Forecasting industrial production using models with business cycle asymmetry
AbstractThis 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.
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Bibliographic InfoPaper provided by Federal Reserve Bank of San Francisco in its series Working Papers in Applied Economic Theory with number 93-12.
Date of creation: 1993
Date of revision:
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- 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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