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Common Trends and Common Cycles in Canadian Sectoral Output

Author

Listed:
  • Francisco Barillas
  • Christoph Schleicher
Abstract
The authors examine evidence of long- and short-run co-movement in Canadian sectoral output data. Their framework builds on a vector-error-correction representation that allows them to test for and compute full-information maximum-likelihood estimates of models with codependent cycle restrictions. They find that the seven sectors under consideration contain five common trends and five codependent cycles and use their estimates to obtain a multivariate Beveridge- Nelson decomposition to isolate and compare the common components. A forecast error variance decomposition indicates that some sectors, such as manufacturing and construction, are subject to persistent transitory shocks, whereas other sectors, such as financial services, are not. The authors also find that imposing common feature restrictions leads to a non-trivial gain in the ability to forecast both aggregate and sectoral output. Among the main conclusions is that manufacturing, construction, and the primary sector are the most important sources of business cycle fluctuations for the Canadian economy.

Suggested Citation

  • Francisco Barillas & Christoph Schleicher, 2003. "Common Trends and Common Cycles in Canadian Sectoral Output," Staff Working Papers 03-44, Bank of Canada.
    • Handle: RePEc:bca:bocawp:03-44
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    References listed on IDEAS

    as
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    Cited by:

    1. Elizabeth Wakerly & Byron Scott & James Nason, 2006. "Common trends and common cycles in Canada: who knew so much has been going on?," Canadian Journal of Economics, Canadian Economics Association, vol. 39(1), pages 320-347, February.
    2. Cesar R. Sobrino, 2021. "Analyzing the Role of the Permanent and Temporary Shocks in Peru Using the Co-Movements Approach," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 13(11), pages 111-111, November.
    3. Christoph Schleicher, 2007. "Codependence in cointegrated autoregressive models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(1), pages 137-159.
    4. de Silva, Ashton & Hyndman, Rob J. & Snyder, Ralph, 2009. "A multivariate innovations state space Beveridge-Nelson decomposition," Economic Modelling, Elsevier, vol. 26(5), pages 1067-1074, September.

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    More about this item

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • 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
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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