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A system for dating long wave phases in economic development

  • Marco GallegatiEmail author
Regular Article
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Abstract

Long wave chronologies are generally established by identifying phase periods associated with relatively higher and lower average growth rates in the world economy. However, the long recognition lag typical of the phase-growth approach prevents it from providing timely information about the present long wave phase period. In this paper, using world GDP growth rates data over the period 1871–2016, we develop a system for long wave phases dating, based on the systematic timing relationship between cyclical representations in growth rates and in levels. The proposed methodology allows an objective periodization of long waves which is much more timely than that based on the phase-growth approach. We find a striking concordance of the established long waves chronology with the dating chronologies elaborated by long wave scholars using the phase-growth approach, both in terms of the number of high- and low-growth phases of the world economy and their approximate time of occurrence. In terms of the current long wave debate, our findings suggest that the upswing phase of the current fifth long wave is still ongoing, and thus the recent financial/economic crisis only marks a flattening in the current upswing phase of the world economy.

Keywords

Long wave phases World economy Dating methodology Cyclical turning points 

JEL codes

B52 C14 E32 N10 O40 

Notes

Acknowledgements

I’d like to thank two anonymous referees that with their comments contributed to greatly improve the paper. All errors and responsibilities are, of course, mine.

Compliance with ethical standards

Conflict of interest

The author declares that he has no conflict of interest.

Supplementary material

191_2019_622_MOESM1_ESM.csv (25 kb)
ESM 1 (CSV 24 kb)

References

  1. Allianz Global Investors (2010) Analysis and trends: the sixth Kondratieff-long waves of prosperity. Allianz Global Investors, FrankfurtGoogle Scholar
  2. Anas J, Ferrara L (2004) Detecting cyclical. Turning Points: The ABCD Approach and Two Probabilistic Indicators. J Bus Cycle Meas Anal 12(2):193–225CrossRefGoogle Scholar
  3. Anas J, Billio M, Ferrara L, Mazzi GL (2008) A system for dating and detecting turning points in the euro area. Manch Sch 76(5):549–577CrossRefGoogle Scholar
  4. Baxter M (1994) Real exchange rates and real interest differentials. Have We Missed the Business-Cycle Relationship? J Mon Econ 33:5 37Google Scholar
  5. Baxter M, King R (1999) Measuring business Cycles: approximate band-pass Filters for economic time series. Rev Econ Stat 81:575–593CrossRefGoogle Scholar
  6. Becker R, Enders W, Lee J (2006) A stationary test with an unknown number of smooth breaks. J Time Ser Anal 27:381–409CrossRefGoogle Scholar
  7. Bernard L, Gevorkyan A, Palley T, Semmler W (2014) Time scales and mechanisms of economic cycles: a review of theories of long waves. Review of Keynesian Economics 2(1):87–107CrossRefGoogle Scholar
  8. Berry BJL (1991) Introduction in long wave rhythms in economic development and political behavior. John Hopkins University Press, BaltimoreGoogle Scholar
  9. Bolt J, Inklaar R, de Jong H, van Zanden JL (2018) Rebasing, Maddison: new income comparisons and the shape of long-run economic development. Maddison Project Working Paper n:10Google Scholar
  10. E Bosserelle (2012) La croissance economique dans le long terme: S. Kuznets versus N.D. Kondratiev - Actualité d'une controverse apparue dans l’entre-deux-guerres. Economies et Societes, Cahiers de l'ISMEA, serie Histoire economique quantitative, AF, 45 1655‑1688Google Scholar
  11. Chase-Dunn C, Grimes P (1995) World-systems analysis. Annu Rev Sociol 21:387 417CrossRefGoogle Scholar
  12. Christiano LJ, Fitzgerald TJ (2003) The band pass filter. Intern Econ Rev 44(2):435–465CrossRefGoogle Scholar
  13. Crowley P (2007) A guide to wavelets for economists. J Econ Surv 21:207–267CrossRefGoogle Scholar
  14. Daubechies I (1992) Ten lectures on wavelets, CBSM-NSF regional conference series in applied mathematics, vol 61. SIAM, PhiladelphiaGoogle Scholar
  15. Devezas TC, Corradine JT (2001) The biological determinants of long-wave behaviour in socioeconomic growth and development. Technol. Forecast. Soc. Change 68(1):57CrossRefGoogle Scholar
  16. Devezas TC (2010) Crises, depressions, and expansions: global analysis and secular trends. Technol Forecast Soc Change 77:739 761Google Scholar
  17. Dosi G (1982) Technological paradigms and technological trajectories. Res Policy 11:147 162CrossRefGoogle Scholar
  18. van Duijn JJ (1983) The long wave in economic life. Allen and Unwin, Boston, MAGoogle Scholar
  19. Enders W, Lee J (2009) A unit root test using a Fourier series to approximate smooth breaks. Oxf Bull Econ StatGoogle Scholar
  20. Erten B, Ocampo JA (2013) Super cycles of commodity prices since the mid nineteenth century. World Devel 44:14 30CrossRefGoogle Scholar
  21. Everts M, Filters B-P (2006) Munich Personal RePec Archive Paper no 2049Google Scholar
  22. van Ewijk C (1982) A spectral analysis of the Kondratieff cycle. Kyklos 35(3):468 499Google Scholar
  23. Freeman C (1983) Long waves in the world economy. Frances Pinter, LondonGoogle Scholar
  24. Freeman C (2009) Techno-economic paradigms. Essays in honour of Carlota Perez. In: Drechsler W, Kattel R, Reinert ES (eds) Schumpeter’s business Cycles and techno-economic paradigms. Anthem Press, LondonGoogle Scholar
  25. Freeman C, Perez C (1988) In: Dosi G, Freeman C, Nelson R, Silverberg G, Soete L (eds) Technical Change and Economic TheoryStructural crises of adjustment: business Cycles and investment behaviour. Pinter Publisher, LondonGoogle Scholar
  26. Freeman C, Louca F (2001) As time Goes by: from the industrial revolutions to the information revolution. Oxford University Press, OxfordGoogle Scholar
  27. Friedman M, Schwartz AJ (1963) A monetary history of the United States. NBER Publications. Princeton University Press, Princeton, pp 1867–1960Google Scholar
  28. Gallant AR (1981) On the bias in flexible functional forms and an essentially unbiased form. the flexible Fourier form J Econom 15:211 245Google Scholar
  29. Gallegati M, Gallegati M, Ramsey JB, Semmler W (2017) Long waves in prices: new evidence from wavelet analysis. Cliometrica 11(1):127 151CrossRefGoogle Scholar
  30. Marco Gallegati D (2018) Delli Gatti, long waves in history: a new global financial instability index. J Econ Dynam Control, 91 190:205Google Scholar
  31. Geels FW (2002) Technological transitions as evolutionary reconfiguration processes: a multilevel perspective and a case-study. Res Policy 31(8):1257–1274CrossRefGoogle Scholar
  32. Geels FW, Kemp R, Dudley G, Lyons G (eds) (2012) Automobility in transition? A Socio-Technical Analysis of Sustainable Transport. Routledge, New YorkGoogle Scholar
  33. Gencay R, Selcuk F, Whitcher B (2002) An introduction to wavelets and other filtering methods in finance and Economics. San Diego Academic Press, San DiegoGoogle Scholar
  34. Goldstein JS (1988) Long Cycles: prosperity and war in the modern age. Yale University Press, New HavenGoogle Scholar
  35. Goldstein JP (1999) The existence, endogeneity and synchronization of long waves: structural time series model estimates. Rev Radic Polit Econ 31:61 101CrossRefGoogle Scholar
  36. Gordon DM (1978) Up and down the long roller coaster? In: Economics P (ed) Union for Radical. U.S. Capitalism in Crisis, URPE, New YorkGoogle Scholar
  37. Gore C (2010) The global recession of 2009 in a long-term development perspective. J Intern Dev 22(6):714–738CrossRefGoogle Scholar
  38. Grin J, Rotmans J, Schot J (2010) Transitions to sustainable development: new directions in the study of long term transformative change. Routledge, New YorkGoogle Scholar
  39. Hamilton JD (1989) A new approach to the economic analysis of nonstationary time series and the business cycle. Econom. 57:357 384CrossRefGoogle Scholar
  40. Harding D, Pagan A (2016) The econometric analysis of recurrent events in macroeconomics and finance. Princeton University Press, PrincetonCrossRefGoogle Scholar
  41. Heap A (2005) China - the engine of a commodities super cycle. Citigroup Smith Barney, New York CityGoogle Scholar
  42. Jerret D, Cuddington JT (2008) Broadening the statistical search for metal Price super Cycles to steel and related metals. Res Policy 33:188 195Google Scholar
  43. Kleinknecht A (1981) Innovation, accumulation, and crisis: waves in economic development. Review (Fernand Braudel Center) IV 687 711Google Scholar
  44. Kohler J (2012) A comparison of the neo-Schumpeterian theory of Kondratiev waves and the multi-level perspective on transitions. Environ Innov Soc Transit 3:1–15CrossRefGoogle Scholar
  45. Kondratiev ND (1935) The long waves in economic life. Rev Econ Stat 17(6):105–115CrossRefGoogle Scholar
  46. Korotayev AV, Tsirel SV (2010) A spectral analysis of world GDP dynamics: Kondratieff waves, Kuznets swings, Juglar and Kitchin Cycles in global economic development, and the 2008-2009 economic crisis. Struct Dyn 4(1):3–57Google Scholar
  47. Kriedel N (2009) Long waves of economic development and the diffusion of general-purpose technologies: the case of railway networks. Economies et Societes, serie histoire economique quantitative. AF, 40 877:900Google Scholar
  48. Kuczynski T (1978) Spectral analysis and cluster analysis as mathematical methods for the periodization of historical processes. Kondratieff Cycles - appearance or reality? In: Proceedings of the seventh international economic history congress, vol 2. International Economic History Congress, Edinburgh, pp 79–86Google Scholar
  49. Kuczynski T (1982) Leads and lags in an escalation model of capitalist development: Kondratieff Cycles reconsidered, proceedings of the eighth international economic history congress. Vol. 3. International economic history congress. BudapestGoogle Scholar
  50. Lewis WA (1978) Growth and fluctuations 1870–1913. Allen and Unwin, MAGoogle Scholar
  51. Maddison A (1991) Dynamic forces in capitalist development. Oxford University Press, OxfordGoogle Scholar
  52. Maddison A (2003) The world economy: historical statistics. OECD, ParisCrossRefGoogle Scholar
  53. Maddison A (2007) Fluctuations in the momentum of growth within the capitalist epoch. Cliometrica 1:145 175CrossRefGoogle Scholar
  54. Mason P (2015) PostCapitalism: A Guide to our Future. Allen Lane, UKGoogle Scholar
  55. Metz R (1992) A re-examination of long waves in aggregate production series. In: Kleinknecht A (ed) New findings in long waves research. St. Martin’s Printing, New YorkGoogle Scholar
  56. Mintz I (1969) Dating Postwar Business Cycles: Methods and their application to Western Germany: 1950-1967. In: Occasional Paper, vol 107. NBER, New YorkGoogle Scholar
  57. Mintz I (1972) Dating American growth Cycles. In: Zarnowitz V (ed) The business cycle today. NBER, New YorkGoogle Scholar
  58. Percival DB, Walden AT (2000) Wavelet methods for time series analysis. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  59. Perez C (2002) Technological revolutions and financial capital: the dynamics of bubbles and Golden ages. Edward Elgar, UK, CheltenhamCrossRefGoogle Scholar
  60. Perez C (2007) Finance and technical change: a long-term view. In: Hanusch H, Pyka A (eds) The Elgar companion to neo-Schumpeterian Economics. Edward Elgar, CheltenhamGoogle Scholar
  61. Perez C (2009) The double bubble at the turn of the century: technological roots and structural implications. Camb J Econ 33(4):779–805CrossRefGoogle Scholar
  62. Perez C (2010) Technological revolutions and techno-economic paradigms. Camb J Econ 34:185 202CrossRefGoogle Scholar
  63. Proietti T (2011) Trend estimation. In: Lovric M (ed) International encyclopedia of statistical science, 1st edn. Springer, BerlinGoogle Scholar
  64. Ramsey JB (2010) Wavelets. In: Durlauf SN, Blume LE (eds) The new Palgrave dictionary of Economics. Palgrave Macmillan, BasingstokeGoogle Scholar
  65. Ramsey JB (2014) Functional representation, approximation, bases and wavelets. In: Gallegati M, Semmler W (eds) Wavelet applications in Economics and finance. Springer-Verlag, HeidelbergGoogle Scholar
  66. Ramsey JB, Zhang Z (1996) The application of waveform dictionaries to stock market index data. In: Kravtsov YA, Kadtke J (eds) Predictability of complex dynamical systems. Springer-Verlag, BerlinGoogle Scholar
  67. Reati A, Toporowski J (2009) An economic policy for the fifth long wave. PSL quart. Rev, 62 143:186Google Scholar
  68. Rosenberg N, Frischtak CR (1983) Long waves and economic growth: a critical appraisal. Amer Econ Rev 73:146–151Google Scholar
  69. Schot J, Kanger L (2018) Deep transitions: emergence, acceleration, stabilization and directionality. Res Policy 47(6):1045 1059CrossRefGoogle Scholar
  70. Schot J (2016) Confronting the second deep transition through the historical imagination. Technol Cult 57(2):445–456CrossRefGoogle Scholar
  71. Schumpeter JA (1939) Business Cycles. McGraw-Hill, New York, NYGoogle Scholar
  72. Standard Chartered, The super-cycle report. London: global research standard Chartered, 2010Google Scholar
  73. Swilling M (2013) Economic Crisis, Long Waves and the sustainability transition: an African perspective. Environ. Innov. Soc. Transit. 6:96–115CrossRefGoogle Scholar
  74. Terasvirta T (1994) Specification, estimation, and evaluation of smooth transition autoregressive models. J Amer Stat Ass 89:208 218Google Scholar
  75. Tyfield D (2016) On Paul Mason’s “post-capitalism”: an extended review. MimeoGoogle Scholar
  76. Tylecote A (1991) The long wave in the world economy. Routledge, LondonGoogle Scholar
  77. Vogelsang TJ, Perron P (1998) Additional tests for a unit root allowing for a break in the trend function at an unknown time. Intern. Econ. Rev. 39:1073 1100CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Economics and Social Sciences, Faculty of Economics “G. Fuà”Università Politecnica delle MarcheAnconaItaly

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