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Forecasting industrial production using models with business cycle asymmetry

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  • Chan Guk Huh

Abstract

This paper exploits business cycle asymmetry observed in data, namely, a systematic shift in the dynamic relationship between the output and the interest rate spread across expansionary and contractionary periods in forecasting monthly industrial production. A bivariate model of monthly industrial production and the spread between the 6-month commercial paper and the federal funds rates is used as an example to illustrate forecast exercise. This paper's method does not require a forecaster to make an exact ex-ante determination of turning points in the output series which is being forecasted. Comparison of the forecast performance of various two-regime based and conventional models suggests that a measurable gain can be made by considering models which explicitly incorporate asymmetry in data.

Suggested Citation

  • Chan Guk Huh, 1993. "Forecasting industrial production using models with business cycle asymmetry," Working Papers in Applied Economic Theory 93-12, Federal Reserve Bank of San Francisco.
  • Handle: RePEc:fip:fedfap:93-12
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    Cited by:

    1. Diebold, Francis X & Rudebusch, Glenn D, 1996. "Measuring Business Cycles: A Modern Perspective," The Review of Economics and Statistics, MIT Press, vol. 78(1), pages 67-77, February.

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