Using Macro-Financial Variables To Forecast Recessions. An Analysis Of Canada, 1957-2002

Khurshid Kiani and Terry L. Kastens ()

Applied Econometrics and International Development, 2006, vol. 6, issue 3

Abstract: We employ artificial neural networks using macro-financial variables to predict recessions. We model the relationship between indicator variables and recessions to periods into the future and employ a procedure that penalizes a misclassified recession more than a misclassified non-recession. Our results reveal that among 16 models that we constructed from indicator variables and their combinations, the indicator variables Spread, -year bond rates, -year bond rates, monetary base, industrial production are candidate variables for predicting recessions ranging to periods in the future. However, most indicator variables become candidate for predicting recessions when misclassified recessions are penalized heavily than misclassified non-recessions.

Keywords: business cycles; neural networks; out-of-sample forecasts; recession; real GDP (search for similar items in EconPapers)
JEL-codes: C22 C32 (search for similar items in EconPapers)
Date: 2006
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Citations: View citations in EconPapers (2)

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