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Forecasting Canadian GDP: Region-Specific versus Countrywide Information

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  • Frédérick Demers
  • David Dupuis

Abstract

The authors investigate whether the aggregation of region-specific forecasts improves upon the direct forecasting of Canadian GDP growth. They follow Marcellino, Stock, and Watson (2003) and use disaggregate information to predict aggregate GDP growth. An array of multivariate forecasting models are considered for five Canadian regions, and single-equation models are considered for direct forecasting of Canadian GDP. The authors focus on forecasts at 1-, 2-, 4-, and 8-quarter horizons, which best represent the monetary policy transmission framework of long and variable lags. Region-specific forecasts are aggregated to the country level and tested against aggregate country-level forecasts. The empirical results show that Canadian GDP growth forecasts can be improved by indirectly forecasting the GDP growth of the Canadian economic regions using a multivariate approach, namely a vector autoregression and moving average with exogenous regressors (VARMAX) model.

Suggested Citation

  • Frédérick Demers & David Dupuis, 2005. "Forecasting Canadian GDP: Region-Specific versus Countrywide Information," Staff Working Papers 05-31, Bank of Canada.
    • Handle: RePEc:bca:bocawp:05-31
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    References listed on IDEAS

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    1. Frédérick Demers & Annie De Champlain, 2005. "Forecasting Core Inflation in Canada: Should We Forecast the Aggregate or the Components?," Staff Working Papers 05-44, Bank of Canada.
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    Cited by:

    1. Frédérick Demers & Annie De Champlain, 2005. "Forecasting Core Inflation in Canada: Should We Forecast the Aggregate or the Components?," Staff Working Papers 05-44, Bank of Canada.
    2. Chernis, Tony & Cheung, Calista & Velasco, Gabriella, 2020. "A three-frequency dynamic factor model for nowcasting Canadian provincial GDP growth," International Journal of Forecasting, Elsevier, vol. 36(3), pages 851-872.

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    Keywords

    Econometric and statistical methods;

    JEL classification:

    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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