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A Three-Frequency Dynamic Factor Model for Nowcasting Canadian Provincial GDP Growth

Author

Listed:
  • Tony Chernis
  • Calista Cheung
  • Gabriella Velasco

Abstract

This paper estimates a three-frequency dynamic factor model for nowcasting Canadian provincial gross domestic product (GDP). Canadian provincial GDP is released by Statistics Canada on an annual basis only, with a significant lag (11 months). This necessitates a mixed-frequency approach that can process timely monthly data, the quarterly national accounts and the annual target variable. The model is estimated on a wide set of provincial, national and international data. We assess the extent to which these indicators can be used to nowcast annual provincial GDP in a pseudo real-time setting and construct indicators of unobserved monthly GDP for each province that can be used to assess the state of regional economies. The monthly activity indicators fit the data well in-sample, are able to track business-cycle turning points across the provinces, and showcase the significant regional heterogeneity that characterizes a large diverse country like Canada. They also provide more timely indications of business-cycle turning points and are able to pick up shorter periods of economic contraction that would not be observed in the annual average. In a pseudo real-time exercise, we find the model outperforms simple benchmarks and is competitive with more sophisticated mixedfrequency approaches such as MIDAS models.

Suggested Citation

  • Tony Chernis & Calista Cheung & Gabriella Velasco, 2017. "A Three-Frequency Dynamic Factor Model for Nowcasting Canadian Provincial GDP Growth," Discussion Papers 17-8, Bank of Canada.
  • Handle: RePEc:bca:bocadp:17-8
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    Cited by:

    1. Tony Chernis & Rodrigo Sekkel, 2018. "Nowcasting Canadian Economic Activity in an Uncertain Environment," Discussion Papers 18-9, Bank of Canada.
    2. Sarah Miller & David Amirault & Laurent Martin, 2017. "What’s Up with Unit Non-Response in the Bank of Canada’s Business Outlook Survey? The Effect of Staff Tenure," Discussion Papers 17-11, Bank of Canada.
    3. 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.
    4. Evzen Kocenda & Karen Poghosyan, 2018. "Nowcasting real GDP growth with business tendency surveys data: A cross country analysis," KIER Working Papers 1002, Kyoto University, Institute of Economic Research.

    More about this item

    Keywords

    Business fluctuations and cycles; Econometric and statistical methods; Regional economic developments;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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