Araştırma Makalesi
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KONJONKTÜR DALGALARININ BELİRLEYİCİLERİ: G7 VE E7 ÜLKELERİ ÜZERİNE KARŞILAŞTIRMALI BİR ANALİZ

Yıl 2020, Cilt: 16 Sayı: 4, 762 - 793, 31.12.2020

Ali Gökhan Yücel

https://doi.org/10.17130/ijmeb.784320

Öz

Konjonktür dalgaları konusunda yaklaşık bir asırdır araştırma yapılmasına rağmen, konjonktür dalgalarının nedenleri ve niteliği ile ilgili halen net bir sonuca ulaşılmış değildir. Piyasa ekonomilerinin yaşadığı dalgalanmalar sonucu yaşanılan krizlerle ve durgunluklarla mücadele edebilmek için dalgaların yapısının iyi analiz edilmesi gerekmektedir. Bu çalışmanın amacı konjonktür dalgalarının belirleyicilerini gelişmiş ve gelişmekte olan ülkeler için ampirik olarak test etmektir. Bu amaca ulaşmak için G7 ve E7 ülkelerinin 1960-2017 dönemine ait yıllık verileri kullanılarak konjonktür dalgaları, kamu harcamaları, para arzı, toplam faktör verimliliği, seçimleri temsil eden kukla değişken, ticari açıklık oranı ve tarımsal üretim değişkenleri kullanılarak panel zaman serisi analizi gerçekleştirilmiştir. Uygulama kısmında yatay kesit bağımlılığı, ikinci nesil birim kök testleri ve ikinci nesil panel eşbütünleşme testleri gerçekleştirilmiştir. Çalışmanın bulgularına göre hem G7 ülkelerinde hem de E7 ülkelerinde konjonktür dalgaları durağan bir sürece sahip değildir. Dolayısıyla, gayrisafi yurtiçi hasılanın uzun dönem büyüme trendinden sapması olan nitelendirilen konjonktür dalgalanmaları zaman içerisinde uzun dönem trend değerine kendiliğinden dönmemektedir. Bu nedenle, politika yapıcıların aktif iktisat politikaları kullanarak konjonktür dalgalarına müdahale etmeleri gerekmektedir. Eşbütünleşme modeli tahmini sonuçlarına göre hem gelişmiş ülkelerde hem de gelişmekte olan ülkelerde hükümet harcamalarının ve para arzının konjonktür dalgalarını azaltıcı; ticari açıklığın ise konjonktür dalgalarını artırıcı etkisi vardır. Gelişmiş ülkelerde toplam faktör verimliliğinin konjonktür dalgalarını artırıcı etkisi var iken, gelişmekte olan ülkelerde seçimlerin konjonktür üzerinde pozitif bir etkisi vardır. Tarımsal üretimin hasıladaki payı ile konjonktür dalgaları arasında anlamlı bir ilişki tespit edilememiştir.

Anahtar Kelimeler

Konjonktür dalgaları Hodrick-Prescott Filtresi Panel Veri Analizi Fourier Panel KPSS Panel Eşbütünleşme Panel Cointegration

Destekleyen Kurum

Bu çalışma; Erciyes Üniversitesi Bilimsel Araştırma Projeleri Birimi tarafından SDK-2015-5480 kodlu proje ile desteklenmiştir.

Proje Numarası

SDK-2015-5480

Teşekkür

Bu çalışma Prof. Dr. Ekrem Erdem danışmanlığında tamamlanan “Konjonktür dalgalarının belirleyicileri: Gelişmiş ve gelişmekte olan ülkeler üzerine uygulamalı bir analiz” başlıklı doktora tezinden yararlanılarak hazırlanmıştır.

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DETERMINANTS OF BUSINESS CYCLES: A COMPARATIVE ANALYSIS BETWEEN G7 AND E7 COUNTRIES

Yıl 2020, Cilt: 16 Sayı: 4, 762 - 793, 31.12.2020

Ali Gökhan Yücel

https://doi.org/10.17130/ijmeb.784320

Öz

Although business cycles have been investigated for nearly a century, there is still no clear conclusion on the causes and the nature of business cycles. In order to combat the crises and stagnation as a result of the fluctuations experienced by market economies, the structure of business cycles must be well analyzed. The aim of this study is to empirically test the determinants of business cycles for developed and developing countries. To achieve this aim, panel time series analysis was performed using the variables of government expenditure, money supply, total factor productivity, dummy variable representing elections, trade openness and agricultural production for G7 and E7 countries covering the period of 1960-2017. In the empirical part of the study, cross sectional dependency tests, second-generation panel unit root tests and second-generation panel cointegration tests were applied. According to the empirical findings, business cycles are not stationary in both G7 and E7 countries. Therefore, business cycles which could be defined as deviations from the long-term economic growth trend of gross domestic product do not spontaneously return to the long-term trend value over time. For this reason, policy makers need to intervene in the business cycles by using active economic policies. According to the results of cointegration model estimation, government expenditures and money supply are negatively related to business cycles both in E7 and G7 countries, while trade openness is positively related to business cycles. Total factor productivity and dummy variable representing elections are positively related to business cycles in G7 and E7 countries, respectively. Finally, there is no statistically significant relationship between the share of agricultural production and business cycles.

Anahtar Kelimeler

Business Cycles Hodrick-Prescott Filter Panel Data Analysis

Proje Numarası

SDK-2015-5480

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  • Lin, S.-C., & Kim, D.-H. (2014). The link between economic growth and growth volatility. Empirical Economics, 46(1), 43–63. https://doi.org/10.1007/s00181-013-0680-y
  • Maddala, G. S., & Wu, S. (1999). A comparative study of unit root tests with panel data and a new simple test. Oxford Bulletin of Economics and Statistics, 61(s1), 631–652. https://doi.org/10.1111/1468-0084.0610s1631
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  • Massmann, M., Mitchell, J., & Weale, M. (2003). Business cycles and turning points: A survey of statistical techniques. National Institute Economic Review, 183, 90–106.
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  • Papageorgiou, T., Michaelides, P. G., & Tsionas, E. G. (2016). Business cycle determinants and fiscal policy: A Panel ARDL approach for EMU. The Journal of Economic Asymmetries, 13, 57–68. https://doi.org/10.1016/J.JECA.2015.12.001
  • Pawęta, B. (2018). Impact of the global financial crisis on the business cycle in the Visegrad Group. Entrepreneurial Business and Economics Review, 6(3), 43–58. https://doi.org/10.15678/EBER.2018.060303
  • Pedroni, P. (1999). Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and Statistics, 61(S1), 653–670. https://doi.org/10.1111/1468-0084.0610s1653
  • Pedroni, P. (2000). Fully modified OLS for heterogeneous cointegrated panels. Içinde B. H. Baltagi, T. B. Fomby, & R. Carter (Ed.), Nonstationary Panels, Panel Cointegration, and Dynamic Panels (Advances in Econometrics, Volume 15) (ss. 93–130). https://doi.org/10.1016/S0731-9053(00)15004-2
  • Pedroni, P. (2001). Purchasing power parity tests in cointegrated panels. Review of Economics and Statistics, 83(4), 727–731. https://doi.org/10.1162/003465301753237803
  • Pedroni, P. (2004). Panel cointegration: Asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Econometric Theory, 20(03), 597–625. https://doi.org/10.1017/S0266466604203073
  • Perron, P. (1989). The great crash, the oil price shock, and the unit root hypothesis. Econometrica, 57(6), 1361. https://doi.org/10.2307/1913712
  • Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels (IZA DP No. 1240). Cambridge Working Papers in Economics.
  • Pesaran, M. H. (2006). Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica, 74(4), 967–1012. https://doi.org/10.1111/j.1468-0262.2006.00692.x
  • Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics, 22(2), 265–312. https://doi.org/10.1002/jae.951
  • Pesaran, M. H., Shin, Y., & Smith, R. (1999). Pooled mean group estimation of dynamic heterogeneous panels. Journal of the American Statistical Association, 94(446), 621–634.
  • Pesaran, M. H., Smith, V. L., & Yamagata, T. (2013). Panel unit root tests in the presence of a multifactor error structure. Journal of Econometrics, 175(2), 94–115. https://doi.org/10.1016/J.JECONOM.2013.02.001
  • Quinn, D. P., & Woolley, J. T. (2001). Democracy and national economic performance: The preference for stability. American Journal of Political Science, 45(3), 634. https://doi.org/10.2307/2669243
  • Raddatz, C. (2006). Liquidity needs and vulnerability to financial underdevelopment. Journal of Financial Economics, 80(3), 677–722. https://doi.org/10.1016/J.JFINECO.2005.03.012
  • Rand, J., & Tarp, F. (2002). Business cycles in developing countries: Are they different? World Development, 30(12), 2071–2088. https://doi.org/10.1016/S0305-750X(02)00124-9
  • Ravn, M. O., & Uhlig, H. (2002). On adjusting the Hodrick-Prescott filter for the frequency of observations. The Review of Economics and Statistics, 84(2), 371–375.
  • Reese, S., & Westerlund, J. (2016). Panicca: Panic on cross-section averages. Journal of Applied Econometrics, 31(6), 961–981. https://doi.org/10.1002/jae.2487
  • Rodrigues, P. M. M., & Taylor, A. M. R. (2012). The flexible Fourier form and local generalised least squares de-trended unit root tests. Oxford Bulletin of Economics and Statistics, 74(5), 736–759. https://doi.org/10.1111/j.1468-0084.2011.00665.x
  • Rodrik, D. (1998). Why do more open economies have bigger governments? Journal of Political Economy, 106(5), 997–1032. https://doi.org/10.1086/250038
  • Rodrik, D. (1999). Where did all the growth go? External shocks, social conflict, and growth collapses. Journal of Economic Growth, 4(4), 385–412. https://doi.org/10.1023/A:1009863208706
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  • Rumler, F., & Scharler, J. (2011). Labor market institutions and macroeconomic volatility in a panel of OECD countries. Scottish Journal of Political Economy, 58(3), 396–413. https://doi.org/10.1111/j.1467-9485.2011.00552.x
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  • Westerlund, J. (2007a). Estimating cointegrated panels with common factors and the forward rate unbiasedness hypothesis. Journal of Financial Econometrics, 5(3), 491–522. https://doi.org/10.1093/jjfinec/nbm006
  • Westerlund, J. (2007b). Testing for error correction in panel data. Oxford Bulletin of Economics and Statistics, 69(6), 709–748. https://doi.org/10.1111/j.1468-0084.2007.00477.x
  • Westerlund, J. (2008). Panel cointegration tests of the Fisher effect. Journal of Applied Econometrics, 23(2), 193–233. https://doi.org/10.1002/jae.967
  • Westerlund, J. (2012). Testing for unit roots in panel time-series models with multiple level breaks. The Manchester School, 80(6), 671–699. https://doi.org/10.1111/j.1467-9957.2012.02270.x
  • Westerlund, J., & Edgerton, D. L. (2007). A panel bootstrap cointegration test. Economics Letters, 97(3), 185–190. https://doi.org/10.1016/J.ECONLET.2007.03.003
  • Westerlund, J., & Larsson, R. (2009). A note on the pooling of individual PANIC unit root tests. Econometric Theory, 25(06), 1851. https://doi.org/10.1017/S0266466609990351
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Toplam 133 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Ekonomi
Bölüm Araştırma Makaleleri
Yazarlar

Ali Gökhan Yücel 0000-0001-7509-7693

Proje Numarası SDK-2015-5480
Yayımlanma Tarihi 31 Aralık 2020
Gönderilme Tarihi 23 Ağustos 2020
Kabul Tarihi 5 Ekim 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 16 Sayı: 4

Kaynak Göster

APA Yücel, A. G. (2020). KONJONKTÜR DALGALARININ BELİRLEYİCİLERİ: G7 VE E7 ÜLKELERİ ÜZERİNE KARŞILAŞTIRMALI BİR ANALİZ. Uluslararası Yönetim İktisat Ve İşletme Dergisi, 16(4), 762-793. https://doi.org/10.17130/ijmeb.784320
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