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Forecasting industrial production using models with business cycle asymmetry

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  • Chan Huh
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    Abstract

    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.

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    Bibliographic Info

    Article provided by Federal Reserve Bank of San Francisco in its journal Economic Review.

    Volume (Year): (1998)
    Issue (Month): ()
    Pages: 29-41

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    Handle: RePEc:fip:fedfer:y:1998:p:29-41:n:1

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    Related research

    Keywords: Business cycles ; Industrial productivity ; Econometric models;

    References

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    1. Meese, R. & Rogoff, K., 1988. "Was It Real? The Exchange Rate-Interest Differential Ralation Over The Modern Floating-Rate Period," Working papers 368, Wisconsin Madison - Social Systems.
    2. Kling, John L, 1987. "Predicting the Turning Points of Business and Economic Time Series," The Journal of Business, University of Chicago Press, vol. 60(2), pages 201-38, April.
    3. Romer, Christina D., 1994. "Remeasuring Business Cycles," The Journal of Economic History, Cambridge University Press, vol. 54(03), pages 573-609, September.
    4. Friedman, Benjamin M & Kuttner, Kenneth N, 1992. "Money, Income, Prices, and Interest Rates," American Economic Review, American Economic Association, vol. 82(3), pages 472-92, June.
    5. Victor Zarnowitz, 1972. "Forecasting Economic Conditions: The Record And The Prospect," NBER Chapters, in: Economic Research: Retrospect and Prospect Vol 1: The Business Cycle Today, pages 183-240 National Bureau of Economic Research, Inc.
    6. Pindyck, Robert S, 1991. "Irreversibility, Uncertainty, and Investment," Journal of Economic Literature, American Economic Association, vol. 29(3), pages 1110-48, September.
    7. James H. Stock & Mark W. Watson, 1993. "A Procedure for Predicting Recessions with Leading Indicators: Econometric Issues and Recent Experience," NBER Chapters, in: Business Cycles, Indicators and Forecasting, pages 95-156 National Bureau of Economic Research, Inc.
    8. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March.
    9. Ben Bernanke, 1990. "On the Predictive Power of Interest Rates and Interest Rate Spreads," NBER Working Papers 3486, National Bureau of Economic Research, Inc.
    10. Michael D. Boldin, 1992. "Using switching models to study business cycle asymmetries: 1. overview of methodology and application," Research Paper 9211, Federal Reserve Bank of New York.
    11. Deutsch, Melinda & Granger, Clive W. J. & Terasvirta, Timo, 1994. "The combination of forecasts using changing weights," International Journal of Forecasting, Elsevier, vol. 10(1), pages 47-57, June.
    12. Potter, Simon M, 1995. "A Nonlinear Approach to US GNP," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(2), pages 109-25, April-Jun.
    13. French, Mark W & Sichel, Daniel E, 1993. "Cyclical Patterns in the Variance of Economic Activity," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(1), pages 113-19, January.
    14. Granger, Clive W. J. & Terasvirta, Timo, 1993. "Modelling Non-Linear Economic Relationships," OUP Catalogue, Oxford University Press, number 9780198773207.
    15. Ploberger, Werner & Kramer, Walter, 1992. "The CUSUM Test with OLS Residuals," Econometrica, Econometric Society, vol. 60(2), pages 271-85, March.
    16. Francis X. Diebold & Roberto S. Mariano, 1991. "Comparing predictive accuracy I: an asymptotic test," Discussion Paper / Institute for Empirical Macroeconomics 52, Federal Reserve Bank of Minneapolis.
    17. Sichel, D.E., 1988. "Business Cycle Asymmetry: A Deeper Look," Papers 85, Princeton, Department of Economics - Financial Research Center.
    18. Engle, Robert F. & Granger, C. W. J. & Kraft, Dennis, 1984. "Combining competing forecasts of inflation using a bivariate arch model," Journal of Economic Dynamics and Control, Elsevier, vol. 8(2), pages 151-165, November.
    19. Filardo, Andrew J, 1994. "Business-Cycle Phases and Their Transitional Dynamics," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 299-308, July.
    20. Chan G. Huh, 1994. "Asymmetry in the bivariate relationship between output and interest rates," Working Papers in Applied Economic Theory 94-13, Federal Reserve Bank of San Francisco.
    21. Rendigs Fels & C. Elton Hinshaw, 1968. "Forecasting and Recognizing Business Cycle Turning Points," NBER Books, National Bureau of Economic Research, Inc, number fels68-1.
    22. Neftci, Salih N, 1984. "Are Economic Time Series Asymmetric over the Business Cycle?," Journal of Political Economy, University of Chicago Press, vol. 92(2), pages 307-28, April.
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    Cited by:
    1. Bruno, Giancarlo & Lupi, Claudio, 2003. "Forecasting Euro-Area Industrial Production Using (Mostly) Business Surveys Data," MPRA Paper 42332, University Library of Munich, Germany.
    2. Jörg Döpke & Christian Pierdzioch, 2004. "Politics and the Stock Market � Evidence from Germany," Kiel Working Papers 1203, Kiel Institute for the World Economy.

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