Monitoring cyclical processes. A non-parametric approach
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 .
Volume (Year): 29 (2002)
Issue (Month): 7 ()
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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.
- Francis X. Diebold & Glenn D. Rudebusch, 1987.
"Scoring the leading indicators,"
Special Studies Papers
206, Board of Governors of the Federal Reserve System (U.S.).
- C R Birchenhall & D R Osborn & M Sensier, 2000.
"Predicting UK Business Cycle Regimes,"
Centre for Growth and Business Cycle Research Discussion Paper Series
02, Economics, The Univeristy of Manchester.
- 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.
- Chris Birchenhall & Marianne Sensier, 2000. "Predicting UK Business Cycle Regimes," Econometric Society World Congress 2000 Contributed Papers 0953, Econometric Society.
- 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.
- Arteaga, Carmen & Ledolter, Johannes, 1997. "Control charts based on order-restricted tests," Statistics & Probability Letters, Elsevier, vol. 32(1), pages 1-10, February.
- James H. Stock & Mark W. Watson, 1992.
"A Procedure for Predicting Recessions With Leading Indicators: Econometric Issues and Recent Experience,"
NBER Working Papers
4014, National Bureau of Economic Research, Inc.
- 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.
- 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.
- 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.
- Neftici, Salih N., 1982. "Optimal prediction of cyclical downturns," Journal of Economic Dynamics and Control, Elsevier, vol. 4(1), pages 225-241, November.
- 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.
- 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.
- 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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