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Predicting Canadian recessions using dynamic probit modelling approaches

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  • 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.

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Bibliographic Info

Article provided by Canadian Economics Association in its journal Canadian Journal of Economics.

Volume (Year): 44 (2011)
Issue (Month): 4 (November)
Pages: 1297-1330

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Handle: RePEc:cje:issued:v:44:y:2011:i:4:p:1297-1330

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Cited by:
  1. Kajal Lahiri & Liu Yang, 2012. "Forecasting Binary Outcomes," Discussion Papers 12-09, University at Albany, SUNY, Department of Economics.
  2. Jean-Marie Dufour & Joachim Wilde, 2013. "Weak Identification in Probit Models with Endogenous Covariates," Working Papers 95, Institute of Empirical Economic Research, revised 28 Feb 2013.

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