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Using Monthly Indicators to Predict Quarterly GDP


  • Isabel Yi Zheng
  • James Rossiter


The authors build a model for predicting current-quarter real gross domestic product (GDP) growth using anywhere from zero to three months of indicators from that quarter. Their equation links quarterly Canadian GDP growth with monthly data on retail sales, housing starts, consumer confidence, total hours worked, and U.S. industrial production. The authors use time-series methods to forecast missing observations of the monthy indicators; this allows them to assess the performance of the method under various amounts of monthly information. The authors' model forecasts GDP growth as early as the first month of the reference quarter, and its accuracy generally improves with incremental monthly data releases. The final forecast from the model, available five to six weeks before the release of the National Income and Expenditure Accounts, delivers improved accuracy relative to those of several macroeconomic models used for short-term forecasting of Canadian output. The implications of real-time versus pseudo-real-time forecasting are investigated, and the authors find that the choice between real-time and latestavailable data affects the performance ranking among alternative models.

Suggested Citation

  • Isabel Yi Zheng & James Rossiter, 2006. "Using Monthly Indicators to Predict Quarterly GDP," Staff Working Papers 06-26, Bank of Canada.
  • Handle: RePEc:bca:bocawp:06-26

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    References listed on IDEAS

    1. Dean Croushore & Tom Stark, 2003. "A Real-Time Data Set for Macroeconomists: Does the Data Vintage Matter?," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 605-617, August.
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    Cited by:

    1. Antipa, Pamfili & Barhoumi, Karim & Brunhes-Lesage, Véronique & Darné, Olivier, 2012. "Nowcasting German GDP: A comparison of bridge and factor models," Journal of Policy Modeling, Elsevier, vol. 34(6), pages 864-878.
    2. Paulo Soares Esteves & António Rua, 2012. "Short-term forecasting for the portuguese economy: a methodological overview," Economic Bulletin and Financial Stability Report Articles, Banco de Portugal, Economics and Research Department.
    3. Esteves, Paulo Soares, 2013. "Direct vs bottom–up approach when forecasting GDP: Reconciling literature results with institutional practice," Economic Modelling, Elsevier, vol. 33(C), pages 416-420.
    4. Maxime Leboeuf & Louis Morel, 2014. "Forecasting Short-Term Real GDP Growth in the Euro Area and Japan Using Unrestricted MIDAS Regressions," Discussion Papers 14-3, Bank of Canada.
    5. Guerrero Víctor M. & García Andrea C. & Sainz Esperanza, 2013. "Rapid Estimates of Mexico’s Quarterly GDP," Journal of Official Statistics, De Gruyter Open, vol. 29(3), pages 397-423, June.
    6. Michael P. Clements & Ana Beatriz Galvao, 2009. "Forecasting US output growth using leading indicators: an appraisal using MIDAS models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(7), pages 1187-1206.
    7. Yun-Yeong Kim, 2016. "Dynamic Analyses Using VAR Model with Mixed Frequency Data through Observable Representation," Korean Economic Review, Korean Economic Association, vol. 32, pages 41-75.
    8. Michael P. Clements & Ana Beatriz Galvão, 2007. "Macroeconomic Forecasting with Mixed Frequency Data: Forecasting US Output Growth," Working Papers 616, Queen Mary University of London, School of Economics and Finance.
    9. Dimitra Lamprou, 2015. "Nowcasting GDP in Greece: A Note on Forecasting Improvements from the Use of Bridge Models," South-Eastern Europe Journal of Economics, Association of Economic Universities of South and Eastern Europe and the Black Sea Region, vol. 13(1), pages 85-100.
    10. Arora Siddharth & Little Max A. & McSharry Patrick E., 2013. "Nonlinear and nonparametric modeling approaches for probabilistic forecasting of the US gross national product," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(4), pages 395-420, September.
    11. Claudia Godbout & Jocelyn Jacob, 2010. "Le pouvoir de prévision des indices PMI," Discussion Papers 10-3, Bank of Canada.

    More about this item


    Economic models; Econometric and statistical methods;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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