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

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Author Info
Khurshid M. KIANI ()
Terry L. KASTENS ()

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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.

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Publisher Info
Article provided by Euro-American Association of Economic Development in its journal Applied Econometrics and International Development.

Volume (Year): 6 (2006)
Issue (Month): 3 ()
Pages:
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Handle: RePEc:eaa:aeinde:v:6:y:2006:i:3_7

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Related research
Keywords: business cycles; neural networks; out-of-sample forecasts; recession; real GDP;

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Find related papers by JEL classification:
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions

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  17. Khurshid M. Kiani & Prasad V. Bidarkota & Terry L. Kastens, 2005. "Forecast performance of neural networks and business cycle asymmetries," Applied Financial Economics Letters, Taylor and Francis Journals, vol. 1(4), pages 205-210, July. [Downloadable!] (restricted)
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