Abstract
This study examines international synchronization of growth rate cycles using spectral techniques in the frequency domain. In particular, we look at synchronization of growth rate cycles between bilateral country pairs, US, UK, Germany, Japan and India over the period from January 1974 to December 2018. We examine two aspects of the synchronization process—one, strength of co-movement across countries’ growth rate cycles, and two, sequencing in terms of leads and lags of these cycles vis-à-vis each other. The strength of co-movements is analyzed using coherences of growth rate cycles between bilateral country pairs across frequency bands and over time. The lead–lag structure between growth rate cycles of countries is obtained from the spectral phase shift parameter. This is evaluated against the lead–lag structure in the time domain, as inferred from the reference chronology given by the Economic Cycle Research Institute (ECRI). Based on the growth rate of the coincident index obtained from ECRI, we infer the sequencing of growth rate cycles in one country vis-à-vis the other in terms of the relative timing of their peaks and troughs. This comparative analysis across the time and frequency domains highlights both the pattern of lead–lag in terms of timing of peaks and troughs (time domain) as well as the lead–lag in terms of all phases of the cycle (frequency domain). For analyzing these patterns over time, we undertake the exercise over two subsamples: January 1974–December 1990 and January 1991–December 2018. We find that the coherence between developed country pairs is, in general, higher than that between developed–emerging economy pairs. We also find evidence of greater co-movement of country cycles post-1990, as compared with that in 1974–1990. The magnitude of leads–lags shows that the synchronization process is faster in the latter time period. The leads–lags obtained from the spectral phase shift estimates are found to be in line with those inferred from economic indicator analysis (EIA).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
To gain insight into the cyclical process of growth, it is important to understand the difference between business cycles and growth rate cycles and their relationship. A business cycle measures ups and downs in economic activity. Growth rate cycles, on the other hand, are cyclical upswings and downswings in the growth rate of economic activity. The reference chronology method (based on the economic indicator analysis) of dating cycles defines peaks and troughs in the cycle. A movement from a peak to a trough is said to constitute a contraction (slowdown) while that from a trough to a peak an expansion (pickup). A slowdown, a milder counterpart of a recession, is a downshift in the pace of growth in economic activity. Economic slowdowns begin with reduced but still positive growth rates, which may eventually develop into recessions.
- 2.
- 3.
For illustration, we use the relationship to the numbers used in Fig. 3. Over a period of 10 years, the high-frequency component corresponding to T = 12 months is equivalent to ω = 2πf = 2π/T = π/6. Similarly, the business cycle frequencies for T = 48 work out to be associated with ω = π/24, and a low frequency corresponding to T = 108 has ω = π/54.
- 4.
- 5.
- 6.
For details, see Priestley, (1981).
- 7.
Some authors refer to \(\left| {w_{ij} (\omega )} \right|^{2}\) as the coherence.
- 8.
For the polar form \(\hat{h}_{ij} (\omega ) = \hat{c}_{ij} (\omega ) - i\hat{q}_{ij} (\omega )\), the co-spectrum is the real part and quadrature spectrum the imaginary part.
- 9.
Results are not reported for the sake of brevity, but are available from the authors on request.
- 10.
Granger and Hatanaka, (1964) call a coherence value of more than 0.5 as high.
- 11.
Since the beginning of 1990s has historical significance as far as events in the international economy are concerned, this was used as a divide year for the sample.
- 12.
See Granger and Hatanaka, (1964).
References
Aguiar-Conraria, L., & Soares, M. J. (2011). Business cycle synchronization and the Euro: A wavelet analysis. Journal of Macroeconomics, 33, 477–489.
Allegret, J. P., & Essaadi, E. (2011). Business cycle synchronization in East Asian economy: Evidence from time-varying coherence study. Economic Modelling, 28, 351–365.
Aloui, C., & Hkiri, B. (2014). Co-movements of GCC emerging stock markets: New evidence from wavelet coherence analysis. Economic Modelling, 36, 421–431.
Anderson, H. M., & Terasvirta, T. (1992). Characterizing non-linearities in business cycles using smooth transition autoregressive models. Journal of Applied Econometrics, 7, S119–S136.
Antonakakis, N. (2012). Business cycle synchronization during US recessions since the beginning of the 1870s. Economics Letters, 117, 467–472.
Artis, M. (2003). Is there a European business cycle, WP 1053, CESIFO.
Artis, M., Kontolemis, Z. G., & Osborn, D. R. (1997). Business cycles for G7 and European countries. Journal of Business, 70, 249–270.
Backus, D. K., Kehoe, P. J., & Kydland, F. E. (1992). International real business cycles. Journal of Political Economy, 100, 745–775.
Banerji, A., & Dua, P. (2010). Synchronization of recessions in major developed and emerging economies. Margin—The Journal of Applied Economic Research, 4, 197–223.
Banerji, A., & Dua, P. (2011). Predicting recessions and slowdowns: A robust approach, WP 202, Centre for Development Economics
Banerji, A., & Hiris, L. (2001). A framework for measuring international business cycles. International Journal of Forecasting, 17, 333–348.
Bátorová, I. (2007). Application of factor models in business cycle analysis in the enlarged EU, Unpublished Master Thesis, Bratislava.
Bátorová, I., Fidrmuc, J., & Korhonen, I. (2008). China in the world economy: Dynamic correlation analysis of business cycles, discussion paper 7, Bank of Finland.
Bátorová, I., Fidrmuc, J., & Korhonen, I. (2009). New global players and disharmonies in the world orchestra: Cohesion analysis of business cycles of China. InThe Economic Performance of the European Union, Palgrave Macmillan, London.
Bátorová, I., Fidrmuc, J., & Korhonen, I. (2011). From Decoupling of Asian Stock Markets to Recoupling during the Great Recession. In S. Rosefielde, M. Kuboniwa, & S. Mizobata (Eds.), Two Asias: The emerging postcrisis divide, World Scientific, Singapore.
Bátorová, I., Fidrmuc, J., & Korhonen, I. (2013). China in the world economy: Dynamic correlation analysis of business cycles. Cesifo Economic Studies, 59, 392–411.
Baxter, M. (1995). International trade and business cycles. Handbook of International Economics, 3, 1801–1864.
Bayoumi, T. & Helbling, T. (2003). Are they all in the same boat? The 2000–2001 Growth Slowdown and the G-7 Business Cycle Linkages, WP 46, IMF.
Berdiev, A. N., & Chang, C. P. (2015). Business cycle synchronization in Asia-Pacific: New evidence from wavelet analysis. Journal of Asian Economics, 37, 20–33.
Boehm, E. A. (2001). The contribution of economic indicator analysis to understanding and forecasting business cycles. Indian Economic Review, 36, 1–36.
Bordo, M. D. & Hebling, T. (2003). Have national business cycles become more synchronized, WP 10130, NBER.
Breitung, J., & Candelon, B. (2001). Is there a European business cycle? New insights from a frequency domain analysis. Vierteljahrshefte Zur Wirtschaftsforschung (quarterly Journal of Economic Research), 70, 331–338.
Bry, G. & Boschan, C. (1971). Standard Business Cycle Analysis of Economic Time Series. In G. Bry, & C. Boschan (Eds.), Cyclical analysis of time series: Selected procedures and computer programs, NBER.
Burns, A., & Mitchell, W. (1946). Measuring business cycles. New York: NBER.
Çakır, M. Y., & Kabundi, A. (2013). Trade shocks from BRIC to South Africa: A global VAR analysis. Economic Modelling, 32, 190–202.
Camba-Mendez, G. & Kapetanios, G. (2001). Spectral based methods to identify common trends and common cycles, WP 62, European Central Bank.
Campos, N. F., Fidrmuc, J., & Korhonen, I. (2019). Business cycle synchronisation and currency unions: A review of the econometric evidence using meta-analysis. International Review of Financial Analysis, 61, 274–283.
Candelon, B., & Hecq, A. (2000). Stability of Okun’s law in a co-dependent system. Applied Economic Letters, 7, 687–693.
Canova, F. (1998). Detrending and business cycle facts. Journal of Monetary Economics, 41, 475–512.
Canova, F., & Dellas, H. (1993). Trade interdependence and the international business cycle. Journal of International Economics, 34, 23–47.
Canova, F., Ciccarelli, M., & Ortega, E. (2007). Similarities and convergence in G-7 cycles. Journal of Monetary Economics, 54, 850–878.
Chatfield, C. (1996). The Analysis of Time Series: An Introduction. London: Chapman and Hall.
Croux, C., Forni, M., & Reichlin, L. (1999). A measure of co-movement for economic variables: Theory and empirics, No. 2339, CEPR Discussion Paper.
Cubadda, G., & Hecq, A. (2001). On non-contemporaneous short-run co-movements. Economics Letters, 73, 389–397.
Diebold, F. X., & Yilmaz, K. (2009). Measuring financial asset return and volatility spillovers with applications to global equity markets. The Economic Journal, 119, 158–171.
Dufrénot, G., & Keddad, B. (2014). Business cycles synchronization in East Asia: A Markov-switching approach. Economic Modelling, 42, 186–197.
Elliot, G., Rothenberg, T. J., & Stock, J. H. (1996). Efficient tests for an autoregressive unit root. Econometrica, 64, 813–836.
Engle, R. F., & Vahid, F. (1993). Common trends and common cycles. Journal of Applied Econometrics, 8, 341–360.
Engle, R. F., & Kozicki, S. (1993). Testing for common features. Journal of Business and Economic Statistics, 11, 369–395.
Fidrmuc, J., Korhonen, I., & Poměnková, J. (2014). China and the world economy: Wavelet spectrum analysis of business cycles. Applied Economics Letters, 21, 1309–1313.
Filardo, A. J., & Gordon, S. F. (1994). International Co-movements of Business Cycles, WP 94–11, Federal Reserve Bank of Kansas City.
Fuller, W. (1976). Introduction to statistical time series. New York: Wiley.
Gordon, R. A. (1961). Business fluctuations. New York: Harper and Row.
Granger, C. (1969). Investigating casual relations by econometric models and cross-spectral methods. Econometrica, 37, 424–438.
Granger, C. J., & Hatanaka, M. (1964). Spectral Analysis of Economic Time Series. Princeton: Princeton University Press.
Hamilton, J. (1989). A new approach to the economic analysis of non-stationary time series and business cycles. Econometrica, 57, 357–384.
Hamilton, J. (1994). Time series analysis. Princeton: Princeton University Press.
Hamilton, J. D. (2005). Oil and the macroeconomy. In S. Durlauf, & L. Blume (Eds.), The new Palgrave dictionary of economics, Macmillan, London.
Hanus, L. & Vacha, L. (2015). Business Cycle Synchronization of the Visegrad Four and the European Union, WP 42, Collaborative EU Project Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
Harding, D., & Pagan, A. (2006). Synchronization of Cycles. Journal of Econometrics, 132, 59–79.
Harvey, A. (1993). Time series models. Cambridge: The MIT Press.
Hatanaka, M. (1996). Time-series-based econometrics. New York: Oxford University Press.
Hecq, A. (2009). Asymmetric business cycle co-movements. Applied Economics Letters, 16, 579–584.
Hughes-Hallett, A. J., & Richter, C. (2004). A Time-Frequency Analysis of the Coherences of the US Business Cycle and the European Business Cycle, No. 4751, Centre for Economic Policy Research.
Hughes-Hallett, A. J., & Richter, C. (2008). Have the Eurozone economies converged on a common European cycle. International Economics and Economic Policy, 5, 71–101.
IMF. (2013). World economic outlook. Washington: IMF.
Janacek, G., & Swift, L. (1993). Time Series: Forecasting, Simulation, Applications, Ellis Horwood, New York.
Jensen, R. V., & Selover, D. (1999). Mode-locking and international business cycle transmission. Journal of Economic Dynamics and Control, 23, 591–618.
Kapounek, S., & Pomënková, J. (2010). Business Cycle Development in Czech And Slovak Economies, Bulletin of the Transilvania University of Brașov, 3.
Kim, S. H., & Saiki, A. (2014). Business cycle synchronization and vertical trade integration: A case study of the Eurozone and East Asia, WP 407, Netherlands Central Bank Working Papers.
Kwiatkowski, D., Phillips, P., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics, 54, 159–178.
Maršálek, R., & Pomënková, J. (2010). Spectral analysis of the cyclical behaving of the Czech republic industrial production. Forum Statisticum Slovacum, 2, 123–128.
Maršálek, R., & Pomënková, J. (2011). Time and frequency domain in the business cycle structure, WP 7, MENDELU Working Papers in Business and Economics.
Medhioub, I. (2010). Business cycle synchronization: A mediterranean comparison, WP 527, Economic Research Forum.
Owens, R., & Sarte, P. D. (2005). How well do diffusion indexes capture business cycles? A Spectral Analysis, Federal Reserve Bank of Richmond Economic Quarterly, 91.
Priestley, M. B. (1981). Spectral analysis and time series, I and II, Academic Press.
Pakko (2004). A spectral analysis of the cross-country consumption correlation puzzle, WP 2003–023B, Federal Reserve Bank of St. Louis.
Saiki, A. (2018). Business cycle synchronization and vertical trade integration: A case study of the Eurozone and East Asia. Global Economy Journal, 18, 1–15.
Tiao, G. C., & Tsay, R. S. (1994). Some advances in non-linear and adaptive modelling in time-series. Journal of Forecasting Special Issue on Time Series Advances in Economic Forecasting, 13, 109–131.
Yetman, J. (2011). Exporting recessions: International links and the business cycle. Economics Letters, 110, 12–14.
Zimmermann, C. (1997). International real business cycles among heterogeneous countries. European Economic Review, 41, 319–356.
Acknowledgements
The authors would like to thank the Economics Cycle Research Institute (ECRI), New York, for providing the data used in the study.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Dua, P., Sharma, V. (2023). International Synchronization of Growth Rate Cycles: An Analysis in Frequency Domain. In: Dua, P. (eds) Macroeconometric Methods. Springer, Singapore. https://doi.org/10.1007/978-981-19-7592-9_11
Download citation
DOI: https://doi.org/10.1007/978-981-19-7592-9_11
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-7591-2
Online ISBN: 978-981-19-7592-9
eBook Packages: Economics and FinanceEconomics and Finance (R0)