DOI :10.26650/B/SS10.2023.001.08   IUP :10.26650/B/SS10.2023.001.08    Full Text (PDF)

Regional Analysis of Inflation Convergence in Turkiye with a Nonlinear Approach

Ahmet İncekaraYeşim Erönal

Inflation affects a country's consumption level, imports, exports, and trade, while significantly impacting the entire region's social welfare. Therefore, the convergence of inflation levels in different areas is essential for the macroeconomic development of a country. This study aims to analyze the existence of inter-regional inflation convergence by using data from 26 regions of Turkey in NUTS Level 2. Along with the general inflation rates, the convergence of the inflation rates in goods and services groups such as food and nonalcoholic beverages, alcoholic beverages and tobacco, housing, water, electricity, gas, health, and transportation are among the CPI indicators according to the main expenditure groups released by TurkStat, to the general inflation rates were investigated. In the first stage of the study, spatial statistics methods of Moran I and LISA statistics were applied to the CPI and goods and services groups for the years 2005, 2010, 2015, 2019, and 2020, and the inflation heterogeneity of the NUTS Level 2 regions was visualized with the obtained data. In the next stage, the existence of inflation convergence between regions between 2003:01-2020:07 was empirically tested. In this respect, after examining the linearity structure of the inflation series according to areas with the Harvey, Leybourne, and Xiao (2008) test, ADF and LS tests were applied for the series that exhibit a linear form, while Hepsağ (2019) nonlinear unit root was applied for the nonlinear structured series. The findings obtained from the tests show that the inflation rates of 20 regions in NUTS Level 2 converge with the general inflation rates.


  • Anselin, L. (1988). Spatial econometrics: methods and models (Vol. 4). Springer Science & Business Media. Anselin, Luc. (1995). “Local Indicators of Spatial Association — LISA.” Geographical Analysis 27: 93-115. Anselin, L. (2001). Spatial econometrics. A companion to theoretical econometrics, 310330. google scholar
  • Anselin, L. (2021). GeoDa (Spatial Statistical Program). The Encyclopedia of Research Methods in Criminology and Criminal Justice, 2, 839-841. google scholar
  • Arbia, G., Battisti, M., & Di Vaio, G. (2010). Institutions and geography: an empirical test of spatial growth models for European regions. Economic modeling, 27(1), 12-21. google scholar
  • Barro, R. J., Xavier Sala-I-Martin, Blanchard, O. J., & Hall, R. E. (1991). Convergence across states and regions. Brookings Papers on Economic Activity, 1991(1), 107-182. google scholar
  • Barro, Robert J., & Xavier Sala-i Martin. (1992). Convergence. Journal of Political Economy 100(2): 223-251. google scholar
  • Baumol, W. J. (1986). Productivity growth, convergence, and welfare: what the long-run data show. The American Economic Review, 1072-1085. google scholar
  • Belke, M. & Al, İ. (2019). Türkiye’de bölgesel enflasyon yakınsaması: panel birim kök testlerinden kanıtlar. Uluslararası Ekonomi ve Yenilik Dergisi , 5 (2) , 301-323 . DOI: 10.20979/ueyd.601832 google scholar
  • Bozkurt, İ. & Karakuş, R. (2020). Provincial financial inclusion in Turkey: measurement and its spatial determinants. Ege Academic Review, 20 (2), 101-124. DOI: 10.21121/eab.729532 google scholar
  • Busetti, F., Forni, L., Harvey, A., & Venditti, F. (2006). Inflation convergence and divergence within the European Monetary Union. ECB Working Paper No. 574, Available at SSRN: google scholar
  • Ciccone, A. (2002). Agglomeration effects in Europe. European Economic Review, 46(2), 213-227. https://doi. org/10.1016/S0014-2921(00)00099-4 1 google scholar
  • Cliff, A. & J. Ord. (1973). Spatial Autocorrelation. London: Pion. google scholar
  • Çelikel Yiğiter, S. (2019). İstatistiki bölge birimleri sınıflaması düzey 2 bölgelerinde iş kazalarının değerlendirilmesi . İSG Akademik , 1 (1) , 1-11 . Retrieved from issue/51374/654458 google scholar
  • Çiğdem, G., & Altaylar, M. (2021). Nonlinear relationship between economic growth and tax revenue in Turkey: Hidden cointegration approach. İstanbul İktisat Dergisi - Istanbul Journal of Economics, 71(1), 21-38. google scholar
  • Dickey, D.A. & Fuller, W.A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427-431. google scholar
  • Duran, H. E. (2015): Regional inflation convergence in Turkey, Discussion Paper, No. 2015/10, Turkish Economic Association, Ankara google scholar
  • Geary, R. C. (1954). The contiguity ratio and statistical mapping. The Incorporated Statistician, 5(3), 115-146. google scholar
  • Getis, A. (1999). Spatial statistics. Geographical information systems, 1, 239-251. google scholar
  • Getis, A., and J. K. Ord (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis 24 (July), 189-206. google scholar
  • Giacomini, R., & Granger, C. W. (2004). Aggregation of space-time processes. Journal of econometrics, 118(1-2), 7-26. google scholar
  • Gujarati, D. (2015). Econometrics by example (2. ed.). London, United Kingdom: Macmillan International Higher Education. google scholar
  • Güriş, S., Çağlayan A., E., Bülbül, H. (2020). Enflasyon yakınsamasının fourier birim kök testleri ile incelenmesi: kırılgan beşli örneği. Social Sciences Research Journal, 9 (3), 85-92. google scholar
  • Harvey, D. I., Leybourne, S. J., & Xiao, B. (2008). A robust test for linearity when the order of integration is unknown. Studies in Nonlinear Dynamics & Econometrics, 12(3). google scholar
  • Hepsağ, A. (2017). Inflation convergence among the next eleven economies: evidence from asymmetric nonlinear unit root test. Theoretical & Applied Economics. 24(4). google scholar
  • Hepsağ, A. (2019). A unit root test based on smooth transitions and nonlinear adjustment. Communications in Statistics - Simulation and Computation. doi:10.1080/03610918.2018.1563154 google scholar
  • Karahasan, Burhan C. 2010. “Dynamics and Variation of Regional Firm Formation-Case of Turkey.” Ph.D. dissertation, Marmara University. google scholar
  • Kneissel, J., Huyssen, A., & Moore, J. (1974). The convergence theory: the debate in the Federal Republic of Germany. New German Critique, (2), 16-27. google scholar
  • Kruse R. (2011). A new unit root test against estar based on a class of modified statistics. Statistical Papers, 52, 71-85. google scholar
  • Kocenda, E., & Papell, D.H. (1996). Inflation convergence within the European Union: a panel data analysis. European Economics: Macroeconomics & Monetary Economics eJournal. google scholar
  • 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. google scholar
  • Lee, J., & Strazicich, M. C. (2004). Minimum LM unit root test with one structural break. Appalachian State University Working Papers. No.04-17: 1-15. google scholar
  • LeSage, James P. (2008). An Introduction to Spatial Econometrics , Revue d’économie industrielle [Online], document 4, URL : ; DOI : 10.4000/rei.3887 google scholar
  • LeSage, J., & Pace, R. K. (2009). Introduction to spatial econometrics. Chapman and Hall/CRC. https://doi. org/10.1201/9781420064254 google scholar
  • Leybourne, S., Newbold, P., & Vougas, D. (1998). Unit roots and smooth transitions. Journal of time series analysis, 19(1), 83-97. google scholar
  • Liontakis, A., & Kremmydas, D. (2014). Food Inflation in the European Union: Distribution Analysis and Spatial Effects. Geographical Analysis, 46(2), 148-164. google scholar
  • Liu, T. Y., & Lee, C. C. (2021). Global convergence of inflation rates. The North American Journal of Economics and Finance, 58, 101501. google scholar
  • Mankiw, N. G., Romer, D., & Weil, D. N. (1992). A Contribution to the Empirics of Economic Growth, The Quarterly Journal of Economics, Volume 107, Issue 2, May 1992, Pages 407-437, https://doi. org/10.2307/2118477 google scholar
  • Mentz, M., & Sebastian, S. P. (2003). Inflation convergence after the introduction of the Euro. Available at SSRN 460820. google scholar
  • Moran, P. A. P. (1950). Notes on Continuous Stochastic Phenomena. Biometrika, 37(1/2), 17—23. https://doi. org/10.2307/2332142 google scholar
  • Nagayasu, Jun, (2010). “Regional Inflation (Price) Behaviors: Heterogeneity and Convergence,” MPRA Paper 25430, University Library of Munich, Germany. google scholar
  • Quah, D. T. (1996). Empirics for economic growth and convergence. European economic review, 40(6), 13531375. google scholar
  • Özmen, M., & Baktemur, F. İ. (2015). Enflasyon Yakınsamasının Mekansal Ekonometrik Analizi. Sosyal Bilimler Araştırma Dergisi, 4(2), 187-194. google scholar
  • Perron, P. (1989). The great crash, the oil price shock, and the unit root hypothesis. Econometrica: Journal of the Econometric Society, 1361-1401. google scholar
  • Rey, S. J., & Montouri, B. D. (1999). US regional income convergence: a spatial econometric perspective. Regional Studies, 33(2), 143-156. google scholar
  • Solow, R. M. (1956). A contribution to the theory of economic growth. The quarterly journal of economics, 70(1), 65-94. google scholar
  • Temiz, M., Konat, G. (2019). Euro bölgesi ülkeleri için enflasyon yakınsamasının panel birim kök testi ile incelenmesi, İşletme Araştırmaları Dergisi, 11 (3), 2333-2337. google scholar
  • T.C. Sanayi Ve Teknoloji Bakanlığı. (2019a). İllerin Ve Bölgelerin Sosyo-Ekonomik Gelişmişlik Sıralaması Araştırması SEGE-2017, Hazırlayanlar: Salih Acar, Mustafa Caner Meydan, Leyla Bilen Kazancık Ve Mustafa Işık, Ankara: Kalkınma Ajansları Genel Müdürlüğü Yayını Sayı: 3, Araştırma Raporu Sayı: 3, Aralık 2019. google scholar
  • Tıraşoğlu, M., & Yurttagüler, İ. M. (2018). Inflation convergence in BRICS countries: a comprehensive unit root test analysis. Alphanumeric Journal, 6(2), 311-324. google scholar
  • Tinbergen, Jan, (1959). “ The Theory of the Optimum Regime .” Selected Papers. Amsterdam: North Holland. google scholar
  • Tobler, W. R. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46: 234-40. google scholar
  • Tunay, K. B. & Silpagar, A. M. (2007). Dinamik mekan-zaman panel veri modelleriyle Türkiye’de bölgesel enflasyon yakinsamasinin analizi . Gazi Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi , 9 (1) , 1-27 . Retrieved from google scholar
  • Tura-Gawron, K., & Szyszko, M. (2018). Spatial Approach to Heterogeneity of Inflation Expectations in the Euro Area. In Proceedings of Economics and Finance Conferences (No. 6910174). International Institute of Social and Economic Sciences. google scholar
  • Yerdelen Tatoğlu, F. (2018). İleri panel veri analizi. Basım. İstanbul: Beta Yayıncılık. google scholar
  • Yeşilyurt, F. (2014). Bölgesel enflasyon yakınsaması: Türkiye örneği. Ege Akademik Bakış, 14(2), 305-314. google scholar
  • Yılancı, V. & Tıraşoğlu, M. (2016). Türkiye’nin makroekonomik zaman serilerinin doğrusallığının testi. Çankırı Karatekin Üniversitesi İİBF Dergisi, 6(2), 1-16. google scholar
  • Yılmazkuday, H. (2009) “Inflation Targeting and Inflation Convergence within Turkey” University Library of Munich MPRA Paper, No:16770. google scholar
  • Yilmazkuday, H. (2021). Inflation Convergence over Time: Sector-Level Evidence within Europe. Available at SSRN: or google scholar


Istanbul University Press aims to contribute to the dissemination of ever growing scientific knowledge through publication of high quality scientific journals and books in accordance with the international publishing standards and ethics. Istanbul University Press follows an open access, non-commercial, scholarly publishing.