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Monitoring cyclical processes. A non-parametric approach

  • E. Andersson
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    Forecasting the turning points in business cycles is important to economic and political decisions. Time series of business indicators often exhibit cycles that cannot easily be modelled with a parametric function. This article presents a method for monitoring time-series with cycles in order to detect the turning points. A non-parametric estimation procedure that uses only monotonicity restrictions is used. The methodology of statistical surveillance is used for developing a system for early warnings of cycle turning points in monthly data. In monitoring, the inference situation is one of repeated decisions. Measurements of the performance of a method of surveillance are, for example, average run length and expected delay to a correct alarm. The properties of the proposed monitoring system are evaluated by means of a simulation study. The false alarms are controlled by a fixed median run length to the first false alarm. Results are given on the median delay time to a correct alarm for two situations: a peak after two and three years respectively .

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    Article provided by Taylor & Francis Journals in its journal Journal of Applied Statistics.

    Volume (Year): 29 (2002)
    Issue (Month): 7 ()
    Pages: 973-990

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    Handle: RePEc:taf:japsta:v:29:y:2002:i:7:p:973-990
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    1. Arteaga, Carmen & Ledolter, Johannes, 1997. "Control charts based on order-restricted tests," Statistics & Probability Letters, Elsevier, vol. 32(1), pages 1-10, February.
    2. 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.
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    5. Zarnowitz, Victor & Moore, Geoffrey H, 1982. "Sequential Signals of Recession and Recovery," The Journal of Business, University of Chicago Press, vol. 55(1), pages 57-85, January.
    6. 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.
    7. Birchenhall, Chris R, et al, 1999. "Predicting U.S. Business-Cycle Regimes," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(3), pages 313-23, July.
    8. Neftici, Salih N., 1982. "Optimal prediction of cyclical downturns," Journal of Economic Dynamics and Control, Elsevier, vol. 4(1), pages 225-241, November.
    9. 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.
    10. Chris R. Birchenhall & Marianne Sensier & Denise R. Osborn, 2000. "Predicting Uk Business Cycle Regimes," Computing in Economics and Finance 2000 134, Society for Computational Economics.
    11. Layton, Allan P., 1996. "Dating and predicting phase changes in the U.S. business cycle," International Journal of Forecasting, Elsevier, vol. 12(3), pages 417-428, September.
    12. Li, David T & Dorfman, Jeffrey H, 1996. "Predicting Turning Points through the Integration of Multiple Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(4), pages 421-28, October.
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