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

Year 2020, Volume: 16 Issue: 4, 762 - 793, 31.12.2020

Ali Gökhan Yücel

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

Abstract

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.

Keywords

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

Supporting Institution

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

Project Number

SDK-2015-5480

Thanks

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.

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

Abstract

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.

Keywords

Business Cycles, Hodrick-Prescott Filter, Panel Data Analysis

Project Number

SDK-2015-5480

References

• Abuaf, N., & Jorion, P. (1990). Purchasing Power Parity in the Long Run. The Journal of Finance, 45(1), 157–174. https://doi.org/10.1111/j.1540-6261.1990.tb05085.x
• Acemoglu, D., Johnson, S., Robinson, J., & Thaicharoen, Y. (2003). Institutional causes, macroeconomic symptoms: volatility, crises and growth. Journal of Monetary Economics, 50(1), 49–123. https://doi.org/10.1016/S0304-3932(02)00208-8
• Alesina, A., Campante, F. R., & Tabellini, G. (2008). Why is fiscal policy often procyclical? Journal of the European Economic Association, 6(5), 1006–1036. https://doi.org/10.1162/JEEA.2008.6.5.1006
• Artis, M. J., & Zhang, W. (1997). International business cycles and the ERM: Is there a European business cycle? International Journal of Finance & Economics, 2(1), 1–16. https://doi.org/10.1002/(SICI)1099-1158(199701)2:1<1::AID-IJFE31>3.0.CO;2-7
• Bacchetta, P., & Caminal, R. (2000). Do capital market imperfections exacerbate output fluctuations? European Economic Review, 44(3), 449–468. https://doi.org/10.1016/S0014-2921(98)00083-X
• Bai, J., & Kao, C. (2006). On the estimation and inference of a panel cointegration model with cross-sectional dependence. Içinde B. H. Baltagi (Ed.), Contributions to Economic Analysis (C. 274, ss. 3–30). Elsevier. https://doi.org/10.1016/S0573- 8555(06)74001-9
• Bai, J., Kao, C., & Ng, S. (2009). Panel cointegration with global stochastic trends. Journal of Econometrics, 149(1), 82–99. https://doi.org/10.1016/J.JECONOM.2008.10.012
• Bai, J., & Ng, S. (2004). A PANIC attack on unit roots and cointegration. Econometrica, 72(4), 1127–1177. https://doi.org/10.1111/j.1468-0262.2004.00528.x
• Bai, J., & Ng, S. (2010). Panel unit root tests with cross-section dependence: A further investigation. Econometric Theory, 26(4), 1088–1114.
• Baltagi, B. H., & Pesaran, M. H. (2007). Heterogeneity and cross section dependence in panel data models: theory and applications introduction. Journal of Applied Econometrics, 22(2), 229–232. https://doi.org/10.1002/jae.955
• Becker, R., Enders, W., & Lee, J. (2006). A stationarity test in the presence of an unknown number of smooth breaks. Journal of Time Series Analysis, 27(3), 381–409. https://doi.org/10.1111/j.1467-9892.2006.00478.x
• Bejan, M. (2006). Trade openness and output volatility. MPRA Paper No. 2759. https://mpra.ub.uni-muenchen.de/2759/1/MPRA_paper_2759.pdf
• Bekaert, G., Harvey, C. R., & Lundblad, C. (2006). Growth volatility and financial liberalization. Journal of International Money and Finance, 25(3), 370–403. https://doi.org/10.1016/J.JIMONFIN.2006.01.003
• Billmeier, A. (2014). Ghostbusting: Which Output Gap Measure Really Matters? Içinde IMF Working Papers (IMF Working Paper WP/04/146, C. 04, Sayı 146). https://doi.org/10.5089/9781451856675.001
• Blackburn, K., & Ravn, M. O. (1992). Business cycles in the United Kingdom: Facts and fictions. Economica, 59(236), 383. https://doi.org/10.2307/2554886
• Breitung, J. (2001). The local power of some unit root tests for panel data. Içinde Nonstationary panels, panel cointegration, dynamic panels (Advances in econometrics) (C. 15, ss. 161–177). JAI Press. https://doi.org/10.1016/S0731-9053(00)15006-6
• Breitung, J. (2005). A parametric approach to the estimation of cointegration vectors in panel data. Econometric Reviews, 24(2), 151–173. https://doi.org/10.1081/ETC-200067895
• Breuer, J. B., McNown, R., & Wallace, M. (2002). Series-specific unit root tests with panel data. Oxford Bulletin of Economics and Statistics, 64(5), 527–546. https://doi.org/10.1111/1468-0084.00276
• Buch, C. M., & Pierdzioch, C. (2005). The integration of imperfect financial markets: Implications for business cycle volatility. Journal of Policy Modeling, 27(7), 789–804. https://doi.org/10.1016/J.JPOLMOD.2005.06.004
• Çakır, M. Y., & Kabundi, A. (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
• Camarero, M., D’adamo, G., & Tamarit, C. (2016). The role of institutions in explaining wage determination in the Eurozone: A panel cointegration approach. International Labour Review, 155(1), 25–56. https://doi.org/10.1111/ilr.12004
• Carrion-i-Silvestre, J. L., Kim, D., & Perron, P. (2009). GLS-based unit root tests with multiple structural breaks both under the null and the alternative hypotheses. Econometric Theory, 25(6), 1754–1792. https://doi.org/10.1017/S0266466609990326
• Carrion‐i‐Silvestre, J., Del Barrio‐Castro, T., & López‐Bazo, E. (2005). Breaking the panels: An application to the GDP per capita. The Econometrics Journal, 8(2), 159–175. https://doi.org/10.1111/j.1368-423X.2005.00158.x
• Cavallo, E. A., Powell, A. P., & Rigobon, R. (2008). Do Credit Rating Agencies Add Value? Evidence from the Sovereign Rating Business Institutions (IDB Working Paper No. 546). http://www.ssrn.com/abstract=1820934
• Çetin, G., Yıldırım, H. H., Koy, A., & Köksal, C. (2018). Defense expenditures and economic growth relationship: A panel data approach for NATO. Içinde H. Dincer, Ü. Hacioglu, & S. Yüksel (Ed.), Contributions to Economics (ss. 131–149). Springer. https://doi.org/10.1007/978-3-319-78494-6_6
• Cetin, M., Ecevit, E., & Yucel, A. G. (2018). The impact of economic growth, energy consumption, trade openness, and financial development on carbon emissions: empirical evidence from Turkey. Environmental Science and Pollution Research, 25(36), 36589–36603. https://doi.org/10.1007/s11356-018-3526-5
• Chebbi, A., Louafi, R., & Hedhli, A. (2014). Financial fluctuations in the Tunisian repressed market context: A Markov-switching–GARCH approach. Macroeconomics and Finance in Emerging Market Economies, 7(2), 284–302. https://doi.org/10.1080/17520843.2013.781048
• Chistè, C., & van Vuuren, G. (2014). Investigating the cyclical behaviour of the dry bulk shipping market. Maritime Policy & Management, 41(1), 1–19. https://doi.org/10.1080/03088839.2013.780216
• Choi, I. (2001). Unit root tests for panel data. Journal of International Money and Finance, 20(2), 249–272. https://doi.org/10.1016/S0261-5606(00)00048-6
• Cogley, T., & Nason, J. M. (1995). Effects of the Hodrick-Prescott filter on trend and difference stationary time series Implications for business cycle research. Journal of Economic Dynamics and Control, 19(1–2), 253–278. https://doi.org/10.1016/0165-1889(93)00781-X
• Croes, R., & Ridderstaat, J. (2017). The effects of business cycles on tourism demand flows in small island destinations. Tourism Economics, 23(7), 1451–1475. https://doi.org/10.1177/1354816617697837
• Danthine, J.-P., & Donaldson, J. B. (1993). Methodological and empirical issues in real business cycle theory. European Economic Review, 37(1), 1–35. https://doi.org/10.1016/0014-2921(93)90068-L
• Darrat, A. F., Abosedra, S. S., & Aly, H. Y. (2005). Assessing the role of financial deepening in business cycles: the experience of the United Arab Emirates. Applied Financial Economics, 15(7), 447–453. https://doi.org/10.1080/09603100500039417
• Davis, J. S. (2014). Financial integration and international business cycle co-movement. Journal of Monetary Economics, 64, 99–111. https://doi.org/10.1016/j.jmoneco.2014.01.007
• Denizer, C. A., Iyigun, M. F., & Owen, A. (2002). Finance and macroeconomic volatility. The B.E. Journal of Macroeconomics, 2(1), 1–32. https://doi.org/10.2202/1534-6005.1048
• Dickerson, A. P., Gibson, H. D., & Tsakalotos, E. (1998). Business cycle correspondence in the European Union. Empirica, 25(1), 49–75. https://doi.org/10.1023/A:1006888704954
• Djennas, M. (2016). Business cycle volatility, growth and financial openness: Does Islamic finance make any difference? Borsa Istanbul Review, 16(3), 121–145. https://doi.org/10.1016/J.BIR.2016.06.003
• Dreher, A., & Vaubel, R. (2004). Do IMF and IBRD cause moral hazard and political business cycles? Evidence from panel data. Open Economies Review, 15(1), 5–22. https://doi.org/10.1023/B:OPEN.0000009422.66952.4b
• Dreher, A., & Vaubel, R. (2009). Foreign exchange intervention and the political business cycle: A panel data analysis. Journal of International Money and Finance, 28(5), 755–775. https://doi.org/10.1016/J.JIMONFIN.2008.12.007
• Easterly, W., Islam, R., & Stiglitz, J. E. (2001). Shaken and stirred: Explaining growth volatility. Içinde B. Pleskovic & N. Stern (Ed.), Annual World Bank Conference on Development Economics (ss. 191–211). The World Bank.
• Eberhardt, M., & Bond, S. (2009). Cross-section dependence in nonstationary panel models: A novel estimator (MPRA Paper No. 17692).
• Ecevit, E., Yücel, A. G., & Yücel, Ö. (2016). Are some taxes better than others for economic growth? An ARDL approach for Turkey. The Empirical Economics Letters, 15(11), 1129–1136. https://www.researchgate.net/publication/312586370
• Enders, W., & Lee, J. (2012a). A unit root test using a Fourier series to approximate smooth breaks. Oxford Bulletin of Economics and Statistics, 74(4), 574–599. https://doi.org/10.1111/j.1468-0084.2011.00662.x
• Enders, W., & Lee, J. (2012b). The flexible Fourier form and Dickey–Fuller type unit root tests. Economics Letters, 117(1), 196–199. https://doi.org/10.1016/J.ECONLET.2012.04.081
• Erdem, E. (2014). Para Banka ve Finansal Sistem (6. Baskı). Detay Yayıncılık.
• Erdem, E., & İlgün, F. (2017). Mali disiplin üzerinde politik faktörlerin etkisi: Az gelişmiş ve gelişmekte olan ülkelere yönelik uygulamalı bir analiz. İktisat Fakültesi Mecmuası, 67(1), 1–23.
• Erdem, E., & Yucel, A. G. (2017). Does agricultural sector matter for business cycles? Evidence from Turkey. The Empirical Economics Letters, 16(11).
• Erdem, E., Yücel, A. G., & Köseoglu, A. (2016). Female Labour Force Participation and Economic Growth: Theoretical and Empirical Evidence. Içinde The Empirical Economics Letters (C. 15, Sayı 10). https://www.researchgate.net/publication/312586284
• Evans, P., & Karras, G. (1996). Convergence revisited. Journal of Monetary Economics, 37(2), 249–265. https://doi.org/10.1016/S0304-3932(96)90036-7
• Fatas, A., & Mihov, I. (2003). The case for restricting fiscal policy discretion. The Quarterly Journal of Economics, 118(4), 1419–1447. https://doi.org/10.1162/003355303322552838
• Feenstra, R. C., Inklaar, R., & Timmer, M. P. (2015). The Next Generation of the Penn World Table. American Economic Review, 105(10), 3150–3182. https://doi.org/10.1257/aer.20130954
• Ferreira-Tiryaki, G. (2008). The informal economy and business cycles. Journal of Applied Economics, 11(1), 91–117.
• Ferreira da Silva, G. (2002). The impact of financial system development on business cycles volatility: cross-country evidence. Journal of Macroeconomics, 24(2), 233–253. https://doi.org/10.1016/S0164-0704(02)00021-6
• Fiorito, R., & Kollintzas, T. (1994). Stylized facts of business cycles in the G7 from a real business cycles perspective. European Economic Review, 38(2), 235–269. https://doi.org/10.1016/0014-2921(94)90057-4
• Furceri, D., & Karras, G. (2007). Country size and business cycle volatility: Scale really matters. Journal of the Japanese and International Economies, 21(4), 424–434. https://doi.org/10.1016/J.JJIE.2007.04.001
• Gali, J., & Perotti, R. (2003). Fiscal policy and monetary integration in Europe. Economic Policy, 18(37), 533–572. https://doi.org/10.3386/w9773
• Gengenbach, C., Urbain, J.-P., & Westerlund, J. (2016). Error correction testing in panels with common stochastic trends. Journal of Applied Econometrics, 31(6), 982–1004. https://doi.org/10.1002/jae.2475
• Gong, C., & Kim, S. (2018). Regional business cycle synchronization in emerging and developing countries: Regional or global integration? Trade or financial integration? Journal of International Money and Finance, 84, 42–57. https://doi.org/10.1016/j.jimonfin.2018.02.006
• Greenwald, B. C., & Stiglitz, J. E. (1993). Financial market imperfections and business cycles. The Quarterly Journal of Economics, 108(1), 77–114. https://doi.org/10.2307/2118496
• Hadri, K. (2000). Testing for stationarity in heterogeneous panel data. The Econometrics Journal, 3(2), 148–161. https://doi.org/10.1111/1368-423X.00043
• Hadri, K., & Kurozumi, E. (2012). A simple panel stationarity test in the presence of serial correlation and a common factor. Economics Letters, 115(1), 31–34. https://doi.org/10.1016/J.ECONLET.2011.11.036
• Harvey, A. C., & Jaeger, A. (1993). Detrending, Stylized Facts and the Business Cycle. Journal of Applied Econometrics, 8(3), 231–247. http://ideas.repec.org/a/jae/japmet/v8y1993i3p231-47.html
• Hodrick, R. J., & Prescott, E. C. (1997). Postwar U.S. business cycles: An empirical investigation. Journal of Money, Credit and Banking, 29(1), 1–16.
• Hsiao, C. (2014). Analysis of panel data (3rd ed.). Cambridge University Press.
• Hurlin, C., & Valérie, M. (2007). Second Generation Panel Unit Root Tests. https://halshs.archives-ouvertes.fr/halshs-00159842/document
• Ibrahim, M., & Alagidede, P. (2017). Financial sector development, economic volatility and shocks in sub-Saharan Africa. Physica A: Statistical Mechanics and its Applications, 484, 66–81. https://doi.org/10.1016/J.PHYSA.2017.04.142
• Im, K. S., Lee, J., & Tieslau, M. (2005). Panel LM unit-root tests with level shifts. Oxford Bulletin of Economics and Statistics, 67(3), 393–419. https://doi.org/10.1111/j.1468-0084.2005.00125.x
• Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115(1), 53–74. https://doi.org/10.1016/S0304-4076(03)00092-7 IMF. (2005). World Economic Outlook: Building Institutions.
• Kao, C. (1999). Spurious regression and residual-based tests for cointegration in panel data. Journal of Econometrics, 90(1), 1–44. https://doi.org/10.1016/S0304-4076(98)00023-2
• Kapetanios, G., Shin, Y., & Snell, A. (2003). Testing for a unit root in the nonlinear STAR framework. Journal of Econometrics, 112(2), 359–379. https://doi.org/10.1016/S0304-4076(02)00202-6
• Karras, G., & Song, F. (1996). Sources of business-cycle volatility: An exploratory study on a sample of OECD countries. Journal of Macroeconomics, 18(4), 621–637. https://doi.org/10.1016/S0164-0704(96)80055-3
• King, R. G., & Rebelo, S. T. (1993). Low frequency filtering and real business cycles. Journal of Economic Dynamics and Control, 17(1–2), 207–231. https://doi.org/10.1016/S0165-1889(06)80010-2
• Kılınç, E. C., & Berberoğlu, C. N. (2018). Kar oranları konjonktür yönlü müdür? Akademik Araştırmalar ve Çalışmalar Dergisi (AKAD), 10(19), 606–621. https://doi.org/10.20990/kilisiibfakademik.441368
• Klomp, J., & de Haan, J. (2009). Political institutions and economic volatility. European Journal of Political Economy, 25(3), 311–326. https://doi.org/10.1016/J.EJPOLECO.2009.02.006
• Konstantakopoulou, I., & Tsionas, E. G. (2014). Half a century of empirical evidence of business cycles in OECD countries. Journal of Policy Modeling, 36(2), 389–409. https://doi.org/10.1016/j.jpolmod.2014.01.006
• Kose, M. A. (2002). Explaining business cycles in small open economies: ‘How much do world prices matter?’ Journal of International Economics, 56(2), 299–327. https://doi.org/10.1016/S0022-1996(01)00120-9
• Kose, M. A., Prasad, E. S., & Terrones, M. E. (2003). Financial integration and macroeconomic volatility. Içinde SSRN Electronic Journal (IMF Working Paper No. 03/50). https://doi.org/10.2139/ssrn.393420
• Kožić, I. (2014). Detecting international tourism demand growth cycles. Current Issues in Tourism, 17(5), 397–403. https://doi.org/10.1080/13683500.2013.808607
• Lane, P. R. (2003). The cyclical behaviour of fiscal policy: Evidence from the OECD. Journal of Public Economics, 87(12), 2661–2675. https://doi.org/10.1016/S0047-2727(02)00075-0
• Larsson, R., Lyhagen, J., & Löthgren, M. (2001). Likelihood‐based cointegration tests in heterogeneous panels. The Econometrics Journal, 4(1), 109–142. https://doi.org/10.1111/1368-423X.00059
• Lee, J., & Strazicich, M. C. (2003). Minimum Lagrange multiplier unit root test with two structural breaks. Review of Economics and Statistics, 85(4), 1082–1089. https://doi.org/10.1162/003465303772815961
• Levin, A., Lin, C.-F., & Chu, J. C.-S. (2002). Unit root tests in panel data: asymptotic and finite-sample properties. Journal of Econometrics, 108(1), 1–24. https://doi.org/10.1016/S0304-4076(01)00098-7
• Leybourne, S. J., C. Mills, T., & Newbold, P. (1998). Spurious rejections by Dickey–Fuller tests in the presence of a break under the null. Journal of Econometrics, 87(1), 191–203. https://doi.org/10.1016/S0304-4076(98)00014-1
• Lin, C.-P., Huang, C.-J., & Chuang, C.-M. (2018). Corruption and business cycle volatility: A corporate governance perspective. Asia-Pacific Journal of Accounting & Economics, 25(5), 586–606. https://doi.org/10.1080/16081625.2017.1378114
• 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
• Magazzino, C. (2016). Is per capita energy use stationary? Panel data evidence for the EMU countries. Energy Exploration & Exploitation, 34(3), 440–448. https://doi.org/10.1177/0144598716631666
• Malik, A., & Temple, J. R. W. (2009). The geography of output volatility. Journal of Development Economics, 90(2), 163–178. https://doi.org/10.1016/J.JDEVECO.2008.10.003
• Massmann, M., Mitchell, J., & Weale, M. (2003). Business cycles and turning points: A survey of statistical techniques. National Institute Economic Review, 183, 90–106.
• Mobarak, A. M. (2005). Democracy, volatility, and economic development. Review of Economics and Statistics, 87(2), 348–361. https://doi.org/10.1162/0034653053970302
• Montoya, L. A., & de Haan, J. (2008). Regional business cycle synchronization in Europe? International Economics and Economic Policy, 5(1–2), 123–137. https://doi.org/10.1007/s10368-008-0106-z
• Nazlioglu, S., & Karul, C. (2017). A panel stationarity test with gradual structural shifts: Re-investigate the international commodity price shocks. Economic Modelling, 61, 181–192. https://doi.org/10.1016/J.ECONMOD.2016.12.003
• O’Connell, P. G. J. (1998). The overvaluation of purchasing power parity. Journal of International Economics, 44(1), 1–19. https://doi.org/10.1016/S0022-1996(97)00017-2
• Omay, T. (2015). Fractional frequency flexible Fourier form to approximate smooth breaks in unit root testing. Economics Letters, 134, 123–126. https://doi.org/10.1016/J.ECONLET.2015.07.010
• 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
• Rubaszek, M., & Serwa, D. (2014). Determinants of credit to households: An approach using the life-cycle model. Economic Systems, 38(4), 572–587. https://doi.org/10.1016/J.ECOSYS.2014.05.004
• 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
• Senhadji, A. (1998). Time-series estimation of structural import demand equations: A cross-country analysis. Staff Papers - International Monetary Fund, 45(2), 236. https://doi.org/10.2307/3867390
• Shamim, F. (2006). International evidence on the role of financial sector in economic growth with less volatile business cycles. Forum of International Development Studies, 31, 233–251.
• Şimşek, T. (2015). Politik istikrarsızlık çerçevesinde politika ve iktisat etkileşimi. Uluslararası Yönetim, Eğitim ve Ekonomik Perspektifler Dergisi, 3(2), 39–54.
• Smith, L. V., Leybourne, S., Kim, T.-H., & Newbold, P. (2004). More powerful panel data unit root tests with an application to mean reversion in real exchange rates. Journal of Applied Econometrics, 19(2), 147–170. https://doi.org/10.1002/jae.723
• Tang, S. H. K. (2018). Does scientific and technical research reduce macroeconomic volatility? Bulletin of Economic Research, 70(1), 68–88. https://doi.org/10.1111/boer.12129
• Tiryaki, G. F. (2003). Financial development and economic fluctuations. METU Studies in Development, 30(1), 89–106.
• Ulucak, R., Yücel, A. G., & İlkay, S. Ç. (2020). Dynamics of tourism demand in Turkey: Panel data analysis using gravity model. Tourism Economics, 135481662090195. https://doi.org/10.1177/1354816620901956
• Westerlund, J. (2005). A panel CUSUM test of the null of cointegration. Oxford Bulletin of Economics and Statistics, 67(2), 231–262. https://doi.org/10.1111/j.1468-0084.2004.00118.x
• 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
• Yang, B. (2008). Does democracy lower growth volatility? A dynamic panel analysis. Journal of Macroeconomics, 30(1), 562–574. https://doi.org/10.1016/J.JMACRO.2007.02.005
• Zivot, E., & Andrews, D. W. K. (1992). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business & Economic Statistics, 10(3), 251. https://doi.org/10.2307/1391541
• Zouaoui, H., Mazioud, M., & Ellouz, N. Z. (2018). A semi-parametric panel data analysis on financial development-economic volatility nexus in developing countries. Economics Letters, 172, 50–55. https://doi.org/10.1016/J.ECONLET.2018.08.010