IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Log in (now much improved!) to save this article

Some statistical aspects of methods for detection of turning points in business cycles

Listed author(s):
  • E. Andersson
  • D. Bock
  • M. Frisen

Methods for online turning point detection in business cycles are discussed. The statistical properties of three likelihood-based methods are compared. One is based on a Hidden Markov Model, another includes a non-parametric estimation procedure and the third combines features of the other two. The methods are illustrated by monitoring a period of the Swedish industrial production. Evaluation measures that reflect timeliness are used. The effects of smoothing, seasonal variation, autoregression and multivariate issues on methods for timely detection are discussed.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500445517
Download Restriction: Access to full text is restricted to subscribers.

As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

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

Volume (Year): 33 (2006)
Issue (Month): 3 ()
Pages: 257-278

as
in new window

Handle: RePEc:taf:japsta:v:33:y:2006:i:3:p:257-278
DOI: 10.1080/02664760500445517
Contact details of provider: Web page: http://www.tandfonline.com/CJAS20

Order Information: Web: http://www.tandfonline.com/pricing/journal/CJAS20

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

as
in new window


  1. Ghysels, Eric & Perron, Pierre, 1996. "The effect of linear filters on dynamic time series with structural change," Journal of Econometrics, Elsevier, vol. 70(1), pages 69-97, January.
  2. Filardo, Andrew J. & Gordon, Stephen F., 1998. "Business cycle durations," Journal of Econometrics, Elsevier, vol. 85(1), pages 99-123, July.
  3. Hamilton, James D & Perez-Quiros, Gabriel, 1996. "What Do the Leading Indicators Lead?," The Journal of Business, University of Chicago Press, vol. 69(1), pages 27-49, January.
  4. Canova, Fabio & Ciccarelli, Matteo, 2004. "Forecasting and turning point predictions in a Bayesian panel VAR model," Journal of Econometrics, Elsevier, vol. 120(2), pages 327-359, June.
  5. Franses, Philip Hans & Paap, Richard, 1999. "Does Seasonality Influence the Dating of Business Cycle Turning Points?," Journal of Macroeconomics, Elsevier, vol. 21(1), pages 79-92, January.
  6. Canova, Fabio, 1999. "Does Detrending Matter for the Determination of the Reference Cycle and the Selection of Turning Points?," Economic Journal, Royal Economic Society, vol. 109(452), pages 126-150, January.
  7. LeSage, James P, 1991. "Analysis and Development of Leading Indicators Using a Bayesian Turning-Points Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(3), pages 305-316, July.
  8. Zuehlke, Thomas W, 2003. "Business Cycle Duration Dependence Reconsidered," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(4), pages 564-569, October.
  9. Xia Pan & Jeffrey Jarrett, 2004. "Applying State Space to SPC: Monitoring Multivariate Time Series," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(4), pages 397-418.
  10. Birchenhall, Chris R & Osborn, Denise R & Sensier, Marianne, 2001. "Predicting UK Business Cycle Regimes," Scottish Journal of Political Economy, Scottish Economic Society, vol. 48(2), pages 179-195, May.
  11. Busetti, Fabio & Harvey, Andrew, 2003. "Seasonality Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(3), pages 420-436, July.
  12. Canova, Fabio, 1998. "Detrending and business cycle facts," Journal of Monetary Economics, Elsevier, vol. 41(3), pages 475-512, May.
  13. Zellner, Arnold & Hong, Chansik & Min, Chung-ki, 1991. "Forecasting turning points in international output growth rates using Bayesian exponentially weighted autoregression, time-varying parameter, and pooling techniques," Journal of Econometrics, Elsevier, vol. 49(1-2), pages 275-304.
  14. Sarlan, Haldun, 2001. "Cyclical aspects of business cycle turning points," International Journal of Forecasting, Elsevier, vol. 17(3), pages 369-382.
  15. Gordon, Stephen, 1997. "Stochastic Trends, Deterministic Trends, and Business Cycle Turning Points," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(4), pages 411-434, July-Aug..
  16. Francis X. Diebold & Joon-Haeng Lee & Gretchen C. Weinbach, 1993. "Regime switching with time-varying transition probabilities," Working Papers 93-12, Federal Reserve Bank of Philadelphia.
  17. 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 421-428, October.
  18. Chang-Jin Kim & Charles R. Nelson, 1998. "Business Cycle Turning Points, A New Coincident Index, And Tests Of Duration Dependence Based On A Dynamic Factor Model With Regime Switching," The Review of Economics and Statistics, MIT Press, vol. 80(2), pages 188-201, May.
  19. Kontolemis, Zenon G, 2001. "Analysis of the US Business Cycle with a Vector-Markov-Switching Model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(1), pages 47-61, January.
  20. Canova, Fabio & Hansen, Bruce E, 1995. "Are Seasonal Patterns Constant over Time? A Test for Seasonal Stability," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 237-252, July.
  21. Petros Maravelakis & John Panaretos & Stelios Psarakis, 2004. "EWMA Chart and Measurement Error," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(4), pages 445-455.
  22. Ivanova, Detelina & Lahiri, Kajal & Seitz, Franz, 2000. "Interest rate spreads as predictors of German inflation and business cycles," International Journal of Forecasting, Elsevier, vol. 16(1), pages 39-58.
  23. 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.
  24. Layton, Allan P. & Katsuura, Masaki, 2001. "Comparison of regime switching, probit and logit models in dating and forecasting US business cycles," International Journal of Forecasting, Elsevier, vol. 17(3), pages 403-417.
  25. Ram Mudambi, 1997. "Estimating turning points using polynomial regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 24(6), pages 723-732.
  26. Diebold, Francis X & Rudebusch, Glenn D, 1989. "Scoring the Leading Indicators," The Journal of Business, University of Chicago Press, vol. 62(3), pages 369-391, July.
  27. Layton, Allan P., 1998. "A further test of the influence of leading indicators on the probability of US business cycle phase shifts," International Journal of Forecasting, Elsevier, vol. 14(1), pages 63-70, March.
  28. 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.
  29. James H. Stock & Mark W. Watson, 1988. "A Probability Model of The Coincident Economic Indicators," NBER Working Papers 2772, National Bureau of Economic Research, Inc.
  30. 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.
  31. Delgado, Miguel A. & Hidalgo, Javier, 2000. "Nonparametric inference on structural breaks," Journal of Econometrics, Elsevier, vol. 96(1), pages 113-144, May.
  32. 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 357-384, March.
  33. Chu, Chia-Shang James & Stinchcombe, Maxwell & White, Halbert, 1996. "Monitoring Structural Change," Econometrica, Econometric Society, vol. 64(5), pages 1045-1065, September.
  34. A. A. Kalgonda & S. R. Kulkarni, 2004. "Multivariate Quality Control Chart for Autocorrelated Processes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(3), pages 317-327.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:taf:japsta:v:33:y:2006:i:3:p:257-278. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Michael McNulty)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.