SYNCHRONIZATION OF BUSINESS CYCLES IN OECD COUNTRIES: 1961-2019

Yıl 2020, Cilt: 10 Sayı: 19, 181 - 197, 30.06.2020

Necmettin Alpay Koçak

Öz

The synchronization of the GDP growth cycles between countries have been discussed in the literature. The existence and dimensions of synchronization between the GDP growth cycles of the OECD countries are examined in this study. The existence of synchronization is revealed by estimating the time-varying (dynamic) conditional correlations used in measuring the level of synchronization in two stages by VAR-DCC-MGARCH methods in the study in which the period between 1961-2019 is analyzed. Unlike the literature, K-Means cluster analysis is applied to examine the differentiation of dynamic conditional correlations with respect to member countries. In addition, the effects of economic crises on dynamic conditional correlations are measured by panel data analysis by clusters. The findings showed that there is a synchronization among the member countries and the synchronization levels differ according to the countries. According to the synchronization levels, the member countries are determined to be divided into two clusters and it is observed that the countries in Europe and the Americas concentrated in one cluster. Finally, it is determined that the 1974 oil and 2009 global economic crises reduced the synchronization between the member countries while the 1963 European and 1997 Asian crises increased synchronization in the analysis period. It is observed that the effects of economic crises on synchronization are similar in direction but different in size by clusters.

Anahtar Kelimeler

Dynamic Conditional Correlation, Business Cycles, Synchronization, Economic Crises, Cluster Analysis, Panel Data Analysis

OECD ÜLKELERİNDE KONJONKTÜR DALGALANMALARININ SENKRONİZASYONU: 1961-2019

Öz

GSYH büyümesindeki döngülerin (konjonktür dalgalanmaları) ülkeler arasındaki senkronizasyonu literatürde tartışılmaktadır. Bu çalışmada OECD’ye üye ülkelerin konjonktür dalgalanmaları arasında senkronizasyonun varlığı ve boyutları incelenmiştir. 1961-2019 yılları arasındaki dönemin analiz edildiği çalışmada, senkronizasyonun seviyesinin ölçülmesinde kullanılan zamana bağlı değişen (dinamik) koşullu korelasyonlar VAR-DCC-MGARCH yöntemleri ile iki aşamada tahmin edilerek senkronizasyonun varlığı ortaya konulmuştur. Literatürden farklı olarak, dinamik koşullu korelasyonların üye ülkelere göre farklılaşmasını irdelemek amacıyla K-Means kümeleme analizi uygulanmıştır. Ayrıca, ekonomik krizlerin dinamik koşullu korelasyonlar üzerindeki etkileri kümeler ayrımında uygulanan panel veri analizi ile ölçülmüştür. Elde edilen bulgular, üye ülkeler arasında bir senkronizasyonun var olduğunu ve ülkelere göre senkronizasyon seviyelerinin farklı olduğunu göstermiştir. Senkronizasyon seviyelerine göre üye ülkelerin iki kümeye ayrıldığı belirlenmiş ve Avrupa ile Amerika kıtasındaki ülkelerinin bir kümede yoğunlaştığı gözlemlenmiştir. Son olarak, analiz döneminde yaşanan 1974 petrol ve 2009 küresel ekonomik krizlerinin üye ülkelerin arasındaki senkronizasyonu azaltıcı fakat 1963 Avrupa ve 1997 Asya krizlerinin senkronizasyonu arttırıcı etki yaptığı tespit edilmiştir. Ekonomik krizlerin senkronizasyon üzerindeki etkilerinin kümeler ayrımında yön olarak benzer, boyut olarak farklı olduğu görülmüştür.

Anahtar Kelimeler

Dinamik koşullu korelasyon, Konjonktür dalgalanmaları, Senkronizasyon, Ekonomik krizler, Kümeleme analizi, Panel veri analizi

Kaynakça

• Amemiya, Takeshi ve Thomas E MaCurdy. 1986. “Instrumental-Variable Estimation of an Error-Components Model.” Econometrica 54 (4): 869–80. https://EconPapers.repec.org/RePEc:ecm:emetrp:v:54:y:1986:i:4:p:869-80.
• Antonakakis, Nikolaos. 2012. “Business cycle synchronization during US recessions since the beginning of the 1870s.” Economics Letters 117 (2): 467–72. https://doi.org/10.1016/j.econlet.2012.06.
• Antonakakis, Nikolaos ve Johann Scharler. 2012. “The synchronization of GDP growth in the G7 during US recessions.” Applied Economics Letters 19 (1): 7–11. https://doi.org/10.1080/13504851.2011.564.
• Baxter, Marianne ve Michael A Kouparitsas. 2005. “Determinants of Business Cycle Comovement: A Robust Analysis.” Journal of Monetary Economics 52 (1): 113–57. Böwer, Uwe ve Catherine Guillemineau. 2006. “Determinants of Business Cycle Synchronisation Across Euro Area Countries.” Working Paper Series 587. European Central Bank. https://EconPapers.repec.org/RePEc:ecb:ecbwps:2006587.
• Calderon, César. 2009. “Trade, Specialization, and Cycle Synchronization: Explaining Output Comovement Between Latin America, China, and India.” China’s and India’s Challenge to Latin America: Opportunity or Threat.
• Cameron, A. Colin, Jonah B. Gelbach ve Douglas L. Miller. 2011. “Robust Inference with Multiway Clustering.” Journal of Business & Economic Statistics 29 (2): 238–49. http://www.jstor.org/stable/25800796.
• Chamberlin, G ve L Yueh. 2006. Macroeconomics. Thompson Learning. London.
• Clark, Todd E. ve Eric van Wincoop. 2001. “Borders and Business Cycles.” Journal of International Economics 55 (1): 59–85. https://doi.org/https://doi.org/10.1016/S0022-1996(01)00095-2.
• Crosby, Mark. 2003. “Business Cycle Correlations in Asia-Pacific.” SSRN Electronic Journal, January. https://doi.org/10.2139/ssrn.1009367.
• Çakır, M. Y. ve A. Kabundi. 2013. “Business Cycle Co-Movements Between South Africa and the Bric Countries.” Applied Economics 45 (33): 4698–4718. https://doi.org/10.1080/00036846.2013.797562.
• Engle, Robert. 2002. “Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models.” Journal of Business & Economic Statistics 20 (3): 339–50.
• Fidrmuc, Jarko. 2004. “The Endogeneity of the Optimum Currency Area Criteria, Intra-Industry Trade, and Emu Enlargement.” Contemporary Economic Policy 22 (1): 1–12. https://doi.org/10.1093/cep/byh001.
• Fiess, Norbert. 2007. “Business Cycle Synchronization and Regional Integration: A Case Study for Central America.” World Bank Economic Review 21 (1): 49–72. https://ideas.repec.org/a/oup/wbecrv/v21y2007i1p49-72.html.
• Frankel, Jeffrey A. ve Andrew K. Rose. 2001. “The Endogenity of the Optimum Currency Area Criteria.” The Economic Journal 108 (449): 1009–25. https://doi.org/10.1111/1468-0297.00327.
• Gruben, William, Jahyeong Koo ve Eric Millis. 2002. “How Much Does International Trade Affect Business Cycle Synchronization?” February.
• Hartigan, J. A. ve M. A. Wong. 1979. “Algorithm as 136: A K-Means Clustering Algorithm.” Journal of the Royal Statistical Society. Series C (Applied Statistics) 28 (1): 100–108. http://www.jstor.org/stable/2346830.
• Hausman, J. A. 1978. “Specification Tests in Econometrics.” Econometrica 46 (6): 1251–71. http://www.jstor.org/stable/1913827.
• Hausman, Jerry A. ve William E. Taylor. 1981. “Panel Data and Unobservable Individual Effects.” Econometrica 49 (6): 1377–98. http://www.jstor.org/stable/1911406.
• Heathcote, Jonathan ve Fabrizio Perri. 2002. “Financial Autarky and International Business Cycles.” Journal of Monetary Economics 49 (3): 601–27. https://doi.org/https://doi.org/10.1016/S0304-3932(02)00103-4.
• Hosking, J. R. M. 1980. “The Multivariate Portmanteau Statistic.” Journal of the American Statistical Association 75 (371): 602–8. http://www.jstor.org/stable/2287656.
• Imbs, Jean. 2010. “The First Global Recession in Decades.” IMF Economic Review 58 (2): 327–54. https://EconPapers.repec.org/RePEc:pal:imfecr:v:58:y:2010:i:2:p:327-354.
• Inklaar, Robert, Richard Jong-A-Pin ve Jakob de Haan. 2008. “Trade and business cycle synchronization in OECD countries–A re-examination.” European Economic Review 52 (4): 646–66. https://ideas.repec.org/a/eee/eecrev/v52y2008i4p646-666.html.
• Kose, M. Ayhan ve Kei-Mu Yi. 2006. “Can the Standard International Business Cycle Model Explain the Relation Between Trade and Comovement?” Journal of International Economics 68 (2): 267–95. https://doi.org/https://doi.org/10.1016/j.jinteco.2005.07.002.
• Leamer, Edward E. 1983. “Let’s Take the Con Out of Econometrics.” The American Economic Review 73 (1): 31–43.
• Lütkepohl, Helmut. 2005. New Introduction to Multiple Time Series Analysis. Springer Science & Business Media.
• Otto, Glenn, Graham Voss ve Luke Willard. 2001. “Understanding Oecd Output Correlations.” RBA Research Discussion Papers. Reserve Bank of Australia. https://EconPapers.repec.org/RePEc:rba:rbardp:rdp2001-05.
• Rousseeuw, Peter. 1987. “Rousseeuw, P.j.: Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis. Comput. Appl. Math. 20, 53-65.” Journal of Computational and Applied Mathematics 20 (November): 53–65. https://doi.org/10.1016/0377-0427(87)90125-7.
• Sala-i-Martin, Xavier. 1996. “The Classical Approach to Convergence Analysis.” Economic Journal 106 (437): 1019–36. https://EconPapers.repec.org/RePEc:ecj:econjl:v:106:y:1996:i:437:p:1019-36.