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Forecasting Industrial Production Using Models with Business Cycle Asymmetry Chan

By Bharat Trehan


Moreno, and Mary Daly, as well as seminar participants for useful suggestions. I also would like to thank Judy Kim and Kelly Ragan for research assistance. 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 ex ante 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 measurable gain can be made by considering models which explicitly incorporate asymmetry in data. There has been ongoing interest in forecasting turning points of business cycle phases (Fels and Hinshaw 1968, Zarnowitz 1972, Zarnowitz and Moore 1982, Wecker 1979, Kling 1987). One method of detecting turning points which has received considerable attention is credited to Neftci (1982). This method uses changes in the Department of Commerce’s index of leading indicators, which exhibits different behavior over expansion and contraction periods, as a signal of imminent change in business cycle phases. The methodology has been extended further by Diebold and Rudebusch (1989, 1991). The basic idea behind their methodology has been further elaborated by many researcher

Year: 2011
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