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


  • 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.

Suggested Citation

  • Francis W. Ahking, 2013. "Measuring U.S. Business Cycles: A Comparison of Two Methods and Two Indicators of Economic Activities," Working papers 2013-10, University of Connecticut, Department of Economics.
  • Handle: RePEc:uct:uconnp:2013-10

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    References listed on IDEAS

    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, June.
    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.
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    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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