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Predicting Canadian recessions using dynamic probit modelling approaches

Author

Listed:
  • Lili Hao
  • Eric C.Y. Ng

Abstract

This paper examines the ability of various financial and macroeconomic variables to forecast Canadian recessions. It evaluates four model specifications, including the advanced dynamic, autoregressive, dynamic autoregressive probit models as well as the conventional static probit model. The empirical results highlight several significant recession predictors, notably the government bond yield spread, growth rates of the housing starts, the real money supply and the composite index of leading indicators. Both the in-sample and out-of-sample results suggest that the forecasting performance of the four probit models is mixed. The dynamic and dynamic autoregressive probit models are better in predicting the duration of recessions while the static and autoregressive probit models are better in forecasting the peaks of business cycles. Hence, the advanced dynamic models and the conventional static probit model can complement one another to provide more accurate forecasts for the duration and turning points of business cycles.

Suggested Citation

  • Lili Hao & Eric C.Y. Ng, 2011. "Predicting Canadian recessions using dynamic probit modelling approaches," Canadian Journal of Economics, Canadian Economics Association, vol. 44(4), pages 1297-1330, November.
  • Handle: RePEc:cje:issued:v:44:y:2011:i:4:p:1297-1330
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    Citations

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    Cited by:

    1. Proaño, Christian R. & Theobald, Thomas, 2014. "Predicting recessions with a composite real-time dynamic probit model," International Journal of Forecasting, Elsevier, vol. 30(4), pages 898-917.
    2. Jean-Marie Dufour & Joachim Wilde, 2013. "Weak Identification in Probit Models with Endogenous Covariates," Working Papers 95, Institute of Empirical Economic Research, Osnabrueck University, revised 28 Feb 2013.
    3. Rachidi Kotchoni & Dalibor Stevanovic, 2016. "Forecasting U.S. Recessions and Economic Activity," EconomiX Working Papers 2016-40, University of Paris Nanterre, EconomiX.
    4. repec:eee:riibaf:v:42:y:2017:i:c:p:295-303 is not listed on IDEAS
    5. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, Elsevier.
    6. Mustapha Olalekan Ojo & Luís Aguiar-Conraria & Maria Joana Soares, 2017. "A time-frequency analysis of the Canadian macroeconomy and the yield curve," NIPE Working Papers 12/2017, NIPE - Universidade do Minho.

    More about this item

    JEL classification:

    • 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

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