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THE IMPACT OF MACROECONOMIC INDICATORS ON CREDIT VOLUME IN TURKEY: A GENERALIZED ADDITIVE MODEL (GAM) PERSPECTIVE

Yıl 2025, Cilt: 15 Sayı: 29, 122 - 143, 26.06.2025

Öz

This study examines the relationship between total credit volume and macroeconomic indicators in Turkey using the Generalized Additive Model (GAM). The research analyzes the effects of key macroeconomic indicators most commonly used in literature, such as interest rates, exchange rates, money supply, inflation, and real sector confidence index, on credit volume. Analysis of the GAM results emphasizes that these variables demonstrate significant and predominantly non-linear effects on credit volume. Notably, money supply (M1), consumer price index (CPI), and commercial credit interest rate (CCIR) variables exhibited the strongest effects on credit interest. The model's high predictive performance is evident, explaining 99.9% of the dependent variable's variance. The study reveals the complex and non-linear nature of macroeconomic indicators' effects on credit volume and demonstrates that the GAM model is a highly effective tool for analyzing such relationships. The findings underscore the importance of non-linear relationships in economic analyses while providing policymakers with a more comprehensive assessment. For future research, it is recommended to expand the analyses using different data analysis techniques and datasets.

Kaynakça

  • Akşehirli, N., ve Karahan, Ö. (2023). Türkiye'de Banka Kredilerinin Makro ve Mikro Belirleyicileri. JOEEP: Journal of Emerging Economies and Policy, 8(2), 179–195. https://dergipark.org.tr/tr/pub/joeep/issue/79312/1344994
  • Aslantaş, M. F., Yılmaz, T., ve Çapanoğlu, M. F. (2024). Finansal İstikrarsızlığın Kamu Bankalarının İstikrarına Etkisi: Bir PMG/ARDL Panel Yaklaşımı. Sosyoekonomi, 32(59), 297-324. https://doi.org/10.17233/sosyoekonomi.2024.01.13
  • Beck, T., Levine, R., & Loayza, N. (2000). Finance and the Sources of Growth. Journal of financial economics, 58(1-2), 261-300. https://doi.org/10.1016/S0304-405X(00)00072-6
  • Bernanke, B. S., & Blinder, A. S. (1988). Credit, money, and aggregate demand. American Economic Review, 78(2), 435-439.
  • Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
  • Chen, Y. (2023). The impact of macroeconomic factors on bank credit risk. Advances iFn Economics Management and Political Sciences, 53(1), 79-84. https://doi.org/10.54254/2754-1169/53/20230799
  • De Benedictis, R., Gallegati, M., & Tamberi, M. (2008). Overall specialization anddevelopment: countries diversity. The Review of World Economic, 145 (1), 37-55.https://doi.org/10.1007/s10290-009-0007-4
  • Dirick, L., Claeskens, G., & Baesens, B. (2017). Time to default in credit scoring using survival analysis: a benchmark study. Journal of the Operational Research Society, 68(6), 652–665. https://doi.org/10.1057/s41274-016-0128-9
  • Djeundje, V. B., & Crook, J. (2019). Identifying hidden patterns in credit risk survival data using Generalised Additive Models. European Journal of Operational Research, 278(3), 764-778. https://doi.org/10.1016/j.ejor.2019.02.006
  • Dovern, J., Meier, C. P., & Vilsmeier, J. (2010). How resilient is the German banking system to macroeconomic shocks?. Journal of Banking & Finance, 34(8), 1839-1848. https://doi.org/10.1016/j.jbankfin.2009.12.001
  • Galán, J. E., & Mencia, J. (2021). Model-based indicators for the identification of cyclical systemic risk. Empirical Economics, 60(2), 421-445. https://doi.org/10.1007/s00181-020-01993-2
  • Gila-Gourgoura, E. and Nikolaidou, E. (2017). Credit Risk Determinants in The Vulnerable Economies of Europe: Evidence From The Spanish Banking System. International Journal of Business and Economic Sciences Applied Research, 10(1), 60-71. https://doi.org/10.25103/ijbesar.101.08
  • Guarin, A., Londoño, J. M., & Mendoza, L. (2014). An Early Warning Model For Predicting Credit Booms. Ensayos sobre Política Económica, 32(1), 45-54. https://doi.org/10.1016/s0120-4483(14)70020-x
  • Hastie, T., & Tibshirani, R. (1986). Generalized Additive Models. Statistical Science.
  • Hawkins, D. M. (2004). The Problem of Overfitting. Journal of chemical information and computer sciences, 44(1), 1-12. https://doi.org/10.1021/ci0342472
  • Jakubik, P. (2006). Macroeconomic Credit Risk Model. Occasional Publications-Chapters in Edited Volumes, 84-92.
  • Karaöz, N., & Aksu, A. (2024). Türkiye’de Kredi Türlerinin Ekonomik Büyümeyle İlişkisi. Journal of Economics, Finance and Sustainability, 2(1), 63–78. https://dergipark.org.tr/tr/pub/efs/issue/86792/1539614
  • Kidane, S. (2020). Credit Risk Management And Profitability: Empirical Evidence On Ethiopian Commercial Banks. Jurnal Perspektif Pembiayaan Dan Pembangunan Daerah, 8(4), 377-386. https://doi.org/10.22437/ppd.v8i4.10225
  • Koop, G., & Korobilis, D. (2010). Bayesian Multivariate Time Series Methods for Empirical Macroeconomics. Foundations and Trends in Econometrics, 3(4), 267-358. https://doi.org/10.1561/0800000013
  • Martinez, J., & Rodriguez, G. (2021). Macroeconomic Effects of Loan Supply Shocks: Empirical evidence for Peru. Latin American Economic Review, 30(1), 1-24. https://doi.org/10.47872/laer-2021-30-5
  • Mileris, R. (2013). Macroeconomic Determinants of Loan Portfolio Credit Risk in banks. Engineering Economics, 23(5). https://doi.org/10.5755/j01.ee.23.5.1890
  • Mubin, M. K., & Sugara, A. (2020). The İmpact of Macroeconomic Variables on Credit risk: Evidence from Indonesian Business Sector Level Data. International Journal of Research in Business and Social Science (2147-4478), 9(5), 235-244. https://doi.org/10.20525/ijrbs.v9i5.804
  • Pesaran, M. H., Schuermann, T., Treutler, B. J., & Weiner, S. M. (2006). Macroeconomic Dynamics and Credit Risk: A Global Perspective. Journal of Money, Credit and Banking, 1211-1261.
  • Pesola, J. (2011). Joint Effect of Financial Fragility And Macroeconomic Shocks on Bank Loan Losses: Evidence From Europe. Journal of Banking & Finance, 35(11), 3134-3144. https://doi.org/10.1016/j.jbankfin.2011.04.013
  • Pluskota, A. (2021). Macroeconomic Determinants Affecting Credit Risk in Central and Eastern Europe. Folia Oeconomica Stetinensia, 21(1), 92-104. https://doi.org/10.2478/foli-2021-0007
  • Salan, M. S. A., Hossain, M. M., et al. (2023). Relationships Between Total Reserve And Financial Indicators of Bangladesh. PLOS ONE. https://doi.org/10.1371/journal.pone.0284179
  • Sarı, S., ve Konukman, A. (2023). Türkiye’de Sektörel Kredi Yoğunlaşması Ve Büyüme İlişkisinin VAR Analizi ile İncelenmesi. Politik Ekonomik Kuram, 7(1), 1–15. https://dergipark.org.tr/tr/pub/pek/issue/78002/1225880
  • Sharma, H., Andhalkar, A., Ajao, O., & Ogunleye, B. (2024). Analysing the Influence of Macroeconomic Factors on Credit Risk in the UK Banking Sector. Analytics, 3(1), 63-83. https://doi.org/10.3390/analytics3010005
  • Ünal, S., ve Kenar, A. (2024). Türkiye’de Banka Kredileri İle Seçilmiş Makroekonomik Göstergeler Arasındaki İlişki. Dumlupınar Üniversitesi İİBF Dergisi, (13), 51–62. https://dergipark.org.tr/tr/pub/dpuiibf/issue/85739/1479653
  • Wood, S. N. (2017). Generalized Additive Models: An Introduction with R. CRC Press.
  • Zhang, L., Lian, Y. H., & Xin, B. H. (2015). Macroeconomic Uncertainty, Banks Heterogeneity and Credit Supply. Modern Economic Science, (37), 60-71... https://doi.org/10.4236/ojbm.2019.72042

GENELLEŞTİRİLMİŞ KATMANLI MODEL (GAM) PERSPEKTİFİNDEN TÜRKİYE'DE MAKROEKONOMİK GÖSTERGELERİN KREDİ HACMİNE ETKİSİ

Yıl 2025, Cilt: 15 Sayı: 29, 122 - 143, 26.06.2025

Öz

Bu çalışma, Türkiye'deki toplam kredi hacminin makroekonomik göstergelerle ilişkisini Genelleştirilmiş Katmanlı Model (GAM) kullanarak incelemiştir. Araştırma, faiz oranları, döviz kurları, para arzı, enflasyon ve reel sektör güven endeksi gibi literatürde en çok kullanılan önemli makroekonomik göstergelerin kredi hacmi üzerindeki etkilerini analiz etmiştir. GAM analiz sonuçları incelendiğinde, bu değişkenlerin kredi hacmi üzerinde anlamlı ve çoğunlukla doğrusal olmayan etkiler ortaya koyduğu vurgulanmıştır. Özellikle para arzı (M1), tüketici fiyat endeksi (TÜFE) ve ticari kredi faiz oranı (TCRFAİZ) değişkenleri kredi faizi üzerinde en güçlü etkileri göstermiştir. Modelin, bağımlı değişkenin varyansının %99.9'unu açıklayarak yüksek bir tahmin performansı ortaya koyduğu söylenebilir. Çalışma, makroekonomik göstergelerin kredi hacmi üzerindeki etkilerinin karmaşık ve doğrusal olmayan yapısını ortaya koymakta ve GAM modelinin buna benzer ilişkileri analiz etmede oldukça başarılı bir araç olduğunu göstermektedir. Bulgular, ekonomik analizlerde doğrusal olmayan ilişkilerin önemini vurgulamakla beraber politika yapıcılar için daha kapsamlı bir değerlendirme sunmaktadır. Gelecekteki araştırmalar için, farklı veri analizi teknikleri ve veri setleriyle analizlerin genişletilmesi önerilmektedir.

Kaynakça

  • Akşehirli, N., ve Karahan, Ö. (2023). Türkiye'de Banka Kredilerinin Makro ve Mikro Belirleyicileri. JOEEP: Journal of Emerging Economies and Policy, 8(2), 179–195. https://dergipark.org.tr/tr/pub/joeep/issue/79312/1344994
  • Aslantaş, M. F., Yılmaz, T., ve Çapanoğlu, M. F. (2024). Finansal İstikrarsızlığın Kamu Bankalarının İstikrarına Etkisi: Bir PMG/ARDL Panel Yaklaşımı. Sosyoekonomi, 32(59), 297-324. https://doi.org/10.17233/sosyoekonomi.2024.01.13
  • Beck, T., Levine, R., & Loayza, N. (2000). Finance and the Sources of Growth. Journal of financial economics, 58(1-2), 261-300. https://doi.org/10.1016/S0304-405X(00)00072-6
  • Bernanke, B. S., & Blinder, A. S. (1988). Credit, money, and aggregate demand. American Economic Review, 78(2), 435-439.
  • Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
  • Chen, Y. (2023). The impact of macroeconomic factors on bank credit risk. Advances iFn Economics Management and Political Sciences, 53(1), 79-84. https://doi.org/10.54254/2754-1169/53/20230799
  • De Benedictis, R., Gallegati, M., & Tamberi, M. (2008). Overall specialization anddevelopment: countries diversity. The Review of World Economic, 145 (1), 37-55.https://doi.org/10.1007/s10290-009-0007-4
  • Dirick, L., Claeskens, G., & Baesens, B. (2017). Time to default in credit scoring using survival analysis: a benchmark study. Journal of the Operational Research Society, 68(6), 652–665. https://doi.org/10.1057/s41274-016-0128-9
  • Djeundje, V. B., & Crook, J. (2019). Identifying hidden patterns in credit risk survival data using Generalised Additive Models. European Journal of Operational Research, 278(3), 764-778. https://doi.org/10.1016/j.ejor.2019.02.006
  • Dovern, J., Meier, C. P., & Vilsmeier, J. (2010). How resilient is the German banking system to macroeconomic shocks?. Journal of Banking & Finance, 34(8), 1839-1848. https://doi.org/10.1016/j.jbankfin.2009.12.001
  • Galán, J. E., & Mencia, J. (2021). Model-based indicators for the identification of cyclical systemic risk. Empirical Economics, 60(2), 421-445. https://doi.org/10.1007/s00181-020-01993-2
  • Gila-Gourgoura, E. and Nikolaidou, E. (2017). Credit Risk Determinants in The Vulnerable Economies of Europe: Evidence From The Spanish Banking System. International Journal of Business and Economic Sciences Applied Research, 10(1), 60-71. https://doi.org/10.25103/ijbesar.101.08
  • Guarin, A., Londoño, J. M., & Mendoza, L. (2014). An Early Warning Model For Predicting Credit Booms. Ensayos sobre Política Económica, 32(1), 45-54. https://doi.org/10.1016/s0120-4483(14)70020-x
  • Hastie, T., & Tibshirani, R. (1986). Generalized Additive Models. Statistical Science.
  • Hawkins, D. M. (2004). The Problem of Overfitting. Journal of chemical information and computer sciences, 44(1), 1-12. https://doi.org/10.1021/ci0342472
  • Jakubik, P. (2006). Macroeconomic Credit Risk Model. Occasional Publications-Chapters in Edited Volumes, 84-92.
  • Karaöz, N., & Aksu, A. (2024). Türkiye’de Kredi Türlerinin Ekonomik Büyümeyle İlişkisi. Journal of Economics, Finance and Sustainability, 2(1), 63–78. https://dergipark.org.tr/tr/pub/efs/issue/86792/1539614
  • Kidane, S. (2020). Credit Risk Management And Profitability: Empirical Evidence On Ethiopian Commercial Banks. Jurnal Perspektif Pembiayaan Dan Pembangunan Daerah, 8(4), 377-386. https://doi.org/10.22437/ppd.v8i4.10225
  • Koop, G., & Korobilis, D. (2010). Bayesian Multivariate Time Series Methods for Empirical Macroeconomics. Foundations and Trends in Econometrics, 3(4), 267-358. https://doi.org/10.1561/0800000013
  • Martinez, J., & Rodriguez, G. (2021). Macroeconomic Effects of Loan Supply Shocks: Empirical evidence for Peru. Latin American Economic Review, 30(1), 1-24. https://doi.org/10.47872/laer-2021-30-5
  • Mileris, R. (2013). Macroeconomic Determinants of Loan Portfolio Credit Risk in banks. Engineering Economics, 23(5). https://doi.org/10.5755/j01.ee.23.5.1890
  • Mubin, M. K., & Sugara, A. (2020). The İmpact of Macroeconomic Variables on Credit risk: Evidence from Indonesian Business Sector Level Data. International Journal of Research in Business and Social Science (2147-4478), 9(5), 235-244. https://doi.org/10.20525/ijrbs.v9i5.804
  • Pesaran, M. H., Schuermann, T., Treutler, B. J., & Weiner, S. M. (2006). Macroeconomic Dynamics and Credit Risk: A Global Perspective. Journal of Money, Credit and Banking, 1211-1261.
  • Pesola, J. (2011). Joint Effect of Financial Fragility And Macroeconomic Shocks on Bank Loan Losses: Evidence From Europe. Journal of Banking & Finance, 35(11), 3134-3144. https://doi.org/10.1016/j.jbankfin.2011.04.013
  • Pluskota, A. (2021). Macroeconomic Determinants Affecting Credit Risk in Central and Eastern Europe. Folia Oeconomica Stetinensia, 21(1), 92-104. https://doi.org/10.2478/foli-2021-0007
  • Salan, M. S. A., Hossain, M. M., et al. (2023). Relationships Between Total Reserve And Financial Indicators of Bangladesh. PLOS ONE. https://doi.org/10.1371/journal.pone.0284179
  • Sarı, S., ve Konukman, A. (2023). Türkiye’de Sektörel Kredi Yoğunlaşması Ve Büyüme İlişkisinin VAR Analizi ile İncelenmesi. Politik Ekonomik Kuram, 7(1), 1–15. https://dergipark.org.tr/tr/pub/pek/issue/78002/1225880
  • Sharma, H., Andhalkar, A., Ajao, O., & Ogunleye, B. (2024). Analysing the Influence of Macroeconomic Factors on Credit Risk in the UK Banking Sector. Analytics, 3(1), 63-83. https://doi.org/10.3390/analytics3010005
  • Ünal, S., ve Kenar, A. (2024). Türkiye’de Banka Kredileri İle Seçilmiş Makroekonomik Göstergeler Arasındaki İlişki. Dumlupınar Üniversitesi İİBF Dergisi, (13), 51–62. https://dergipark.org.tr/tr/pub/dpuiibf/issue/85739/1479653
  • Wood, S. N. (2017). Generalized Additive Models: An Introduction with R. CRC Press.
  • Zhang, L., Lian, Y. H., & Xin, B. H. (2015). Macroeconomic Uncertainty, Banks Heterogeneity and Credit Supply. Modern Economic Science, (37), 60-71... https://doi.org/10.4236/ojbm.2019.72042
Toplam 31 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Uygulamalı Makro Ekonometri, Finansal Ekonomi, Finans
Bölüm Makaleler
Yazarlar

Eyyüp Ensari Şahin 0000-0003-2110-7571

Recep Çakar 0000-0002-4069-7653

Yayımlanma Tarihi 26 Haziran 2025
Gönderilme Tarihi 14 Ocak 2025
Kabul Tarihi 10 Nisan 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 15 Sayı: 29

Kaynak Göster

APA Şahin, E. E., & Çakar, R. (2025). GENELLEŞTİRİLMİŞ KATMANLI MODEL (GAM) PERSPEKTİFİNDEN TÜRKİYE’DE MAKROEKONOMİK GÖSTERGELERİN KREDİ HACMİNE ETKİSİ. Karadeniz Teknik Üniversitesi Sosyal Bilimler Enstitüsü Sosyal Bilimler Dergisi, 15(29), 122-143.
AMA Şahin EE, Çakar R. GENELLEŞTİRİLMİŞ KATMANLI MODEL (GAM) PERSPEKTİFİNDEN TÜRKİYE’DE MAKROEKONOMİK GÖSTERGELERİN KREDİ HACMİNE ETKİSİ. KTÜSBD. Haziran 2025;15(29):122-143.
Chicago Şahin, Eyyüp Ensari, ve Recep Çakar. “GENELLEŞTİRİLMİŞ KATMANLI MODEL (GAM) PERSPEKTİFİNDEN TÜRKİYE’DE MAKROEKONOMİK GÖSTERGELERİN KREDİ HACMİNE ETKİSİ”. Karadeniz Teknik Üniversitesi Sosyal Bilimler Enstitüsü Sosyal Bilimler Dergisi 15, sy. 29 (Haziran 2025): 122-43.
EndNote Şahin EE, Çakar R (01 Haziran 2025) GENELLEŞTİRİLMİŞ KATMANLI MODEL (GAM) PERSPEKTİFİNDEN TÜRKİYE’DE MAKROEKONOMİK GÖSTERGELERİN KREDİ HACMİNE ETKİSİ. Karadeniz Teknik Üniversitesi Sosyal Bilimler Enstitüsü Sosyal Bilimler Dergisi 15 29 122–143.
IEEE E. E. Şahin ve R. Çakar, “GENELLEŞTİRİLMİŞ KATMANLI MODEL (GAM) PERSPEKTİFİNDEN TÜRKİYE’DE MAKROEKONOMİK GÖSTERGELERİN KREDİ HACMİNE ETKİSİ”, KTÜSBD, c. 15, sy. 29, ss. 122–143, 2025.
ISNAD Şahin, Eyyüp Ensari - Çakar, Recep. “GENELLEŞTİRİLMİŞ KATMANLI MODEL (GAM) PERSPEKTİFİNDEN TÜRKİYE’DE MAKROEKONOMİK GÖSTERGELERİN KREDİ HACMİNE ETKİSİ”. Karadeniz Teknik Üniversitesi Sosyal Bilimler Enstitüsü Sosyal Bilimler Dergisi 15/29 (Haziran2025), 122-143.
JAMA Şahin EE, Çakar R. GENELLEŞTİRİLMİŞ KATMANLI MODEL (GAM) PERSPEKTİFİNDEN TÜRKİYE’DE MAKROEKONOMİK GÖSTERGELERİN KREDİ HACMİNE ETKİSİ. KTÜSBD. 2025;15:122–143.
MLA Şahin, Eyyüp Ensari ve Recep Çakar. “GENELLEŞTİRİLMİŞ KATMANLI MODEL (GAM) PERSPEKTİFİNDEN TÜRKİYE’DE MAKROEKONOMİK GÖSTERGELERİN KREDİ HACMİNE ETKİSİ”. Karadeniz Teknik Üniversitesi Sosyal Bilimler Enstitüsü Sosyal Bilimler Dergisi, c. 15, sy. 29, 2025, ss. 122-43.
Vancouver Şahin EE, Çakar R. GENELLEŞTİRİLMİŞ KATMANLI MODEL (GAM) PERSPEKTİFİNDEN TÜRKİYE’DE MAKROEKONOMİK GÖSTERGELERİN KREDİ HACMİNE ETKİSİ. KTÜSBD. 2025;15(29):122-43.

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