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GDP by Industry in Real Time: Are Revisions Well Behaved?

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  • Patrick Rizzetto

Abstract

The monthly data for real gross domestic product (GDP) by industry are used extensively in real time both to ground the Bank of Canada’s monitoring of economic activity and in the Bank’s nowcasting tools, making these data one of the most important high-frequency time series for Canadian nowcasting. This note documents the series’ real-time properties using the reference period August 2007 to September 2017. It shows that revisions to headline GDP-by-industry growth are generally well behaved; that is, they have a zero mean and low variance and are not revised predictably. Therefore, the signal for overall growth from the first GDP-by-industry release can be received with a good deal of confidence. This analysis suggests, however, that while this result is true on average, there are times when revisions can be important. Two examples discussed are when the economy begins to contract or expand and when end-of-quarter revisions are made. Further, revisions to the industrial sectors are not as well behaved. Revisions to some sectors do not have a zero mean and are generally much larger and more volatile than those for headline growth. In addition, revisions for most sectors exhibit predictability. As a result, the monthly signal from the sector data should be considered more cautiously than should the headline growth series.

Suggested Citation

  • Patrick Rizzetto, 2018. "GDP by Industry in Real Time: Are Revisions Well Behaved?," Staff Analytical Notes 2018-40, Bank of Canada.
    • Handle: RePEc:bca:bocsan:18-40
    DOI: 10.34989/san-2018-40
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    References listed on IDEAS

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    2. Lamprou, Dimitra, 2016. "Nowcasting GDP in Greece: The impact of data revisions and forecast origin on model selection and performance," The Journal of Economic Asymmetries, Elsevier, vol. 14(PA), pages 93-102.
    3. André Binette & Jae Chang, 2013. "CSI: A Model for Tracking Short-Term Growth in Canadian Real GDP," Bank of Canada Review, Bank of Canada, vol. 2013(Summer), pages 3-12.
    4. Tony Chernis & Rodrigo Sekkel, 2017. "A dynamic factor model for nowcasting Canadian GDP growth," Empirical Economics, Springer, vol. 53(1), pages 217-234, August.
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    Cited by:

    1. Tony Chernis & Taylor Webley, 2022. "Nowcasting Canadian GDP with Density Combinations," Discussion Papers 2022-12, Bank of Canada.

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts

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