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

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  • Khurshid M. KIANI
  • Terry L. KASTENS

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.

Suggested Citation

  • Khurshid M. KIANI & Terry L. KASTENS, 2006. "Using Macro-Financial Variables To Forecast Recessions. An Analysis Of Canada, 1957-2002," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 6(3).
    • Handle: RePEc:eaa:aeinde:v:6:y:2006:i:3_7
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    Cited by:

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    2. Khurshid M. Kiani, 2009. "Asymmetries in Macroeconomic Time Series in Eleven Asian Economies," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 8(1), pages 37-54, April.

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    More about this item

    Keywords

    business cycles; neural networks; out-of-sample forecasts; recession; real GDP;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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