Journals / Uluslararası Ekonomi İşletme ve Politika Dergisi / 2024 / Cilt: 8 Sayı: 2

Investigation of the Connectedness Between Loans Provided to SMEs and Selected Variables in Turkey Using the TVP-VAR Model

Türkiye’de KOBİ’lere Verilen Krediler ile Seçilmiş Değişkenler Arasındaki Bağlantılılığın TVP-VAR Modeli ile İncelenmesi

Yalçın Yalman(SİVAS CUMHURİYET ÜNİVERSİTESİ)0000-0002-4340-7068
Bahri Fatih Tekin(SİVAS CUMHURİYET ÜNİVERSİTESİ)0000-0002-0541-4371
Pages
442–459
Publication Date
2024-10-25
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

According to the results, it is determined that the amount of credit to SMEs is one of the variables that receives shocks in net total, while the amount of imports is the variable that gives the highest shock in the model.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In both developed and developing countries, SMEs are the cornerstones of economic development and stability with the employment they provide, the value added they create and the export and import activities they realize.
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Key Findings & Data:In Turkey, SMEs have an important place in terms of number of enterprises and employment.
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Conclusion & Impact:According to the results, it is determined that the amount of credit to SMEs is one of the variables that receives shocks in net total, while the amount of imports is the variable that gives the highest shock in the model.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:KOBİ Kredileri · Bağlantılılık Yaklaşımı · TVP-VAR

Abstract

In both developed and developing countries, SMEs are the cornerstones of economic development and stability with the employment they provide, the value added they create and the export and import activities they realize. In Turkey, SMEs have an important place in terms of number of enterprises and employment. In-depth analysis of the contributions and dynamics of these enterprises to the economy is of great importance for developing more effective economic policies and improving the existing system. The aim of this study is to examine the spillovers between selected economic indicators and the amount of credit extended to SMEs and the total, bilateral, net spillovers and network connections between all variables in the model through static and dynamic processes. Based on the connectedness approach, the TVP-VAR model is used and static and dynamic results are reported. According to the results, it is determined that the amount of credit to SMEs is one of the variables that receives shocks in net total, while the amount of imports is the variable that gives the highest shock in the model. It is also concluded that variables other than loans to SMEs have significant spillovers among themselves.

Özet

Hem gelişmiş hem de gelişmekte olan ülkelerde KOBİ'ler, sağladıkları istihdam, yarattıkları katma değer ve gerçekleştirdikleri ihracat ve ithalat faaliyetleri ile ekonomik kalkınmanın ve istikrarın temel taşlarını oluşturmaktadır. Türkiye'de de KOBİ'ler, işletme sayısı ve istihdam açısından önemli bir yer tutmaktadır. Bu işletmelerin ekonomiye olan katkılarının ve dinamiklerinin derinlemesine incelenmesi, daha etkin ekonomik politikaların geliştirilmesi ve var olan sistemin iyileştirilmesi açısından büyük önem taşımaktadır. Bu çalışmanın amacı, seçilen ekonomik göstergeler ile KOBİ'lere verilen kredi tutarı arasındaki yayılımları ve aynı zamanda oluşturulan modeldeki tüm değişkenlerin birbiri arasındaki toplam, ikili, net yayılımları ve ağ bağlantılarını statik ve dinamik süreçler ile ortaya koymaktır. Bağlantılılık yaklaşımı temelinde TVP-VAR modeli kullanılmış olup statik ve dinamik sonuçlar raporlanmıştır. Bulunan sonuçlara göre KOBİ’lere verilen kredi tutarının net toplamda şok alan değişkenlerden biri olduğu, ithalat tutarının ise oluşturulan model içerisinde en fazla şok veren değişken olduğu tespit edilmiştir. KOBİ’lere verilen krediler dışındaki değişkenlerin kendi aralarında da önemli yayılımların gerçekleştiği sonucuna ulaşılmıştır.

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Cite This Article

Yalman, Y., Tekin, B. F. (2024). Investigation of the Connectedness Between Loans Provided to SMEs and Selected Variables in Turkey Using the TVP-VAR Model. Uluslararası Ekonomi İşletme ve Politika Dergisi, Cilt: 8 Sayı: 2, 442–459. https://doi.org/10.29216/ueip.1518676

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References (24)

  1. Ahmed, S. and Murthy, R. (2021). Dynamic Effects of Macroeconomic Fluctuations Using TVP-VAR Models. Journal of Economic Studies, 48(4), 340-360. Access Address: https://doi.org/10.1108/JES-03-2021-0110
  2. Anscombe, F. J. and Glynn, W. J. (1983). Distribution of The Kurtosis Statistic B2 for Normal Samples. Biometrika, 70(1), 227-234. Access Address: https://doi.org/10.1093/biomet/70.1.227
  3. Antonakakis, N. Chatziantoniou, I., and Gabauer, D. (2020). Refined Measures of Dynamic Connectedness Based On Time-Varying Parameter Vector Autoregressions. Journal of Risk and Financial Management, 13(84), 1-23. Access Address: https://doi.org/10.3390/jrfm13040084
  4. Asomaning, R. and Hamayoon, A. (2021). A TVP-VAR Assessment of the Spillover Effects of Geopolitical Risk Shocks On Macroeconomic Variability: A Study of the Ghanaian Economy. Future Business Journal, 7(1), 35-56. Access Address: https://doi.org/10.1186/s43093-021-00052-8
  5. Blanchard, O. J. and Summers, L. H. (1986). Hysteresis and the European unemployment problem. NBER Macroeconomics Annual, 1, 15-78. Access Address: https://doi.org/10.3386/w1950
  6. Cogley, T. and Sargent, T. J. (2005). Drift and Volatilities: Monetary Policies and Outcomes in The Post WWII US. Review of Economic Dynamics, 8(2), 262-302. Access Address: https://doi.org/10.1016/j.red.2004.10.009
  7. D'Agostino, R. B. (1970). Transformation to Normality of the Null Distribution of G1. Biometrika, 57(3), 679-681. https://doi.org/10.1093/biomet/57.3.679
  8. Del Negro, M. and Schorfheide, F. (2011). Bayesian Macroeconometrics. In J. Geweke, G. Koop, ve H. van Dijk (Eds.), The Oxford Handbook of Bayesian Econometrics (pp. 293-389). Oxford University Press. Access Address: https://doi.org/10.1093/oxfordhb/9780199559084.013.0010
  9. Diebold, F. X. and Yilmaz, K. (2012). Better to Give Than to Receive: Predictive Directional Measurement of Volatility Spillovers. International Journal of Forecasting, 28(1), 57-66. Access Address: https://doi.org/10.1016/j.ijforecast.2011.02.006
  10. Diebold, F. X. and Yilmaz, K. (2014). On The Network Topology of Variance Decompositions: Measuring The Connectedness of Financial Firms. Journal of Econometrics, 182(1), 119-134. Access Address: https://doi.org/10.1016/j.jeconom.2014.04.012
  11. Elliott, G. Rothenberg, T. J. and Stock, J. H. (1996). Efficient Tests for An Autoregressive Unit Root. Econometrica, 64(4), 813-836. Access Address: https://doi.org/10.2307/2171846
  12. European Commission. (2023). Annual report on European SMEs. Publications Office of the European Union. Access Address: https://doi.org/10.2873/789352
  13. Fisher, T. J. and Gallagher, C. M. (2012). New Weighted Portmanteau Statistics for Time Series Goodness of Fit Testing. Journal of the American Statistical Association, 107(498), 777-787. Access Address: https://doi.org/10.1080/01621459.2012.688463
  14. Jarque, C. M. and Bera, A. K. (1980). Efficient Tests for Normality, Homoscedasticity and Serial Independence of Regression Residuals. Economics Letters, 6(3), 255-259. Access Address: https://doi.org/10.1016/0165-1765(80)90024-5
  15. Koop, G. Pesaran, M. H. and Potter, S. M. (1996). Impulse Response Analysis in Nonlinear Multivariate Models. Journal of Econometrics, 74(1), 119-147. Access Address: https://doi.org/10.1016/0304-4076(95)01753-4
  16. Kumar, S. and Pradhan, R. (2023). Dynamic Connectedness of Macro-Financial Variables: Evidence from Emerging Markets Using TVP-VAR Model. Journal of Economic Studies, 50(2), 219-240. Access Address: https://doi.org/10.1108/JES-09-2022-0392.
  17. Li, H. and Sun, W. (2021). Time-Varying Impact of Trade Shocks on Macroeconomic Variables in Asia: A TVP-VAR Approach. Asian Economic Journal, 35(3), 299-320. Access Address: https://doi.org/10.1111/asej.12258.
  18. Lütkepohl, H. and Krätzig, M. (2004). Applied Time Series Econometrics. Cambridge University Press. Access Address: https://doi.org/10.1017/CBO9780511606885
  19. Nakajima, J. Kasuya, M. and Watanabe, T. (2011). Bayesian Analysis of Time-Varying Parameter Vector Autoregressive Model for The Japanese Economy and Monetary Policy. Journal of the Japanese and International Economies, 25(3), 225-245. Access Address: https://doi.org/10.1016/j.jjie.2011.04.001
  20. Pesaran, H. H. and Shin, Y. (1998). Generalized Impulse Response Analysis in Linear Multivariate Models. Economics Letters, 58(1), 17-29. Access Address: https://doi.org/10.1016/S0165-1765(97)00214-0
  21. Primiceri, G. E. (2005). Time Varying Structural Vector Autoregressions and Monetary Policy. The Review of Economic Studies, 72(3), 821-852. Access Address: https://doi.org/10.1111/j.1467-937X.2005.00353.x
  22. Stock, J. H. and Watson, M. W. (1996). Evidence On Structural Instability in Macroeconomic Time Series Relations. Journal of Business and Economic Statistics, 14(1), 11-30. Access Address: https://doi.org/10.1080/07350015.1996.10524620
  23. TUİK. (2024). Türkiye İstatistik Kurumu. Erişim Adresi: https://data.tuik.gov.tr
  24. U.S. Small Business Administration. (2023). Small Business Profile. SBA Office of Advocacy. Access Address: https://doi.org/10.1002/jgrd.20414