Forecasting industrial production using models with business cycle asymmetry
This paper exploits an observed business cycle asymmetry, namely, a systematic shift in the dynamic relationship between output growth and an index for financial market conditions across expansionary and contractionary periods, to forecast monthly growth in industrial production. A bivariate model of monthly industrial production and the spread between the yield on 10-year Treasury notes and the federal funds rate is used as an example. This paper's method does not require a forecaster to make an exact exante determination of turning points in the output series being forecasted. A comparison of the forecast performance of various two-regime nonlinear and conventional linear models suggests that a measureable gain can be made by considering models which explicitly incorporate asymmetry in data.
Volume (Year): (1998)
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