IDEAS home Printed from
MyIDEAS: Log in (now much improved!) to save this paper

Measuring U.S. Business Cycles: A Comparison of Two Methods and Two Indicators of Economic Activities

Listed author(s):
  • Francis W. Ahking

    (University of Connecticut)

In this paper, we examine two issues concerning business cycle research. First, a number of studies have demonstrated that more complicated non-linear models do not replicate business cycle features better than simpler linear models. In Harding and Pagan (2002), they showed that a random walk with drift model of real GDP for the U.S., U.K., and Australia can capture the main business cycle features of the respective countries quite well. Adding non-linear structure, such as Hamilton’s (1989) Markov-switching model produced cycles that are too extreme, especially with respect to the cumulative movements of the cycles, where cumulative movements are a measure of cumulated output losses from peak to trough of a business cycle. Furthermore, Harding and Pagan (2003a) argued that based on criteria, such as simplicity, transparency, robustness, and replicability, the non-parametric Bry and Boschan algorithm (1971) is in fact superior to the Markov-switching model in determining turning points in business cycles. Similarly, Hess and Iwata (1997) showed that a non-linear models such as the Markov-switching models are no better than a simple ARIMA(1,1,0) model in replicating business cycle features. We start by comparing how well the Hamilton’s Markov-switching model and the Bry and Boschan algorithm can replicate the U.S. business cycle features. One interesting finding that has not been shown before is that we are unable to replicate Hamilton’s original result for the same sample period using real GDP rather than real GNP as Hamilton did. Furthermore, we also found that Hamilton’s Markov-switching model is not robust with respect to different sample periods. The Bry and Boschan algorithm, on the other hand, replicated business cycle features consistently. Second, Burns and Mitchell (1946) and NBER’s Business Cycle Dating Committee suggested that a variety of time series representing economic activities should be used for the purpose of dating business cycle. Nevertheless, real GDP is by far the most popular and frequently used single series to represent aggregate economic activities in business cycle research. We compared the ability of the U.S. real GDP and a coincident index published by the Federal Reserve Bank of Philadelphia in replicating features of the U.S. business cycle. We found that a constructed quarterly version of the coincident index is slightly preferred over the real GDP, suggesting that the coincident index may be a better indicator than the commonly used real GDP as an overall indicator of U.S. economic activities.

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:
File Function: Full text
Download Restriction: no

Paper provided by University of Connecticut, Department of Economics in its series Working papers with number 2013-10.

in new window

Length: 26 pages
Date of creation: May 2013
Handle: RePEc:uct:uconnp:2013-10
Contact details of provider: Postal:
University of Connecticut 365 Fairfield Way, Unit 1063 Storrs, CT 06269-1063

Phone: (860) 486-4889
Fax: (860) 486-4463
Web page:

More information through EDIRC

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.:

in new window

  1. Harding, Don & Pagan, Adrian, 2003. "Rejoinder to James Hamilton," Journal of Economic Dynamics and Control, Elsevier, vol. 27(9), pages 1695-1698, July.
  2. Beate Schirwitz, 2009. "A comprehensive German business cycle chronology," Empirical Economics, Springer, vol. 37(2), pages 287-301, October.
  3. Harding, Don & Pagan, Adrian, 2003. "A comparison of two business cycle dating methods," Journal of Economic Dynamics and Control, Elsevier, vol. 27(9), pages 1681-1690, July.
  4. Hess, Gregory D & Iwata, Shigeru, 1997. "Measuring and Comparing Business-Cycle Features," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(4), pages 432-444, October.
  5. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1.
  6. 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.
  7. Proietti, Tommaso, 2005. "New algorithms for dating the business cycle," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 477-498, April.
  8. Harding, Don & Pagan, Adrian, 2002. "Dissecting the cycle: a methodological investigation," Journal of Monetary Economics, Elsevier, vol. 49(2), pages 365-381, March.
  9. Boldin, Michael D, 1994. "Dating Turning Points in the Business Cycle," The Journal of Business, University of Chicago Press, vol. 67(1), pages 97-131, January.
  10. Michael Massmann & James Mitchell & Martin Weale, 2003. "Business Cycles and Turning Points: A Survey of Statistical Techniques," National Institute Economic Review, National Institute of Economic and Social Research, vol. 183(1), pages 90-106, January.
  11. Hamilton, James D., 2003. "Comment on "A comparison of two business cycle dating methods"," Journal of Economic Dynamics and Control, Elsevier, vol. 27(9), pages 1691-1693, July.
  12. James H. Stock & Mark W. Watson, 1989. "New Indexes of Coincident and Leading Economic Indicators," NBER Chapters,in: NBER Macroeconomics Annual 1989, Volume 4, pages 351-409 National Bureau of Economic Research, Inc.
  13. Theodore M. Crone & Alan Clayton-Matthews, 2005. "Consistent Economic Indexes for the 50 States," The Review of Economics and Statistics, MIT Press, vol. 87(4), pages 593-603, November.
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:uct:uconnp:2013-10. 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: (Mark McConnel)

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.