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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. Ce texte examine la capacité de différentes variables financières et macroéconomiques à prédire les récessions canadiennes On examine quatre spécifications du modèle probit –, le modèle dynamique avancé, le modèle autorégressif, le modèle dynamique autorégressif, ainsi que le modèle statique conventionnel. Les résultats empiriques soulignent plusieurs prédicteurs significatifs – notamment l’étalement des taux de rendement des obligations gouvernementales, les taux de croissance des mises en chantier des maisons, l’offre de monnaie réelle, et l’indice composite avancé. Les résultats à la fois pour des estimations basées sur l’échantillon ou hors‐échantillon suggèrent que la performance prévisionnelle des quatre modèles probit n’est pas toujours cohérente. Les modèles dynamique et dynamique autorégressif performent mieux dans la prévision de la durée des récessions, alors que les modèles statique et autorégressif font un meilleur travail dans la prévision des sommets des cycles d’affaires. Les modèles dynamique avancé et conventionnel se complètent en ce qu’ils fournissent des données plus précises sur la durée et et les points tournants des cycles d’affaires.

Suggested Citation

  • Lili Hao & Eric C.Y. Ng, 2011. "Predicting Canadian recessions using dynamic probit modelling approaches," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 44(4), pages 1297-1330, November.
    • Handle: RePEc:wly:canjec:v:44:y:2011:i:4:p:1297-1330
    DOI: 10.1111/j.1540-5982.2011.01675.x
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    References listed on IDEAS

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    Cited by:

    1. Rachidi Kotchoni & Dalibor Stevanovic, 2016. "Forecasting U.S. Recessions and Economic Activity," Working Papers hal-04141569, HAL.
    2. MeiChi Huang, 2019. "A Nationwide or Localized Housing Crisis? Evidence from Structural Instability in US Housing Price and Volume Cycles," Computational Economics, Springer;Society for Computational Economics, vol. 53(4), pages 1547-1563, April.
    3. Rachidi Kotchoni & Dalibor Stevanovic, 2020. "GDP Forecast Accuracy During Recessions," Working Papers 20-06, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    4. Mustapha Olalekan Ojo & Luís Aguiar-Conraria & Maria Joana Soares, 2020. "A time–frequency analysis of the Canadian macroeconomy and the yield curve," Empirical Economics, Springer, vol. 58(5), pages 2333-2351, May.
    5. Kajal Lahiri & Liu Yang, 2023. "Predicting binary outcomes based on the pair-copula construction," Empirical Economics, Springer, vol. 64(6), pages 3089-3119, June.
    6. Jean-Marie Dufour & Joachim Wilde, 2018. "Weak identification in probit models with endogenous covariates," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(4), pages 611-631, October.
    7. Boukhatem, Jamel & Sekouhi, Hayfa, 2017. "What does the bond yield curve tell us about Tunisian economic activity?," Research in International Business and Finance, Elsevier, vol. 42(C), pages 295-303.
    8. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    9. Alexandre Bonnet R. Costa & Pedro Cavalcanti G. Ferreira & Wagner Piazza Gaglianone & Osmani Teixeira C. Guillén & João Victor Issler & Artur Brasil Fialho Rodrigues, 2023. "Predicting Recessions in (almost) Real Time in a Big-data Setting," Working Papers Series 587, Central Bank of Brazil, Research Department.
    10. Huiwen Lai & Eric C. Y. Ng, 2020. "On business cycle forecasting," Frontiers of Business Research in China, Springer, vol. 14(1), pages 1-26, December.

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