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Can Machine Learning Models Predict Inflation?

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  • Ivașcu Codruț

    (1 Bucharest University of Economic Studies, Bucharest, Romania)

Abstract

This paper studies the performance of Machine Learning models in inflation forecasting. The most popular algorithms have been used, respectively Support Vector Regression, Neural Networks, LSTM, Random Forest, XGBoost and LightGBM in both univariate and multivariate form, in order to predict the inflation in Romania, expressed as CPI, Core-1, Core-2 and Core-3, on multiple time horizons. The results suggest that the heuristic methods are not suited in a data-poor environment, being unable to surpass a simple autoregressive model.

Suggested Citation

  • Ivașcu Codruț, 2023. "Can Machine Learning Models Predict Inflation?," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 17(1), pages 1748-1756, July.
    • Handle: RePEc:vrs:poicbe:v:17:y:2023:i:1:p:1748-1756:n:10
    DOI: 10.2478/picbe-2023-0155
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    References listed on IDEAS

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    1. Medeiros, Marcelo C. & Mendes, Eduardo F., 2016. "ℓ1-regularization of high-dimensional time-series models with non-Gaussian and heteroskedastic errors," Journal of Econometrics, Elsevier, vol. 191(1), pages 255-271.
    2. Önder Özgür & Uğur Akkoç, 2021. "Inflation forecasting in an emerging economy: selecting variables with machine learning algorithms," International Journal of Emerging Markets, Emerald Group Publishing Limited, vol. 17(8), pages 1889-1908, February.
    3. Inoue, Atsushi & Kilian, Lutz, 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, American Statistical Association, vol. 103, pages 511-522, June.
    4. Nakamura, Emi, 2005. "Inflation forecasting using a neural network," Economics Letters, Elsevier, vol. 86(3), pages 373-378, March.
    5. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
    6. Mandalinci, Zeyyad, 2017. "Forecasting inflation in emerging markets: An evaluation of alternative models," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1082-1104.
    7. Nicoletta Batini & Andrew Haldane, 1999. "Forward-Looking Rules for Monetary Policy," NBER Chapters, in: Monetary Policy Rules, pages 157-202, National Bureau of Economic Research, Inc.
    8. Ang, Andrew & Bekaert, Geert & Wei, Min, 2007. "Do macro variables, asset markets, or surveys forecast inflation better?," Journal of Monetary Economics, Elsevier, vol. 54(4), pages 1163-1212, May.
    9. James H. Stock & Mark W. Watson, 2010. "Modeling Inflation After the Crisis," Working Papers 2010-1, Princeton University. Economics Department..
    10. Manzan, Sebastiano & Zerom, Dawit, 2013. "Are macroeconomic variables useful for forecasting the distribution of U.S. inflation?," International Journal of Forecasting, Elsevier, vol. 29(3), pages 469-478.
    11. Spyros Makridakis & Evangelos Spiliotis & Vassilios Assimakopoulos, 2018. "Statistical and Machine Learning forecasting methods: Concerns and ways forward," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-26, March.
    12. James H. Stock & Mark W. Watson, 2010. "Modeling inflation after the crisis," Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, pages 173-220.
    13. Marcelo C. Medeiros & Gabriel F. R. Vasconcelos & Álvaro Veiga & Eduardo Zilberman, 2021. "Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 98-119, January.
    14. Andrew Atkeson & Lee E. Ohanian, 2001. "Are Phillips curves useful for forecasting inflation?," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 25(Win), pages 2-11.
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