Article Sidebar

Submitted
September 8, 2021
Accepted
March 10, 2022
Published
April 26, 2022
Download
PDF
Statistic
Read Counter : 819 Download : 1191

Downloads

Download data is not yet available.

Main Article Content

Abstract

Purpose: This paper aims to test the accuracy of some Machine Learning (ML) models in forecasting inflation in the case of Turkey and to give a new and also complementary approach to time series models.  Methods: This paper forecasts inflation in Turkey by using time-series and machine learning (ML) models. The data is spanning from the period 2006:M1 to 2020:M12. Findings: According to our findings, although the linear-based Ridge and Lasso regression algorithms perform worse than the VAR model, the multilayer perceptron algorithm gives satisfactory results that are close to the results of the time series algorithm. In this direction, non-linear machine learning models are thought to be a reliable complementary method for estimating inflation in emerging economies. It is also predicted that it can be considered as an alternative method as the amount of data and computational power increase. 

Implication: The findings are expected to be useful as a guide for central banks and policy-makers in emerging economies with volatile inflation rates.

 Originality: We evaluate the forecasting performance of ML models against each other and a time series model, and investigate possible improvements upon the naive model. So, this is the first study in the field, which uses both linear and nonlinear ML methods to make a comparison with the time series inflation forecasts for Turkey.

Keywords

inflation forecasting time series models machine learning models emerging economies

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

How to Cite
Akbulut, H. (2022). Forecasting inflation in Turkey: A comparison of time-series and machine learning models. Economic Journal of Emerging Markets, 14(1), 55–71. https://doi.org/10.20885/ejem.vol14.iss1.art5
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX

References

  1. Almosova, A., & Andresen, N. (2019). Nonlinear inflation forecasting with recurrent neural networks (2019/5; European Central Bank Technical Report).
  2. Altimari, S. N. (2001). Does Money Lead Inflation in the Euro Area? (No. 63; Working Paper Series).
  3. Ball, L., & Mazumder, S. (2019). A Phillips Curve with Anchored Expectations and Short-Term Unemployment. Journal of Money, Credit and Banking, 51(1), 111–137. https://doi.org/10.1111/jmcb.12502
  4. Ball, L., & Mazumder, S. (2020). A Phillips curve for the euro area (No. 2354; ECB Working Paper Series).
  5. Banerjee, A., Cockerell, L., & Russell, B. (2001). An I(2) analysis of inflation and the markup. Journal of Applied Econometrics, 16(3), 221–240. https://doi.org/10.1002/jae.609
  6. Barkan, O., Benchimol, J., Caspi, I., Cohen, E., Hammer, A., & Koenigstein, N. (2021). Forecasting CPI inflation components with hierarchical recurrent Neural Network. In In preparation: Revise and Resubmit, International Journal of Forecasting.
  7. Baybuza, I. (2018). Inflation forecasting using machine learning methods. Russian Journal of Money and Finance, 77(4), 42–59. https://doi.org/10.31477/rjmf.201804.42
  8. Bennouna, H. (2015). A mark-up model of inflation for Morocco. International Journal of Economics and Financial Issues, 5(1), 281–287.
  9. Brouwer, G. De, & Ericsson, N. R. (1998). Modeling Inflation in Australia. Journal of Business and Economic Statistics, 16(4), 433–449. https://doi.org/10.1080/07350015.1998.10524783
  10. Bulut, U. (2016). Do financial conditions have a predictive power on inflation in Turkey? International Journal of Economics and Financial Issues, 6(2), 621–628.
  11. Callen, T., & Chang, D. (1999). Modeling and Forecasting Inflation in India (WP/99/119; IMF Working Paper).
  12. CBRT. (2021). Central bank of Republic of Turkey, electronical data distribution system.
  13. Chakraborty, C., & Joseph, A. (2017). Machine learning at central banks (No. 674).
  14. Chang, J. Y., Pigorini, A., Massimini, M., Tononi, G., Nobili, N., & Van Veen, B. D. (2012). Multivariate autoregressive models with exogenous inputs for intracerebral responses to direct electrical stimulation of the human brain. Frontiers in Human Neuroscience, 6(317). https://doi.org/10.3389/fnhum.2012.00317
  15. Chen, Y. G. (2019). Inflation, Inflation Expectations, and the Phillips Curve (No. 2019–17; Working Paper Series).
  16. Christopher, B., & Jansen, E. S. (2004). A markup model of inflation for the euro area (Working Paper Series 306). http://ssrn.com/abstract_id=515068
  17. Coe, D. T., & McDermott, C. J. (1997). Does the Gap Model Work in Asia? IMF Staff Papers, 44(1), 59–80. https://doi.org/10.2307/3867497
  18. Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 76–66. https://doi.org/10.2307/2286348
  19. Garcia, M. G., Medeiros, M. C., & Vasconcelos, G. (2017). Real-time inflation forecasting with high-dimensional models: The case of Brazil. International Journal of Forecasting, 33(3), 679–693. https://doi.org/10.1016/j.ijforecast.2017.02.002
  20. Gungor, C., & Berk, A. (2006). Money supply and inflation relationship in the Turkish Economy. Journal of Applied Science, 6(9), 2083–2087. https://doi.org/10.3923/jas.2006.2083.2087
  21. Hoerl, A. E., & Kennard, R. W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. https://doi.org/10.1080/00401706.1970.10488634
  22. Jonsson, G. (2001). Inflation, money demand, and purchasing power parity in South Africa. IMF Staff Papers, 48(2), 243–265. https://doi.org/10.5089/9781451854473.001
  23. Kumar, V., Leona, R. P., & Gaskins, J. N. (1995). Aggregate and disaggregate sector forecasting using consumer confidence measures. International Journal of Forecasting, 11(3), 361–377. https://doi.org/10.1016/0169-2070(95)00594-2
  24. Lim, C. H., & Papi, L. (1997). An econometric analysis of the determinants of inflation in Turkey (97/170; IMF Working Paper).
  25. Medeiros, M. C., Vasconcelos, G. F., & De Freitas, E. H. (2016). Forecasting Brazilian inflation with high dimensional models. Brazilian Review of Econometrics, 36(2), 223–254. https://doi.org/10.12660/bre.v99n992016.52273
  26. Medeiros, M. C., Vasconcelos, G. F. R., Veiga, Á., & 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. https://doi.org/10.1080/07350015.2019.1637745
  27. Mishkin, F. S. (2000). Para teorisi ve politikası (The Economics of money, banking, and financial markets, financial times prentice hall). Bilim Teknik Yayınevi.
  28. MTFRT. (2021). Republic of Turkey Ministry of treasury and finance: Budget Statistics.
  29. Muller, A. C., & Guido, S. (2017). Introduction to machine learning with Python: A guide for data scientists. O’Reilly Media, Inc.
  30. Nakamura, E. (2005). Inflation forecasting using a neural network. Economic Letters, 86(3), 373–378. https://doi.org/10.1016/j.econlet.2004.09.003
  31. Önder, A. Ö. (2004). Forecasting Inflation in Emerging Markets by Using the Phillips Curve and Alternative Time Series Models. Emerging Markets Finance and Trade, 40(2), 71–82. https://doi.org/10.1080/1540496x.2004.11052566
  32. Özgür, Ö., & Akkoç, U. (2021). Inflation forecasting in an emerging economy: Selecting variables with machine learning algorithms. In International Journal of Emerging Markets. https://doi.org/10.1108/IJOEM-05-2020-0577
  33. Rodríguez-Vargas, A. (2020). Forecasting Costa Rican inflation with machine learning methods. Latin American Journal of Central Banking, 1(1), 100012. https://doi.org/https://doi.org/10.1016/j.latcb.2020.100012
  34. Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. In Psychological Review (Vol. 65, Issue 6, pp. 386–408). American Psychological Association. https://doi.org/10.1037/h0042519
  35. Sims, C. (1980). Macroeconomics and Reality. Econometrica, 48(1), 1–48. https://doi.org/10.2307/1912017
  36. Stock, J. H., & Watson, M. W. (2013). Phillips Curve Inflation Forecasts. In Understanding Inflation and the Implications for Monetary Policy (No. 14322; NBER Working Paper). https://doi.org/10.7551/mitpress/9780262013635.003.0003
  37. Stock, J., & Watson, M. (1999). A comparison of linear and nonlinear univariate models for forecasting macroeconomic time series. In R. Engle & H. White (Eds.), Cointegration, Causality and Forecasting: A Festschrift for Clive W.J. Granger (pp. 1–44). Oxford University Press.
  38. Tibshirani, R. (2016). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society Series B ( Methodological ), 58(1), 267–288.
  39. Ülke, V., Sahin, A., & 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(5), 1519–1527. https://doi.org/10.1007/s00521-016-2766-x
  40. Wojciech, D., & Derek, F. D. (1992). New directions in econometric practice general to spesific modelling, cointegration and vector autoregressions. Edward Elgar Publishing.