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

Yıl 2024, Sayı: 28, 15 - 29, 30.10.2024

Ahmed İhsan Şimşek

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

Öz

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.

Anahtar Kelimeler

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

Kaynakça

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  • Ali, A. O., & Mohamed, J. (2022). The optimal forecast model for consumer price index of Puntland State, Somalia. Quality and Quantity, 56(6), 4549–4572. https://doi.org/10.1007/s11135-022-01328-6
  • Álvarez-Díaz, M., & Gupta, R. (2016). Forecasting US consumer price index: does nonlinearity matter? Applied Economics, 48(46), 4462–4475. https://doi.org/10.1080/00036846.2016.1158922
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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.
  • Ayub, G., Rehman, N., Iqbal, M., Zaman, Q., & Atif, M. (2014). Relationship between inflation and interest rate: evidence from Pakistan. Research Journal of Recent Sciences ISSN, 2277, 2502.
  • 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.
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  • 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.
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MAKROEKONOMİK DEĞİŞKENLER KULLANARAK TÜKETİCİ FİYAT ENDEKSİNİN TAHMİN EDİLMESİ: MAKİNE ÖĞRENMESİ VE DERİN ÖĞRENME YAKLAŞIMLARININ KARŞILAŞTIRMALI BİR ANALİZİ

Yıl 2024, Sayı: 28, 15 - 29, 30.10.2024

Ahmed İhsan Şimşek

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

Öz

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.

Anahtar Kelimeler

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

Kaynakça

  • Aha, D., Kibler, D.W., Albert, M.K. (1991). Instance-based learning algorithms. Mach Learn, 6, 37–66
  • Aharon, D. Y., Aziz, M. I. A., & Kallir, I. (2023). Oil price shocks and inflation: A cross-national examination in the ASEAN5+ 3 countries. Resources Policy, 82, 103573.
  • Ali, A. O., & Mohamed, J. (2022). The optimal forecast model for consumer price index of Puntland State, Somalia. Quality and Quantity, 56(6), 4549–4572. https://doi.org/10.1007/s11135-022-01328-6
  • Álvarez-Díaz, M., & Gupta, R. (2016). Forecasting US consumer price index: does nonlinearity matter? Applied Economics, 48(46), 4462–4475. https://doi.org/10.1080/00036846.2016.1158922
  • Amaefula, C. G. (2016). Long-run relationship between interest rate and inflation: Evidence from Nigeria. Journal of Economics and Finance, 7(3), 24-28.
  • Ambukege, G., Justo, G., & Mushi, J. (2017). Neuro Fuzzy Modelling for Prediction of Consumer Price Index. International Journal of Artificial Intelligence & Applications, 8(5), 33–44. https://doi.org/10.5121/ijaia.2017.8503
  • Amra, I. A. A., & Maghari, A. Y. (2017, May). Students performance prediction using KNN and Naïve Bayesian. In 2017 8th international conference on information technology (ICIT) (pp. 909-913). IEEE.
  • Anandasayanan, S., Thevananth, J., & Amaresh, M. (2019). The Relationship Between Inflation and Gold Price: Evidence From Sri Lanka. International Journal of Accounting and Financial Reporting ISSN, 2162-3082.
  • 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.
  • Ayub, G., Rehman, N., Iqbal, M., Zaman, Q., & Atif, M. (2014). Relationship between inflation and interest rate: evidence from Pakistan. Research Journal of Recent Sciences ISSN, 2277, 2502.
  • 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.
  • Krompas, I. (2022). Natural Gas Price Inefficiencies as an Obstacle in Taming EU Inflation. HAPSc Policy Briefs Series, 3(2), 146-152.
  • Lacheheb, M., & Sirag, A. (2019). Oil price and inflation in Algeria: A nonlinear ARDL approach. The Quarterly Review of Economics and Finance, 73, 217-222.
  • Lee, C. C., Olasehinde-Williams, G., & Özkan, O. (2023). Geopolitical oil price uncertainty transmission into core inflation: Evidence from two of the biggest global players. Energy Economics, 126, 106983.
  • Li, S., Tang, G., Yang, D., & Du, S. (2019). Research on the Relationship between CPI and PPI Based on VEC Model. Open journal of statistics, 9(02), 218.
  • Li, W., Yin, Y., Quan, X., & Zhang, H. (2019). Gene expression value prediction based on XGBoost algorithm. Frontiers in genetics, 10, 1077.
  • Li, Y., & Guo, J. (2022). The asymmetric impacts of oil price and shocks on inflation in BRICS: a multiple threshold nonlinear ARDL model. Applied Economics, 54(12), 1377-1395.
  • Li, Y., Wang, R., Li, Y., Zhang, M., & Long, C. (2023). Wind power forecasting considering data privacy protection: A federated deep reinforcement learning approach. Applied Energy, 329, 120291.
  • Lima, RID. (2019). Does PPI lead CPI in Brazil?. International Journal of Production Economics, 214, 73-79.
  • Liu, J., Ye, J., & E, J. (2023). A multi-scale forecasting model for CPI based on independent component analysis and non-linear autoregressive neural network. Physica A: Statistical Mechanics and Its Applications, 609, 128369. https://doi.org/10.1016/j.physa.2022.128369
  • Liu, Z., Liu, Y., Xu, H., Liao, S., Zhu, K., & Jiang, X. (2022). Dynamic economic dispatch of power system based on DDPG algorithm. Energy Reports, 8, 1122-1129.
  • Lucey, B. M., Sharma, S. S., & Vigne, S. A. (2017). Gold and inflation (s)–A time-varying relationship. Economic Modelling, 67, 88-101.
  • Mendiola-Rodriguez, T. A., & Ricardez-Sandoval, L. A. (2022). Robust control for anaerobic digestion systems of Tequila vinasses under uncertainty: A Deep Deterministic Policy Gradient Algorithm. Digital Chemical Engineering, 3, 100023.
  • Milunovich, G. (2020). Forecasting Australia’s real house price index: A comparison of time series and machine learning methods. Journal of Forecasting, 39(7), 1098–1118. https://doi.org/10.1002/for.2678
  • Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., ... & Hassabis, D. (2015). Human-level control through deep reinforcement learning. nature, 518(7540), 529-533.
  • Mohammed Adnan, A., Prince Immanuel, J., & Roobini, M. S. (2023). Forecasting Consumer Price Index (CPI) Using Deep Learning and Hybrid Ensemble Technique. Proceedings of the 2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023, 1–8. https://doi.org/10.1109/ACCAI58221.2023.10200153
  • Mukhtarov, S., Mammadov, J., & Ahmadov, F. (2019). The impact of oil prices on inflation: The case of Azerbaijan. International Journal of Energy Economics and Policy, 9(4), 97-102.
  • Nguyen, T. T., Nguyen, H. G., Lee, J. Y., Wang, Y. L., & Tsai, C. S. (2023). The consumer price index prediction using machine learning approaches: Evidence from the United States. Heliyon, 9(10), e20730. https://doi.org/10.1016/j.heliyon.2023.e20730
  • Peer, A. H., & Baig, M. A. (2021). Inflation targeting and exchange rate pass-through in india: an empirical investigation. Critical Perspectives on Emerging Economies: An International Assessment, 115.
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  • Ramadhan, M. R., Ermawati, W. J., & Fariyanti, A. (2023). The influence of Indonesia’s macroeconomic factors: Inflation and interest rate on large-cap cryptocurrency herding behavior. Journal of Accounting and Investment, 24(2), 569-586.
  • Ridwan, M. (2022). DETERMINANTS OF INFLATION: Monetary and Macroeconomic Perspectives. KINERJA: Jurnal Manajemen Organisasi dan Industri, 1(1), 1-10.
  • Riofrío, J., Chang, O., Revelo-Fuelagán, E. J., & Peluffo-Ordóñez, D. H. (2020). Forecasting the Consumer Price Index (CPI) of Ecuador: A comparative study of predictive models. International Journal on Advanced Science, Engineering and Information Technology, 10(3), 1078–1084. https://doi.org/10.18517/ijaseit.10.3.10813
  • Romdhane, Y. B., Loukil, S., & Kammoun, S. (2019). Targeting inflation and exchange rate management in tunisia before and after the revolution. Int J Soc Sci Econ Invent.
  • Sarangi, P. K., Sahoo, A. K., & Sinha, S. (2022). Modeling Consumer Price Index: A Machine Learning Approach. Macromolecular Symposia, 401(1), 1–6. https://doi.org/10.1002/masy.202100349
  • Sarveswararao, V., & Ravi, V. (2020). Chaos, Machine Learning and Deep Learning based Hybrid to forecast Consumer Price Index Inflation in India. 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020, 2551–2557. https://doi.org/10.1109/SSCI47803.2020.9308309
  • Sean, M., Pastpipatkul, P., & Boonyakunakorn, P. (2019). Money supply, inflation and exchange rate movement: the case of Cambodia by Bayesian VAR approach. Journal of Management, Economics, and Industrial Organization, 3(1), 63-81.
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  • Shah, M. A. A., Aleem, M., & Arshed, N. (2014). Statistical analysis of the factors affecting inflation in Pakistan. Middle-East Journal of Scientific Research, 21(1), 181-189.
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  • Sun, J., Xu, J., Cheng, X., Miao, J., & Mu, H. (2023). Dynamic causality between PPI and CPI in China: A rolling window bootstrap approach. International Journal of Finance & Economics, 28(2), 1279-1289.
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  • Tursoy, T., & Muhammad, M. I. (2020). Lead-lag and relationship between money growth and inflation in Turkey: New evidence from a wavelet analysis. Theoretical and Practical Research in the Economic Fields, 11(1), 47-57.
  • Wang, H., Wang, J., Cao, L., Li, Y., Sun, Q., & Wang, J. (2021). A Stock Closing Price Prediction Model Based on CNN-BiSLSTM. Complexity, 2021. https://doi.org/10.1155/2021/5360828
  • Wei, S. J., & Xie, Y. (2022). On the wedge between the PPI and CPI inflation indicators (No. 2022-5). Bank of Canada Staff Working Paper.
  • Woo, K. Y., Lee, S. K., & Ng, C. Y. J. (2019). An investigation into the dynamic relationship between CPI and PPI: Evidence from the UK, France and Germany. The Singapore Economic Review, 64(05), 1081-1100.
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  • Xu, Q., Wang, Z., Jiang, C., & Liu, Y. (2023). Deep learning on mixed frequency data. Journal of Forecasting, February, 2099–2120. https://doi.org/10.1002/for.3003
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  • Zahara, S., Sugianto, & Ilmiddaviq, M. B. (2020). Consumer price index prediction using Long Short Term Memory (LSTM) based cloud computing. Journal of Physics: Conference Series, 1456(1). https://doi.org/10.1088/1742-6596/1456/1/012022
  • Zakaria, M., Khiam, S., & Mahmood, H. (2021). Influence of oil prices on inflation in South Asia: Some new evidence. Resources Policy, 71, 102014.
  • Zheng, J., Wang, Y., Li, S., & Chen, H. (2021). The stock index prediction based on SVR model with bat optimization algorithm. Algorithms, 14(10), 299.
Toplam 78 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Karar Desteği ve Grup Destek Sistemleri, Zaman Serileri Analizi
Bölüm Araştırma Makaleleri
Yazarlar

Ahmed İhsan Şimşek 0000-0002-2900-3032

Erken Görünüm Tarihi 27 Ekim 2024
Yayımlanma Tarihi 30 Ekim 2024
Gönderilme Tarihi 23 Kasım 2023
Kabul Tarihi 9 Eylül 2024
Yayımlandığı Sayı Yıl 2024Sayı: 28

Kaynak Göster

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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