Bütçe Tahmincileri için Enflasyon Tahmini: Türkiye'de YSA Modeli Performansının İncelenmesi

Abstract

Bütçe gelir ve harcama tahminlerinin güvenilirliği, enflasyon tahminlerinin doğruluğuna bağlıdır. Gerçekçi enflasyon tahminleri olmadan sağlıklı bir bütçe tahmini yapmak mümkün değildir. Bu çalışma, Türkiye’de bütçe tahmincilerine enflasyon tahminleri konusunda rehberlik etmek amacıyla hazırlanmıştır. Çalışmada, 2005-2023 dönemine ait veriler kullanılmıştır. Döviz kuru sepeti (Dolar ve Euro), işsizlik, ithalat, ihracat, bütçe gelir ve harcamaları, faiz oranı, sanayi üretim endeksi, para arzı, genel fiyat endeksi ve asgari ücret, Holt-Winters, ARIMA, SARIMA, Prophet, LSTM ve Hibrit yöntemleriyle tahmin edilmiştir. Daha sonra bu tahminler kullanılarak YSA, SVR, RF ve GBM yöntemiyle aylık enflasyon tahminleri üretilmiştir. Sonuçlar, çalışmada YSA ile edilen tahminlerin, Türkiye’nin bütçe kanunu ve orta vadeli programda yer alan enflasyon tahminlerinden daha gerçekçi olduğunu göstermektedir. Çalışma, YSA yönteminin bütçe tahmincileri tarafından enflasyonu doğru tahmin etmek için etkili bir araç olarak kullanılabileceğini ortaya koymaktadır. Bulgular, IMF, OECD, Merkez Bankası ve Avrupa Birliği tahminleriyle karşılaştırmalı analiz yoluyla değerlendirilmiştir. Gelecek akademik araştırmaları desteklemek amacıyla, 2025 yılına ilişkin bağımsız değişkenler ve enflasyon tahminleri de çalışmaya dahil edilmiştir.

Keywords

• Bütçe Tahmini
• Enflasyon Tahmini
• Yapay Sinir Ağları

Forecasting the Inflation for Budget Forecasters: An Analysis of ANN Model Performance in Türkiye

Abstract

The reliability of budget revenue and expenditure forecasts depends on the accuracy of inflation forecasts. Without realistic inflation forecasts, it is not possible to produce sound budget forecasts. This study aims to guide budget forecasters in Türkiye by providing accurate inflation forecasts. The analysis utilizes data from the 2005–2023 period. The basket exchange rate (USD and Euro), unemployment, imports, exports, budget revenues and expenditures, interest rates, industrial production index, money supply, general price index, and minimum wage are forecasted using Holt-Winters, ARIMA, SARIMA, Prophet, LSTM, and Hybrid models. These forecasts are then used as inputs in ANN, SVR, RF, and GBM models to forecast monthly inflation. The results indicate that the forecasts generated with ANN are significantly more realistic than those presented in Türkiye’s budget law and the Medium-Term Program. The study demonstrates that ANN can be an effective tool for budget forecasters in accurately forecasting inflation and, consequently, improving budget forecasts. The findings are further evaluated through a comparative analysis with forecasts from institutions such as the IMF, OECD, Central Bank, and the European Union. To support future academic research, inflation forecasts for 2025, along with forecasts for independent variables, are also included in the study.

Keywords

• Budget Forecasting
• Inflation Forecasting
• Artificial Neural Network

References

1. Allan, C.M. (1965). Fiscal marksmanship, 1951-1963. Oxford Economic Papers, 17(2), 317-327. https://doi.org/10.1093/oxfordjournals.oep.a040992
2. Almosova, A. and Andresen, N. (2023). Nonlinear inflation forecasting with recurrent neural networks. Journal of Forecasting, 44(2), 240-259. doi:10.1002/for.2901
3. Araujo, G.S. and Gaglianone, W.P. (2023). Machine learning methods for inflation forecasting in Brazil: New contenders versus classical models. Latin American Journal of Central Banking, 4(2), 100087. doi:10.1016/j.latcb.2023.100087
4. Atkeson, A. and Ohanian, L. (2001). Are Phillips Curves useful for forecasting inflation? Federal Reserve Bank of Minneapolis Quarterly Review, 25(1), 2–11. Retrieved from https://citeseerx.ist.psu.edu/
5. Barkan, O., Benchimol, J., Caspi, I., Cohen, E., Hammer, A. and Koenigstein, N. (2023). Forecasting CPI inflation components with hierarchical recurrent neural networks. International Journal of Forecasting, 39(3), 145-1162. doi:10.1016/j.ijforecast.2022.04.009
6. Bayramoğlu, A.T. and Öztürk, Z. (2017). ARIMA ve Gri sistem modelleri ile enflasyon tahmini. İnsan ve Toplum Bilimleri Araştırmaları Dergisi, 6(2), 760-776. doi:10.15869/itobiad.300059
7. Bokil, M. and Schimmelpfennig, A. (2005). Three attempts at inflation forecasting in Pakistan (IMF Working Paper No. 05/105). Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=887974
8. Bos, C.S., Franses, P.H. and Ooms, M. (2002). Inflation, forecast interval,s and long memory regression models. International Journal of Forecasting, 18(2), 243-264. doi:10.1016/S0169-2070(01)00156-X

9. Breiman, L. (2001) Random forests. Machine Learning, 45, 5–32. https://doi.org/10.1023/A:1010933404324
10. Bretschneider, S. and Gorr, W.L. (1992). Economic, organizational, and political influences on biases in forecasting state sales tax receipts. International Journal of Forecasting, 7(4), 457-466. doi:10.1016/0169-2070(92)90029-9
11. Čaklovica, L. and Efendic, A. (2020). Determinants of inflation in Europe – A dynamic panel analysis. Financial Internet Quarterly, 16(3), 51-79. doi:10.2478/fiqf-2020-0018
12. Choudhary, M.A. and Haider, A. (2012). Neural network models for inflation forecasting: An appraisal. Applied Economics, 44(20), 2631–2635. doi:10.1080/00036846.2011.566190
13. Deniz, P., Tekce, M. and Yilmaz, A. (2016). Investigating the determinants of inflation: A panel data analysis. International Journal of Financial Research, 7(2), 233-246. doi:10.5430/ijfr.v7n2p233
14. Doguwa, S.I. and Alade, S. (2013). Short-term inflation forecasting models for Nigeria. CBN Journal of Applied Statistics, 4(3), 1-29. Retrieved from https://www.econstor.eu/
15. Drucker, H., Burges, C.J., Kaufman, L., Smola, A. and Vapnik, V. (1997). Support vector regression machines. In M.C. Mozer, M. Jordan and T. Petsche (Eds.), Advances in neural information processing systems 9 (pp. 155-161). Paper presented at the NIPS 1996. Retrieved from https://papers.nips.cc/paper_files/paper/1996
16. Erilli, N.A., Körez, M., Öner, Y. and Alakuş, K. (2012). Kritik kriz dönem enflasyon hesaplamalarında bulanık regresyon tahminlemesi. Doğuş Üniversitesi Dergisi, 13(2), 239-253. Retrieved from https://dergipark.org.tr/tr/pub/doujournal/
17. Erişoğlu, Ü. and Erişoğlu, M. (2022). Holt-Winters üstel düzleştirme yöntemi ile finansal gelişmişlik endeksinin tahmini. A. Akpınar and A. Güler (Eds.), Fen ve matematik bilimlerinde güncel yaklaşımlar içinde (s. 107-123). Lyon: Livre de Lyon.
18. Faust, J. and Wright, J. (2013). Forecasting inflation. In G. Elliott, and A. Timmermann (Eds.), Handbook of economic forecasting (pp. 2-56). Amsterdam: Elsevier.
19. Friedman, J.H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5), 1189–1232. doi:10.1214/aos/1013203451
20. Garcia, M.G., Medeiros, M. and Vasconcelos, G. (2017). Real-time inflation forecasting with high-dimensional models: The case of Brazil. International Journal of Forecasting, 33(3), 679-693. doi:10.1016/j.ijforecast.2017.02.002
21. Gil-Alana, L., Moreno, A. and de Gracia, F.P. (2012). Exploring survey-based inflation forecasts. Journal of Forecasting, 31(6), 524-539. doi:10.1002/for.1235
22. Groen, J.J., Paap, R. and Ravazzolo, F. (2013). Real-time inflation forecasting in a changing world. Journal of Business & Economic Statistics, 31(1), 29-44. doi:10.1080/07350015.2012.727718
23. Haider, A. and Hanif, M.N. (2008). Inflation forecasting in Pakistan using artificial neural networks. Pakistan Economic and Social Review, 47(1), 123-138. Retrieved from https://www.jstor.org/
24. Hanif, M.N. and Malik, J.M. (2015). Evaluating the performance of inflation forecasting models of Pakistan. SBP Research Bulletin, 11(1), 43-78. Retrieved from https://www.sbp.org.pk/
25. Hansen, P., Lunde, A. and Nason, J. (2011). The model confidence set. Econometrica, 79(2), 453-497. https://doi.org/10.3982/ECTA5771
26. Hauzenberger, N., Huber, F. and Klieber, K. (2023). Real-time inflation forecasting using non-linear dimension reduction techniques. International Journal of Forecasting, 39(2), 901-921. doi:10.1016/j.ijforecast.2022.03.002
27. Haykin, S. (1998). Neural networks: A comprehensive foundation. New Jersey: Prentice Hall PTR.
28. He, Q., Shen, H. and Tong, Z. (2012). Investigation of Inflation Forecasting. Applied Mathematics & Information Sciences, 6(3), 649-655. Retrieved from https://digitalcommons.aaru.edu.jo/
29. Holt, C.C. (2004). Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting, 20(1), 5-10. doi:10.1016/j.ijforecast.2003.09.015
30. Inoue, A. and Kilian, L. (2008). How useful is bagging in forecasting economic time series? A case study of U.S. consumer price inflation. Journal of the American Statistical Association, 103(482), 511-522. doi:10.1198/016214507000000473
31. Jain, A.K., Mao, J. and Mohiuddin, K.M. (1996). Artificial neural networks: A tutorial. Computer, 29(3), 31-44. Retrieved from https://ieeexplore.ieee.org/
32. Jakšić, S. (202Modelingling determinants of inflation in CESEE countries: Global vector autoregressive approach. Review of Economic Perspectives, 22(2), 137-169. doi:10.2478/revecp-2022-0007
33. Jenkins, G. and Box, G. (1976). Time series analysis: Forecasting and control. San Francisco: Holden Hay.
34. Kapur, M. (2013). Revisiting the Phillips Curve for India and inflation forecasting. Journal of Asian Economics, 25, 17-27. doi:10.1016/j.asieco.2012.12.002
35. Kara, B. (2024a). Türkiye'de kamu bütçesi tahminlerinin gerçekliği. Ankara: Gazi Kitabevi.
36. Kara, B. (2024b). The impact of budget revenue and expenditure forecasting errors on inflation in Turkey: An analysis of the 1975-2021 period. İstatistik ve Uygulamalı Bilimler Dergisi, 9, 19-28. doi:10.52693/jsas.1417919
37. Kayran, G. and Uzun Araz, Ö. (2023). Toplu taşıma yolcu talebinin SARIMA ve Prophet yöntemleri ile tahmini. Journal of Industrial Engineering (Turkish Chamber of Mechanical Engineers), 34(3), 334-356. doi:10.46465/endustrimuhendisligi.1281645
38. Kızılkaya, O. (2017). Türkiye’nin enflasyon ve işsizlik oranının yapay sinir ağları ve Box-Jenkins yöntemiyle tahmini. Social Sciences Studies Journal, 3(12), 2197-2207. doi:10.26449/sssj.299
39. Kinlaw, W., Kritzman, M., Metcalfe, M. and Turkington, D. (2023). The determinants of inflation. Journal of Investment Management, 21(3), 29-41. doi:10.2139/ssrn.4137861
40. Krenker, A., Bešter, J. and Kos, A. (2011). Introduction to the artificial neural networks. In K. Suzuki (Ed.), Artificial neural networks-methodological advances and biomedical applications (pp. 1-18). India: InTech.
41. Lim, Y.C. and Sek, S.K. (2015). An examination on the determinants of inflation. Journal of Economics, Business and Management, 3(7), 678-682. doi:10.7763/JOEBM.2015.V3.265
42. Malki, A., Atlam, E.-S., Hassanien, A., Ewis, A., Dagnew, G. and Gad, I. (2022). SARIMA model-based forecasting required number of COVID-19 vaccines globally and empirical analysis of peoples’ view towards the vaccines. Alexandria Engineering Journal, 61(12), 12091-12110. https://doi.org/10.1016/j.aej.2022.05.051
43. Martins, M.M. and Verona, F. (2023). Inflation dynamics in the frequency domain. Economics Letters, 231, 111304. doi:10.1016/j.econlet.2023.111304
44. McKnight, S., Mihailov, A. and Rumler, F. (2020). Inflation forecasting using the New Keynesian Phillips Curve with a time-varying trend. Economic Modelling, 87, 383-393. doi:10.1016/j.econmod.2019.08.011
45. Meçik, O. and Karabacak, M. (2011). ARIMA modelleri ile enflasyon tahminlemesi: Türkiye uygulaması. Sosyal Ekonomik Araştırmalar Dergisi, 11(22), 177-198. Retrieved from https://dergipark.org.tr/en/pub/susead/
46. Medeiros, M.C., Vasconcelos, G.F., Veiga, Á. and Zilberman, E. (2021). Forecasting inflation in a data-rich environment: The benefits of machine learning methods. Journal of Business & Economic Statistics, 39(1), 98-119. doi:10.1080/07350015.2019.1637745
47. Moser, G., Rumler, F. and Scharler, J. (2007). Forecasting Austrian inflation. Economic Modelling, 24(3), 470–480. doi:10.1016/j.econmod.2006.10.003
48. Moshiri, S. and Cameron, N. (2000). Neural network versus econometric Models in forecasting inflation. Journal of Forecasting, 19(3), 201-217. https://doi.org/10.1002/(SICI)1099-131X(200004)19:3<201::AID-FOR753>3.0.CO;2-4
49. Nakamura, E. (2005). Inflation forecasting using a neural network. Economics Letters, 86(3), 373 – 378. doi:10.1016/j.econlet.2004.09.003
50. Nas, S., Akboz Caner, A. and Ergin Ünal, A. (2024). Türkiye’de enflasyon oranlarının makine öğrenme yöntemi ile tahmini. Gaziantep University Journal of Social Sciences, 23(3), 1029-1045. doi:10.21547/jss.1371005
51. Öğünç, F., Akdoğan, K., Başer, S., Gülenay Chadwick, M., Ertuğ, D., Hülagü, T., . . . Tekatlı, N. (2013). Short-term inflation forecasting models for Turkey and a forecast combination analysis. Economic Modelling, 33, 312-325. doi:10.1016/j.econmod.2013.04.001
52. Özgür, Ö. and Akkoç, U. (2022). Inflation forecasting in an emerging economy: Selecting variables with machine learning algorithms. International Journal of Emerging Markets, 17(8), 1889-1908. doi:10.1108/IJOEM-05-2020-0577
53. Öztemel, E. (2003). Yapay sinir ağları. İstanbul: Papatya Yayıncılık.
54. Sbrana, G., Silvestrini, A. and Venditti, F. (2017). Short-term inflation forecasting: The M.E.T.A. approach. International Journal of Forecasting, 33(4), 1065-1081. doi:10.1016/j.ijforecast.2017.06.007
55. Siami-Naimi, S., Tavakoli, N. and Namin, A. (2019). The performance of LSTM and BiLSTM in forecasting time series. Papers presented at the 2019 IEEE International Conference on Big Data. Retrieved from https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9005997
56. Song, X., Liu, Y., Xue, L., Wang, J., Zhang, J., Wang, J., . . . Cheng, Z. (2020). Time-series well performance prediction based on long short-term memory (LSTM) neural network model. Journal of Petroleum Science and Engineering, 186, 106682. https://doi.org/10.1016/j.petrol.2019.106682
57. Stock, J.H. and Watson, M.W. (1999). Forecasting inflation. Journal of Monetary Economics, 44(2), 293-335. doi:10.1016/S0304-3932(99)00027-6
58. Stock, J.H. and Watson, M.W. (2003). Forecasting output and inflation: The role of asset prices. Journal of Economic Literature, 41(3), 788–829. doi:10.1257/002205103322436197
59. Stock, J.H. and Watson, M.W. (2004). Combination forecasts of output growth in a seven country dataset. Journal of Forecasting, 23(6), 405-430. doi:10.1002/for.928
60. Stock, J.H. and Watson, M.W. (2007). Why has U.S. inflation become harder to forecast? Journal of Money, Credit and Banking, 39, 3-33. https://doi.org/10.1111/j.1538-4616.2007.00014.x
61. Stock, J.H. and Watson, M.W. (2008). Phillips Curve inflation forecasts (Working Paper No. 14322). Retrieved from https://www.nber.org/system/files/working_papers/w14322/w14322.pdf
62. Tang, Y. and Zhou, J. (2015). The Performance of PSO-SVM in Inflation Forecasting. Papers presented at the 12th International Conference on Service Systems and Service Management. Retrieved from https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7170251
63. Taylor, S.J. and Letham, B. (2018). Forecasting at scale. The American Statistician, 72(1), 37-45. https://doi.org/10.1080/00031305.2017.1380080
64. Theoharidis, A.F., Guillén, D.A. and Lopes, H. (2023). Deep learning models for inflation forecasting. Applied Stochastic Models in Business and Industry, 39(3), 447-470. doi:10.1002/asmb.2757
65. Ülke, V., Sahin, A. and Subasi, A. (2018). A comparison of time series and machine learning models for inflation forecasting: Empirical evidence from the USA. Neural Computing and Applications, 30, 1519–1527. doi:10.1007/s00521-016-2766-x
66. Winters, P.R. (1960). Forecasting Sales by Exponentially Weighted Moving Averages. Management Science, 6(3), 324–342. https://doi.org/10.1002/for.1088
67. Wright, J.H. (2009). Forecasting US inflation by Bayesian model averaging. Journal of Forecasting, 28(2), 131-144. doi:10.1002/for.1088
68. Yadav, A., Jha, C. and Sharan, A. (2020). Optimizing LSTM for time series prediction in Indian stock market. Procedia Computer Science, 167, 2091-2100. https://doi.org/10.1016/j.procs.2020.03.257
69. Zakaria, M., Al-Shebany, M. and Sarhan, S. (2014). Artificial neural network: A brief overview. Journal of Engineering Research and Applications, 4(2), 7-12. Retrieved from www.ijera.com