June.
    41. Jushan Bai & Serena Ng, 2006. "Confidence Intervals for Diffusion Index Forecasts and Inference for Factor-Augmented Regressions," Econometrica, Econometric Society, vol. 74(4), pages 1133-1150, July.
    42. Jean Boivin & Marc P. Giannoni, 2006. "Has Monetary Policy Become More Effective?," The Review of Economics and Statistics, MIT Press, vol. 88(3), pages 445-462, August.
    43. Raffaella Giacomini & Barbara Rossi, 2010. "Forecast comparisons in unstable environments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 595-620.
    44. Marcelo C. Medeiros & Gabriel F. R. Vasconcelos & Álvaro Veiga & Eduardo Zilberman, 2021. "Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 98-119, January.
    45. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
    46. Tibor F. Liska, 2007. "The Liska model," Society and Economy, Akadémiai Kiadó, Hungary, vol. 29(3), pages 363-381, December.
    47. Leandro Prados de la Escosura, 2016. "Mismeasuring long-run growth: the bias from splicing national accounts—the case of Spain," Cliometrica, Journal of Historical Economics and Econometric History, Association Française de Cliométrie (AFC), vol. 10(3), pages 251-275, September.
    48. Bai, Jushan & Ng, Serena, 2008. "Large Dimensional Factor Analysis," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(2), pages 89-163, June.
    49. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
    50. Micossi, Stefano, 2015. "The Monetary Policy of the European Central Bank (2002-2015)," CEPS Papers 10610, Centre for European Policy Studies.
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    Cited by:

    1. 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.
    2. 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.
    3. Ardia, David & Bluteau, Keven & Kassem, Alaa, 2021. "A century of Economic Policy Uncertainty through the French–Canadian lens," Economics Letters, Elsevier, vol. 205(C).
    4. Philippe Goulet Coulombe, 2020. "The Macroeconomy as a Random Forest," Papers 2006.12724, arXiv.org, revised Mar 2021.
    5. Manuel Paquette-Dupuis & Dalibor Stevanovic & Rachidi Kotchoni, 2019. "Prévisions de l’activité économique en temps de crise," CIRANO Project Reports 2019rp-04, CIRANO.
    6. 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.
    7. Kevin Moran & Dalibor Stevanovic & Stephane Surprenant, 2024. "Risk Scenarios and Macroeconomic Forecasts," Working Papers 24-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Apr 2024.
    8. Matteo Barigozzi & Claudio Lissona & Lorenzo Tonni, 2024. "Large datasets for the Euro Area and its member countries and the dynamic effects of the common monetary policy," Papers 2410.05082, arXiv.org.
    9. 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.
    10. 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.
    11. 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.
    12. 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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    Keywords

    Big Data; Factor Model; Forecasting; Structural Analysis;
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