Türk Ekonomisinde Cari İşlemler Dengesinin NARX ile Analizi: Doğrusal Olmayan Bir Yaklaşım

Year 2022, , 1 - 14, 30.06.2022

Tayfun Tuncay Tosun

https://doi.org/10.30711/utead.1081318

Cited By: 1

Abstract

This paper reveals that the current account balance is a crucial macroeconomic performance indicator in the Turkish economy. For this purpose, this paper employs multiple independent variables that penetrate the macroeconomy significantly for the empirical analysis of the Turkey’s current account balance. NARX Artificial Neural Network, a robust and non-linear statistical method, is used for the empirical analysis in this study. The outcome of the analysis demonstrates that over 90% of the current account balance can be explained by multiple independent variables, which have a significant impact on the macroeconomy. This empirical finding denotes that the current account balance is strongly related to a complex and multiple set of macroeconomic variables. Consequently, considering the current account balance as a critical macroeconomic performance indicator is crucial for the Turkish economy. In this context, the performance evaluation of the political-economic system in Turkey should be based on the current account balance.

Keywords

NARX, Artificial Neural Network, Current Account Balance, Performance Indicator

Analysis of the Current Account Balance in the Turkish Economy with NARX: A Non-linear Approach

Abstract

This paper reveals that the current account balance is a crucial macroeconomic performance indicator in the Turkish economy. For this purpose, this paper employs multiple independent variables that penetrate the macroeconomy significantly for the empirical analysis of Turkey’s current account balance. NARX Artificial Neural Network, a robust and non-linear statistical method, is used for the empirical analysis in this study. The outcome of the analysis demonstrates that over 90% of the current account balance can be explained by multiple independent variables, which have a significant impact on the macroeconomy. This empirical finding denotes that the current account balance is strongly related to a complex and multiple set of macroeconomic variables. Consequently, considering the current account balance as a critical macroeconomic performance indicator is crucial for the Turkish economy. In this context, the performance evaluation of the political-economic system in Turkey should be based on the current account balance.

Keywords

NARX, Artificial Neural Network, Current Account Balance, Performance Indicator

References

• Alexander, S.S. (1952). “Effects of A Devaluation on a Trade Balance”, International Monetary Fund Staff Papers, 2, 263–278.
• Aimon, H., Kurniadi A.P., and Sentosa S.U. (2020). “Determinants and Causality of Current Account Balance and Foreign Direct Investment: Lower Middle-Income Countries in ASEAN” in 3rd International Research Conference on Economics and Business, KnE Social Sciences, 10-22. DOI 10.18502/kss.v4i7.6839.
• Allende, H., Moraga C., and Salas, R. (2002). Artificial Neural Networks in Time Series Forecasting: A Comparative Analysis”, Kybernetika, 38(6), 685-707.
• Aristovnik, A. (2007). ‘’Short and Medium Term Determinants of Current Account Balances in Middle East and North Africa Countries’’, William Davidson Institute Working Paper, No. 862.
• Belke, A. and Dreger C. (2011). ‘’Current Account Imbalances in the Euro Area: Catching Up or Competitiveness?’’, German Institute for Economic Research, No. 1106.
• Benli, A., and Tonus Ö. (2019). “Türkiye Ekonomisinde Cari İşlemler Açığının Belirleyicileri: Dönemler Arası Yaklaşım”, Anadolu Üniversitesi Sosyal Bilimler Üniversitesi, 19(3), 437-460.
• Bickerdike, C.F. (1920). “The Instability of Foreign Exchange,” Economic Journal, 30, 118-122.
• Bollano, J., and Ibrahimaj D. (2015). “ Current Account Determinants in Central Eastern European Countries”, Graduate Institute Geneva Working Paper, No. HEIDWP0022-2015.
• Calderon, C., Chong C., and Loayza N. (2001). “Are African Current Account Deficits Different? Stylized Facts, Transitory Shocks, and Decomposition Analysis”, World Bank Working Paper Series, No. WP/01/04.
• Canıdemir, S., Uslu R., Ekici D., and Yarat M. (2011). “Türkiye’de Cari Açığın Yapısal ve Dönemsel Belirleyicileri”, Ekonomik Yaklaşım Kongreler Dizisi VII Gazi Üniversiyesi Ankara.
• Chinn, M.D., and Ito H. (2003). “Current Account Balances, Financial Development and Institutions: Assaying the World “Saving Glut”, Sorthcoming in the Journal of International Finance and Money.
• Chinn, M.D., and Prasad E.S. (2003). “Medium-term Determinants of Current Accounts in Industrial and Developing Countries: An Empirical Exploration”, Journal of International Economics, 59, 47-76.
• Chaudhuri, T.D., and Ghosh I. (2016). “Artificial Neural Network and Time Series Modeling Based Approach to Forecasting the Exchange Rate in A Multivariate Framework. Journal of Insurance and Financial Management, 1(5), 92-123.
• Chuku, C., Atan J., Obioesio F., and Onye K. (2017). “Current Account Adjustments and Integration in West Africa”, African Development Bank Group Working Paper Series, No. 287.
• Dam, M.M., Göçer İ., Bulut Ş., and Mercan M. (2012). “Determinants of Turkey Current Account Deficit: An Econometric Analysis”, 3rd International Symposium on Sustainable Development, Sarajevo; 111–122.
• Debelle, G., and Farugee H. (1996). ‘’What Determines the Current Account?’’, IMF Working Paper, No. 058.
• Diaconescu, E. (2008). “The Use of NARX Neural Networks to Predict Chaotic Time Series” WSEAS Transactions on Computer Research, 3(3), 182-191.
• Erbaykal, E. (2007). ‘’ Türkiye’de Ekonomik Büyüme ve Döviz Kuru Cari Açık Üzerinde Etkili midir? Bir Nedensellik Analizi’’, ZKÜ Sosyal Bilimler Dergisi, 3(6), 81–88.
• Frenkel, J.A., and Johnson H.G. (eds) (1976). “The Monetary Approach to the Balance of Payments”, London: Allen and Unwin.
• Gao, Y.E., and Meng J. (2005). “NARMAX Time Series Model Prediction: Feedforward and Recurrent Fuzzy Neural Network Approach”, Fuzzy Sets and Systems, 150(2), 331-350.
• Gavin P. H. (2020), ‘’The Levenberg-Marquardt Algorithm for Nonlinear Least Squares Curve-Fitting Problems’’, Department of Civil and Environmental Engineering Duke University. https://people.duke.edu/~hpgavin/ce281/lm.pdf (Acessed on 28 February 2022).
• Göçer, İ. (2013). ‘’Türkiye’de Cari Açığın Nedenleri, Finansman Kalitesi ve Sürdürülebilirliği: Ekonometrik Bir Analiz’’, Eskişehir Osmangazi Üniversitesi İİBF Dergisi, 8(1), 213-242.
• İyidoğan, P.V., and Erkam S. (2013). ‘’İkiz Açıklar Hipotezi: Türkiye için Amprik Bir İnceleme (1987-2005)’’, Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 15(2013), 39-48.
• Ketenci, N., and Uz I. (2010). “Determinants of Current Account in the EU: The Relation Between Internal and External Balances in the New Members”, MRPA Working Paper, No.27466.
• Lin, T., Horne B.G., Tino P., and Giles L.C. (1996). “Learning Long-term Dependencies in NARX Recurrent Neural Networks. IEEE Transactions on Neural Network, 7(6), 1329-1351.
• Lobo, A.L.M., Osorio G.A., Yau L.J.R., Cisnero O.S., Moreno P. (2014). “A Digital Predistortion Technique Based on a NARX Network to Linearize Gan Class F Power Amplifiers. 2014 IEEE 57th International Midwest Symposium on Circuits and Systems (MWSCAS). DOI: 10.1109/MWSCAS.2014.6908515.
• Mahmud, S.F., Ullah A., and Yücel E.M. (2004). “Testing Marshall-Lerner Condition: A Non-parametric Approach”, Applied Economics Letters, 11, 231-236.
• Masters, T. (1993). “Practical Neural Network Recipes in C++”, Toronto: Academic Press.
• Menezes, J.M.P., and Barretuilharme A. (2008). Long-term Time Series Prediction with the NARX Network: An Empirical Evaluation. Neurocomputing, 71(16-18), 3335-3343.
• Metzler, L. (1948). “A Survey of Contemporary Economics”, Vol. I, Homewood: IL Richard D. Irwin.
• Milesi, G.M. and Razin F.A. (1998). ‘’Current Account Reversals and Currency Crises: Empirical Regularities’’, IMF Working Paper, No. WP/98/89.
• Obstfeld, M., and Rogoff K. (1994). “The Intertemporal Approach to the Current Account”, NBER Working Paper Series, No. 4893.
• Obstfeld, M., and Rogoff K. (1996). “Foundations of International Macroeconomics”, Cambridge: MIT Press MA.
• Pawlak, K.K., and Muck J. (2019). “Structural Current Account Benchmarks for the European Union Countries: Cross-section Exploration”, NBP Working Paper, No. 320.
• Peker, O., and Hotunluoğlu H. (2009). “Türkiye’de Cari Açığın Nedenlerinin Ekonometrik Analizi”, Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 23(3), 221-237.
• Petrasek, L. (2005). “The Determinants of Current Account Dynamics in the Medium Run: An International Approach”,https://corescholar.libraries.wright.edu/econ_student/40 (Accessed on 28, February 2022).
• Phillips, S., Catão L., Ricci L., Bems R., Das M., Di Gionanni J., Unsal D.F., Castillo M., Lee J., Rodriguez J., Vargas M. (2013). “The External Balance Assessment (EBA) Methodology. International Monetary Fund Working Paper, No. 13/272.
• Riaz, F., Javid A.Y., and Mubarik F. (2019). "Macroeconomic Determinants of Current Account in South-Asian Countries." Paradigms, 13(1), 106-112.
• Robinson, J. (1947). “The Foreign Exchanges”, Essays in the Theory of Employment, Oxford: Basil Blackwell.
• Sachs, J.D. (1981). “The Current Account and Macroeconomic Adjustment in the 1970s”, Brookings Papers on Economic Activity, 201-268. https://doi.org/10.2307/2534399.
• Saksonovs, S. (2006). ‘’The Intertemporal Approach to the Current Account and Currency Crises’’, Cambridge University United Kingdom CB3 9EU Darwin College Research Report, No. DCRR-005.
• Seyidoğlu, H. (2017). ‘’Uluslararası İktisat’’, İstanbul: Güzemcan Yayınları.
• Thirlwall, A.P. (1980). ‘’The Absorption Approach to the Balance of Payments. In: Balance-of-Payments Theory and the United Kingdom Experience’’, London: Palgrave.
• Tosun, T.T. (2020). “Türk Ekonomisinde Cari İşlemler Hesabının Belirleyicilerinin Yapay Sinir Ağı ile Analizi”, İstanbul Ticaret Üniversitesi Working Paper Series, No. 248.
• Turan, T., and Afsal M.Ş. (2020). “Türkiye’de Cari Açığın Belirleyicileri: Amprik Bir Analiz”, Finans Politik & Ekonomik Yorumlar, 651, 217-236.
• Williamson, J. (1994). “Estimates of FEERs. [in:] J. Williamson (ed.), Estimating Equilibrium Exchange Rates”, Washington: Institute for International Economics.
• Wilamowski, B.M., Chen Y. (1999). “Efficient Algorithm for Training Neural Networks with One Hidden Layer. In Proc. of the International Joint Conference on Neural Networks, 3, 1725-1728.
• Yang, L. (2011). ‘’An Empirical Analysis of the Currrent Account Determinants in Emerging Asian Economies’’, Cardiff Economics Working Papers, No. E2011/10.
• Yu, X., Zhuang C., and Longxing Q. (2019). “Comparative Study of SARIMA and NARX Models in Predicting the Incidence of Schistosomiasis in China”, Mathematical Biosciences and Engineering, 16(4), 2266-2276.