Skip to main content

Economic Cycles: Asymmetries, Persistence, and Synchronization

  • Chapter
Palgrave Handbook of Econometrics

Abstract

Marking upswings and downswings for a time series {y t } provides insights that are not immediately obvious, but may be meaningful to academics, policy makers, and the general public. The mean and standard deviation of durations, as well as the amplitude and steepness of a given phase, yield fruitful insights about cycle asymmetries and persistence. Expansions and contractions in one series can then be compared to those in another to determine whether their respective cycles are synchronized. However, our primary focus here is on classical nonparametric methods for the analysis of duration, dating back to the seminal work of Burns and Mitchell (1946), Cutler and Ederer (1958), Bry and Boschan (1971), and Cox (1972). Although our specific application is to unemployment cycles, the ideas and techniques discussed in this chapter apply to a wide variety of micro- and macroeconometric studies.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Adamchik, V. (1999) The effect of unemployment benefits on the probability of re-employment in Poland. Oxford Bulletin of Economics and Statistics 61, 95–108.

    Article  Google Scholar 

  • Allison, P.D. (1984) Event History Analysis: Regression for Longitudinal Event Data. London: Sage Publications, Inc.

    Google Scholar 

  • Artis, M.J., Z.G. Kontolemis and D.R. Osborn (1997) Business cycles for G7 and European countries. Journal of Business 70, 249–79.

    Article  Google Scholar 

  • Artis, M.J., H.M. Krolzig and J. Toro (2004) The European business cycle. Oxford Economic Papers 56, 1–44.

    Article  Google Scholar 

  • Artis, M.J., M. Marcellino, and T. Proietti (2004) Dating business cycles: a methodological contribution with an application to the Euro area. Oxford Bulletin of Economics and Statistics 66, 537–65.

    Article  Google Scholar 

  • Bennett, D.S. (1999) Parametric models, duration dependence, and time-varying data revisited. American Journal of Political Science 43, 256–70.

    Article  Google Scholar 

  • Beveridge S. and C.R. Nelson (1981) A new approach to the decomposition of economic time series into permanent and transitory components with particular attention to measurement of the “Business Cycle.” Journal of Monetary Economics 7, 151–74.

    Article  Google Scholar 

  • Boldin, M.D. (1994) Dating turning points in the business cycle. Journal of Business 67, 97–131.

    Article  Google Scholar 

  • Bonanomi, L., P.A. Gaughan and L.W. Taylor (1998) A statistical methodology for measuring lost profits resulting from a loss of customers. Journal of Forensic Economics 11, 103–13.

    Article  Google Scholar 

  • Boschan, C. and W.W. Ebanks (1978) The phase-average trend: a new way of measuring growth. In: 1978 Proceedings of the Business and Economic Statistics Section. Washington, DC: American Statistical Association.

    Google Scholar 

  • Bover, O., M. Arellano and S. Bentolila (2002) Unemployment duration, benefit duration, and the business cycle. Economic Journal 112, 223–65.

    Article  Google Scholar 

  • Bry, G. and C. Boschan (1971) Cyclical Analysis of Times Series: Selected Procedures and Computer Programs. New York: National Bureau of Economic Research.

    Google Scholar 

  • Burns, A.F. and W.C. Mitchell (1946) Measuring Business Cycles. New York: National Bureau of Economic Research.

    Google Scholar 

  • Cameron, A.C. and P.K. Trivedi (2005) Microeconometrics: Methods and Applications. New York: Cambridge University Press.

    Book  Google Scholar 

  • Cashin, P. and C.J. McDermott (2002) Riding on the sheep’s back: examining Australia’s dependence on wool exports Economic Record 78, 249–63.

    Article  Google Scholar 

  • Cashin, P., C.J. McDermott and A. Scott (2002) Booms and slumps in world commodity prices. Journal of Development Economics 69, 277–96.

    Article  Google Scholar 

  • Chin, D., J. Geweke and P. Miller (2000) Predicting Turning Points. Washington, DC: Technical Paper Series, Congressional Budget Office.

    Google Scholar 

  • Cooley, T.F. and E.C. Prescott (1995) Economic growth and business cycles. In T.F. Cooley (ed.), Frontiers of Business Cycle Research, pp. 1–38. Princeton: Princeton University Press.

    Google Scholar 

  • Cox, D.R. (1972) Regression models and life tables. Journal of the Royal Statistical Society, Series B 34, 187–202.

    Google Scholar 

  • Cutler, S. and F. Ederer (1958) Maximum utilization of the life table in analyzing survival. Journal of Chronic Disorders 8, 699–712.

    Article  Google Scholar 

  • Davidson, R. and J.G. MacKinnon (2006) Bootstrap methods in econometrics. In T.C. Mills and K. Patterson (eds.), Palgrave Handbook of Econometrics, Volume 1: Econometric Theory, pp. 812–38. New York: Palgrave Macmillan.

    Google Scholar 

  • Diebold, F.X., J.H. Lee and G.C. Weinbach (1994) Regime switching with time-varying transition probabilities. In C. Hargreaves (ed.), Nonstationary Time Series Analysis and Cointegration, pp. 283–302. Oxford: Oxford University Press.

    Google Scholar 

  • Diebold, F.X. and G.D. Rudebusch (1990) A nonparametric investigation of duration dependence in the American business cycle. Journal of Political Economy 98, 596–616.

    Article  Google Scholar 

  • Diebold, F.X. and G.D. Rudebusch (1991) Turning point prediction with the composite leading index: A real-time analysis. In K. Lahiri and G.H. Moore (eds.), Leading Economic Indicators: New Approaches and Forecasting Records, pp. 231–56. Cambridge: Cambridge University Press.

    Chapter  Google Scholar 

  • Diebold, F.X., G.D. Rudebusch and D.E. Sichel (1993) Further evidence on business cycle duration dependence. In J.H. Stock and M.W. Watson (eds.), Business Cycles, Indicators, and Forecasting, pp. 87–116. Chicago: University of Chicago Press for NBER.

    Google Scholar 

  • Durland, J.M and T.H. McCurdy (1994) Duration-dependent transitions in a Markov model of US GNP growth. Journal of Business and Economic Statistics 12, 279–88.

    Google Scholar 

  • Edwards, S., J.G. Biscarri and F.P. de Gracia (2003) Stock market cycles, financial liberalization and volatility. Journal of International Money and Finance 22, 925–55.

    Article  Google Scholar 

  • Efron, B. (1977) The efficiency of Cox’s likelihood function for censored data. Journal of the American Statistical Association 72, 557–65.

    Article  Google Scholar 

  • Eichengreen, B., A.K. Rose and C. Wyplosz (1995) Exchange rate mayhem: the antecedents and the aftermath of speculative attacks. Economic Policy 21, 251–312.

    Google Scholar 

  • Estrella, A. and F.S. Mishkin (1998) Predicting U.S. recessions: financial variables as leading indicators. Review of Economics and Statistics 80, 45–61.

    Article  Google Scholar 

  • Filardo, A.J. (1994) Business-cycle phases and their transitional dynamics. Journal of Business and Economic Statistics 12, 299–308.

    Google Scholar 

  • Gordin, M.I. (1969) The Central Limit Theorem for stationary processes. Soviet Math. Dokl. 10, 1174–76.

    Google Scholar 

  • Greene, W. (2006) Censored data and truncated distributions. In T.C. Mills and K. Patterson (eds.), Palgrave Handbook of Econometrics, Volume I: Econometric Theory, pp. 695–734. New York: Palgrave Macmillan.

    Google Scholar 

  • Hamilton, J.D. (1989) A new approach to the economic analysis of non-stationary time series and the business cycle. Econometrica 57, 357–84.

    Article  Google Scholar 

  • Hamilton, J.D. (1994) Time Series Analysis. Princeton: Princeton University Press.

    Google Scholar 

  • Hamilton, J.D. (2005) What’s real about the business cycle? Working Paper 11161. Cambridge, Mass.: National Bureau of Economic Research.

    Book  Google Scholar 

  • Hannan, E.J. (1973) Central limit theorems for time series regression. Z.Wahrsch. Verw. Gebiete 26, 157–70.

    Article  Google Scholar 

  • Harding, D. and A.R. Pagan (2000) Knowing the cycle. In R. Backhouse and A. Salanti (eds.), Macroeconomics in the Real World. Oxford: Oxford University Press.

    Google Scholar 

  • Harding, D. and A.R. Pagan (2002) Dissecting the cycle: Amethodological approach. Journal of Monetary Economics 49,365–81.

    Article  Google Scholar 

  • Harding, D. and A.R. Pagan (2003) A comparison of two business cycle dating methods. Journal of Economic Dynamics and Control 27, 1681–90.

    Article  Google Scholar 

  • Harding, D. and A.R. Pagan (2005) A suggested framework for classifying modes of cycle research. Journal of Applied Econometrics 20, 151–59.

    Article  Google Scholar 

  • Harding, D. and A.R. Pagan (2006) Synchronization of cycles. Journal of Econometrics 132, 59–79.

    Article  Google Scholar 

  • Harding, D. and A.R. Pagan (2007) The econometric analysis of some constructed binary time series. Working Paper. Brisbane, Australia: National Centre for Econometric Research.

    Google Scholar 

  • Harding, D. and A.R. Pagan (2008) Measuring business cycles. Forthcoming in S. Durlauf and L. Blume (eds.), The New Palgrave Dictionary of Economics (second edition).

    Google Scholar 

  • Harvey, A.C. (1989) Forecasting, Structural Time Series Models and the Kalman Filter. New York: Cambridge University Press.

    Google Scholar 

  • Heckman, J. and B. Singer (1984) A method for minimizing the distributional assumptions in econometric models for duration data. Econometrica 52, 271–320.

    Article  Google Scholar 

  • Hodrick, R.J. and E.C. Prescott (1997) Postwar U.S. business cycles: an empirical investigation. Journal of Money, Credit and Banking 29, 1–16.

    Article  Google Scholar 

  • Hoel, P.G. (1954) Introduction to Mathematical Statistics. New York: John Wiley and Sons.

    Google Scholar 

  • Hollander, M. and R. Proschan (1975) Tests for the residual life. Biometrika 62, 585–93.

    Article  Google Scholar 

  • Hollander, M. and D.A. Wolfe (1999) Life distributions and survival analysis. In M. Hollander and D.A. Wolfe, Nonparametric Statistical Methods, pp. 495–765. New York: John Wiley and Sons.

    Google Scholar 

  • Hoover, K.D. (2006) The methodology of econometrics. In T.C. Mills and K. Patterson (eds.), Palgrave Handbook of Econometrics Volume 1: Econometric Theory, pp. 61–87. New York: Palgrave Macmillan.

    Google Scholar 

  • Horowitz, J. (2001) The bootstrap in econometrics. In J.J. Heckman and E.E. Leamer (eds.), Handbook of Econometrics, Volume, pp. 3159–228. Amsterdam: Elsevier Science B.V.

    Google Scholar 

  • Ibbotson, R.G, J.L. Sindelar and J.R. Ritter (1994) The market’s problems with the pricing of initial paper. Journal of Applied Corporate Finance 7, 66–74.

    Article  Google Scholar 

  • Jensen, M.J. and M. Liu (2006) Do long swings in the business cycle lead to strong persistence in output? Journal of Monetary Economics, 53, 597–611.

    Article  Google Scholar 

  • Kedem, B. (1980) Binary Time Series. New York: Marcel Dekker.

    Google Scholar 

  • King, R.G. and C.I. Plosser (1994) Real business cycles and the test of the Adelmans. Journal of Monetary Economics 33, 405–38.

    Article  Google Scholar 

  • King, R.G. and S.T. Rebelo (1993) Low frequency filtering and real business cycles. Journal of Economic Dynamics and Control 17, 207–31.

    Article  Google Scholar 

  • LIMDEP (1995) Version 7.0, Econometric Software, Inc.

    Google Scholar 

  • Lunde, A. and A. Timmermann (2004) Duration dependence in stock prices: an analysis of bull and bear markets. Journal of Economics and Business Statistics 22, 253–73.

    Article  Google Scholar 

  • Macheu, J. and T. McCurdy (2000) Identifying bull and bear markets. Journal of Business and Economic Statistics 18, 100–12.

    Google Scholar 

  • Mills, T.C. (2001) Business cycle asymmetry and duration dependence: an international perspective. Journal of Applied Statistics 28, 713–24.

    Article  Google Scholar 

  • Mudambi, R. and L.W. Taylor (1991) A nonparametric investigation of duration dependence in the American business cycle: a note. Journal of Political Economy 99, 654–56.

    Article  Google Scholar 

  • Mudambi, R. and L.W. Taylor (1995) Some nonparametric tests for duration dependence: an application to UK business cycle data. Journal of Applied Statistics 22, 163–77.

    Article  Google Scholar 

  • Mudholkar, G.S., D.K. Srivastava and G.D. Kollia (1996) A generalization of the Weibull distribution with application to the analysis of survival data. Journal of the American Statistical Association 91, 1575–83.

    Article  Google Scholar 

  • Neftci, S.N. (1984) Are economic time series asymmetric over the business cycle? Journal of Political Economy 92, 307–28.

    Article  Google Scholar 

  • Ohn, J., L.W. Taylor and A.R. Pagan (2004) Testing for duration dependence in economic cycles. Econometrics Journal 7, 528–49.

    Article  Google Scholar 

  • Pagan, A.R. (1997) Policy, theory and the cycle. Oxford Review of Economic Policy 13, 19–33.

    Article  Google Scholar 

  • Pagan, A.R. (1998) Bulls and bears: a tale of two states. Walras-Bowley Lecture. Montreal: Econometric Society.

    Google Scholar 

  • Pagan, A.R. (2004) Some econometric analysis of constructed time series. Invited paper. Toronto: Canadian Econometric Group Meeting.

    Google Scholar 

  • Pagan, A.R. and K. Sossounov (2003) A simple framework for analysing bull and bear markets. Journal of Applied Econometrics 18, 23–46.

    Article  Google Scholar 

  • Pesaran, M.H. and S. Potter (1997) A floor and ceiling model of U.S. output. Journal of Economic Dynamics and Control 21, pp. 661–95.

    Article  Google Scholar 

  • Pesaran, M.H. and A. Timmermann (1992) A simple nonparametric test of predictive performance. Journal of Business and Economic Statistics 10, 461–5.

    Google Scholar 

  • Rotemberg, J.J. (1999) A heuristic method for extracting smooth trends from economic time series. Working Paper 7439. New York: National Bureau of Economic Research.

    Book  Google Scholar 

  • Shapiro, S.S. and M.B. Wilk (1972) An analysis of variance test for the exponential distribution (complete samples). Technometrics 14, 335–70.

    Article  Google Scholar 

  • Sichel, D.E. (1991) Business cycle duration dependence: a nonparametric approach. Review of Economics and Statistics 73, 254–60.

    Article  Google Scholar 

  • Sichel, D.E. (1994) Inventories and the three phases of the business cycle. Journal of Business and Economic Statistics 12, 269–77.

    Google Scholar 

  • Spanos, A. (1995) On theory testing in econometrics: modelling with nonexperimental data. Journal of Econometrics 67, 189–226.

    Article  Google Scholar 

  • Spanos, A. (2006) Econometrics in retrospect and prospect. In T.C. Mills and K. Patterson (eds.), Palgrave Handbook of Econometrics, Volume 1: Econometric Theory, pp. 3–58. New York: Palgrave Macmillan.

    Google Scholar 

  • Stock, J.H. and M.W. Watson (1989) New indexes of leading and coincidental economic indicators. In O. Blanchard and S. Fisher (eds.), NBER Macroeconomics Annual — 1989, pp. 351–94. Cambridge, Mass.: MIT Press.

    Google Scholar 

  • Stock, J.H. and M.W. Watson (1991) A probability model of coincident economic indicators. In K Lahiri and G.H. Moore (eds.), Leading Economic Indicators: New Approaches and Forecasting Records. Cambridge: Cambridge University Press.

    Google Scholar 

  • Stock, J.H. and M.W. Watson (2003) Has the business cycle changed? Evidence and explanations. Jackson Hole, Wyoming, Federal Reserve Bank of Kansas City symposium, “Monetary Policy and Uncertainty,” August, pp. 28–30.

    Google Scholar 

  • Taylor, L.W. (2007) Estimating duration dependence in militarized interstate disputes. Journal of Applied Statistics 34, pp. 423–41.

    Article  Google Scholar 

  • Teräsvirta, T. (2006) Univariate nonlinear time series models. In T.C. Mills and K. Patterson (eds.), Palgrave Handbook of Econometrics, Volume 1: Econometric Theory, pp. 396–424. New York: Palgrave Macmillan.

    Google Scholar 

  • Thompson, W.A., Jr. (1977) On the treatment of grouped observations in life studies. Biometrics 33, pp. 463–70.

    Article  Google Scholar 

  • Vahid, F. (2006) Common cycles. In T.C. Mills and K. Patterson (eds.), Palgrave Handbook of Econometrics, Volume 1: Econometric Theory, pp. 610–30. New York: Palgrave Macmillan.

    Google Scholar 

  • Watson, M.W. (1994) Business-cycle durations and postwar stabilization of the US Economy. American Economic Review 84, 24–46.

    Google Scholar 

  • White, H. (1984) Asymptotic Theory for Econometricians. New York: Academic Press, Inc.

    Google Scholar 

  • Zarnowitz, V. and A. Ozyildirim (2006) Time series decomposition and measurement of business cycles, trends and growth cycles, Journal of Monetary Economics 53, 1717–39.

    Article  Google Scholar 

  • Zorn, C.J. (2000) Modeling duration dependence. Political Analysis 8, 367–80.

    Article  Google Scholar 

  • Zuehlke, T.W. (2003) Business cycle duration dependence reconsidered. Journal of Business and Economic Statistics 21, 564–69.

    Article  Google Scholar 

Download references

Authors

Editor information

Editors and Affiliations

Copyright information

© 2009 Joe Cardinale and Larry W. Taylor

About this chapter

Cite this chapter

Cardinale, J., Taylor, L.W. (2009). Economic Cycles: Asymmetries, Persistence, and Synchronization. In: Mills, T.C., Patterson, K. (eds) Palgrave Handbook of Econometrics. Palgrave Macmillan, London. https://doi.org/10.1057/9780230244405_7

Download citation

Publish with us

Policies and ethics