Research Article
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FORECASTING CONSUMER PRICE INDEX USING MACROECONOMIC VARIABLES: A COMPARATIVE ANALYSIS OF MACHINE LEARNING AND DEEP LEARNING APPROACHES

Year 2024, Issue: 28, 15 - 29, 30.10.2024

Ahmed İhsan Şimşek

https://doi.org/10.29029/busbed.1394983

Abstract

The Turkish economy has faced many economic difficulties throughout it's history. At this point, predicting inflation accurately is very important for policy makers, businesses, investors and consumers. This study aims to estimate the Turkish Consumer Price Index. Producer price index, M1 money supply, gold price, dollar price, natural gas price and interest rate variables were used to estimate the CPI for Turkey. The variables used in the research were obtained through EVDS, the Central Bank's Electronic Data Management System. Monthly data from January 2003 to August 2023 was used in the study. The obtained data were estimated using DDPG, XGBoost, SVR, KNN and CNN-BiLSTM methods. Model performances were compared using RMSE, MSE, MAE, MAPE and R2 statistical coefficients. When model performances were evaluated, the best CPI prediction for Turkey was obtained by the SVR method.

Keywords

CIP Prediction SVR Time Series Decision Support System Machine Learning Deep Learning

References

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MAKROEKONOMİK DEĞİŞKENLER KULLANARAK TÜKETİCİ FİYAT ENDEKSİNİN TAHMİN EDİLMESİ: MAKİNE ÖĞRENMESİ VE DERİN ÖĞRENME YAKLAŞIMLARININ KARŞILAŞTIRMALI BİR ANALİZİ

Year 2024, Issue: 28, 15 - 29, 30.10.2024

Ahmed İhsan Şimşek

https://doi.org/10.29029/busbed.1394983

Abstract

Türkiye ekonomisi tarih boyunca birçok ekonomik zorlıkla karşılaşmıştır. Bu noktada enflasyonun doğru bir şekilde tahmin edilmesi politika yapıcıları, işletmeler, yatırımcılar ve tüketiciler açısından oldukça önemlidir. Bu çalışmanın amacı Türkiye Tüketici Fiyat Endeksi’nin tahmin edilmesidir. Türkiye için TÜFE’nin tahmin edilmesinde üretici fiyat endeksi, M1 para arzı, altın fiyatı, dolar fiyatı, doğalgaz fiyatı ve faiz oranı değişkenleri kullanılmıştır. Araştırmada kullanılan değişkenler Merkez Bankasının Elektronik Veri Yönetim Sistemi olan EVDS üzerinden elde edilmiştir. Çalışmada Ocak 2003’ten Ağustos 2023’e kadar olan aylık veriler kullanılmıştır. Elde edilen veriler DDPG, XGBoost, SVR, KNN ve CNN-BiLSTM yöntemleri kullanılarak tahmin edilmiştir. Model performansları RMSE, MSE, MAE, MAPE ve R2 istatistik katsayıları kullanılarak karşılaştırılmıştır. Model performansları değerlendirildiğinde Türkiye için en iyi TÜFE tahmini SVR yöntemi tarafından elde edilmiştir.

Keywords

TÜFE Tahmini SVR Zaman Serisi Karar Destek Sistemleri Makine Öğrenmesi Derin Öğrenme

References

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  • Ao, X., Gong, Y., & Zuo, B. (2020). Prediction of Consumer Price Index based on Long Short-Term Memory Model. Journal of Physics: Conference Series, 1550(3). https://doi.org/10.1088/1742-6596/1550/3/032068
  • Aras, S., & Lisboa, P. J. G. (2022). Explainable inflation forecasts by machine learning models. Expert Systems with Applications, 207(June), 117982. https://doi.org/10.1016/j.eswa.2022.117982
  • Ayestarán, R., Infante, J., Tenorio, J. J., & Gil-Alana, L. A. (2023). Evidence of Inflation Using Harmonized Consumer Price Indices in Some Euro Countries: France, Germany, Italy, and Spain, along with the Euro Zone. Mathematics, 11(10), 2365.
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  • Balcilar, M., Uwilingiye, J., & Gupta, R. (2018). Dynamic relationship between oil price and inflation in South Africa. The Journal of Developing Areas, 52(2), 73-93.
  • Barkan, O., Benchimol, J., Caspi, I., Cohen, E., Hammer, A., & Koenigstein, N. (2023). Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks. International Journal of Forecasting, 39(3), 1145–1162. https://doi.org/10.1016/j.ijforecast.2022.04.009
  • Batten, J. A., Ciner, C., & Lucey, B. M. (2014). On the economic determinants of the gold–inflation relation. Resources Policy, 41, 101-108.
  • Biswas, G. K. (2023). Inflation Dynamics of Bangladesh: An Empirical Analysis. European Journal of Business and Management Research, 8(3), 288-292.
  • Boeck, M., Zörner, T. O., & Nationalbank, O. (2023). Natural Gas Prices and Unnatural Propagation Effects: The Role of Inflation Expectations in the Euro Area. SSRN.
  • Budiastuti, I. A., Nugroho, S. M. S., & Hariadi, M. (2017). Predicting daily consumer price index using support vector regression method. QiR 2017 - 2017 15th International Conference on Quality in Research (QiR): International Symposium on Electrical and Computer Engineering, 2017-Decem, 23–28. https://doi.org/10.1109/QIR.2017.8168445
  • Chen, Y., Xu, P., Chu, Y., Li, W., Wu, Y., Ni, L., ... & Wang, K. (2017). Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings. Applied Energy, 195, 659-670.
  • Choi, S., Furceri, D., Loungani, P., Mishra, S., & Poplawski-Ribeiro, M. (2018). Oil prices and inflation dynamics: Evidence from advanced and developing economies. Journal of International Money and Finance, 82, 71-96.
  • Ding, S., Zheng, D., Cui, T., & Du, M. (2023). The oil price-inflation nexus: The exchange rate pass-through effect. Energy Economics, 125, 106828.
  • Doan Van, D. (2020). Money supply and inflation impact on economic growth. Journal of Financial Economic Policy, 12(1), 121-136.
  • Dogan, I., Orun, E., Aydın, B., & Afsal, M. S. (2020). Non-parametric analysis of the relationship between inflation and interest rate in the context of Fisher effect for Turkish economy. International Review of Applied Economics, 34(6), 758-768.
  • Dong, W., Huang, Y., Lehane, B., & Ma, G. (2020). XGBoost algorithm-based prediction of concrete electrical resistivity for structural health monitoring. Automation in Construction, 114, 103155.
  • Dong, Y., & Zou, X. (2020, October). Mobile robot path planning based on improved DDPG reinforcement learning algorithm. In 2020 IEEE 11th International Conference on software engineering and service science (ICSESS) (pp. 52-56). IEEE.
  • Duong, T. H. (2023). The gold price–Inflation relation in the case of Vietnam: empirical investigation in the presence of structural breaks. Asian Journal of Economics and Banking, 7(2), 217-233.
  • Gang, F. A. N., Liping, H. E., & Jiani, H. U. (2009). CPI vs. PPI: Which drives which?. Frontiers of Economics in China, 4(3), 317-334.
  • Gürkaynak, R. S., Kısacıkoğlu, B., & Lee, S. S. (2023). Exchange rate and inflation under weak monetary policy: Turkey verifies theory. Economic Policy, eiad020.
  • Huang, W. (2022, January). KNN Virtual Currency Price Prediction Model Based on Price Trend Characteristics. In 2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA) (pp. 537-542). IEEE.
  • Huong, D. T. T., Van Truong, V., & Lam, B. T. (2016). Forecasting of consumer price index using the ensemble learning model with multi-objective evolutionary algorithms: Preliminary results. International Conference on Advanced Technologies for Communications, 2016-Janua(October), 337–342. https://doi.org/10.1109/ATC.2015.7388346
  • Jadhav, S. D., & Channe, H. P. (2016). Comparative study of K-NN, naive Bayes and decision tree classification techniques. International Journal of Science and Research (IJSR), 5(1), 1842-1845.
  • Jalaee, M. S., Jalaee, S. A., Sadeghi, Z., & Nejati, M. (2021). Investigating Impact of Real Natural Gas Prices on Inflation, Welfare Index and Carbon Emission in Iran: A Dynamic Computable General Equilibrium Model Approach. Journal of Economics and Modeling, 12(1), 173-196.
  • Kantardzic, M. (2011). Data mining: concepts, models, methods, and algorithms. John Wiley & Sons. Khumalo, L. C., Mutambara, E., & Assensoh-Kodua, A. (2017). Relationship between inflation and interest rates in Swaziland revisited. Banks & bank systems, (12,№ 4 (cont.)), 218-226.
  • Kilian, L., & Zhou, X. (2022). Oil prices, gasoline prices, and inflation expectations. Journal of Applied Econometrics, 37(5), 867-881.
  • Köse, N., & Ünal, E. (2021). The effects of the oil price and oil price volatility on inflation in Turkey. Energy, 226, 120392.
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There are 78 citations in total.

Details

Primary Language English
Subjects Decision Support and Group Support Systems, Time-Series Analysis
Journal Section Articles
Authors

Ahmed İhsan Şimşek 0000-0002-2900-3032

Early Pub Date October 27, 2024
Publication Date October 30, 2024
Submission Date November 23, 2023
Acceptance Date September 9, 2024
Published in Issue Year 2024Issue: 28

Cite

APA Şimşek, A. İ. (2024). FORECASTING CONSUMER PRICE INDEX USING MACROECONOMIC VARIABLES: A COMPARATIVE ANALYSIS OF MACHINE LEARNING AND DEEP LEARNING APPROACHES. Bingöl Üniversitesi Sosyal Bilimler Enstitüsü Dergisi(28), 15-29. https://doi.org/10.29029/busbed.1394983
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