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Inflation Forecasting Using Machine Learning Methods

Author

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  • Ivan Baybuza

    (Ludwig Maximilian University of Munich)

Abstract

Inflation forecasting is an important practical problem. This paper proposes a solution to this problem for Russia using several basic machine learning methods: LASSO, Ridge, Elastic Net, Random Forest, and Boosting. Despite the fact that these methods already existed in the early 2000s, for a long time they remained almost unnoticed in the professional literature related to the forecasting of inflation in general, and Russian inflation in particular. This paper is one of the first attempts to apply machine learning methods to the forecasting of inflation in Russia. The present empirical study demostrates that the Random Forest model and the Boosting model are at least as good at inflation forecasting as more traditional models, such as Random Walk and autoregression. The main result of this paper is the confirmation of the possibility of more accurate forecasting of inflation in Russia using machine learning methods.

Suggested Citation

  • Ivan Baybuza, 2018. "Inflation Forecasting Using Machine Learning Methods," Russian Journal of Money and Finance, Bank of Russia, vol. 77(4), pages 42-59, December.
    • Handle: RePEc:bkr:journl:v:77:y:2018:i:4:p:42-59
    DOI: 10.31477/rjmf.201804.42
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    References listed on IDEAS

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    1. James H. Stock & Mark W. Watson, 2008. "Phillips curve inflation forecasts," Conference Series ; [Proceedings], Federal Reserve Bank of Boston.
    2. Chakraborty, Chiranjit & Joseph, Andreas, 2017. "Machine learning at central banks," Bank of England working papers 674, Bank of England.
    3. Garcia, Márcio G.P. & Medeiros, Marcelo C. & Vasconcelos, Gabriel F.R., 2017. "Real-time inflation forecasting with high-dimensional models: The case of Brazil," International Journal of Forecasting, Elsevier, vol. 33(3), pages 679-693.
    4. Stock, James H. & Watson, Mark, 2011. "Dynamic Factor Models," Scholarly Articles 28469541, Harvard University Department of Economics.
    5. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
    6. James H. Stock & Mark W. Watson, 2007. "Erratum to "Why Has U.S. Inflation Become Harder to Forecast?"," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(7), pages 1849-1849, October.
    7. 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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    Citations

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    Cited by:

    1. Denis Shibitov & Mariam Mamedli, 2021. "Forecasting Russian Cpi With Data Vintages And Machine Learning Techniques," Bank of Russia Working Paper Series wps70, Bank of Russia.
    2. Emmanuel O. Akande & Elijah O. Akanni & Oyedamola F. Taiwo & Jeremiah D. Joshua & Abel Anthony, 2023. "Predicting inflation component drivers in Nigeria: a stacked ensemble approach," SN Business & Economics, Springer, vol. 3(1), pages 1-32, January.
    3. Tretyakov, Dmitriy & Fokin, Nikita, 2020. "Помогают Ли Высокочастотные Данные В Прогнозировании Российской Инфляции? [Does the high-frequency data is helpful for forecasting Russian inflation?]," MPRA Paper 109556, University Library of Munich, Germany.
    4. Evgeny Pavlov, 2020. "Forecasting Inflation in Russia Using Neural Networks," Russian Journal of Money and Finance, Bank of Russia, vol. 79(1), pages 57-73, March.
    5. Vasilii Chsherbakov & Ilia Karpov, 2024. "Regional inflation analysis using social network data," Papers 2403.00774, arXiv.org, revised Mar 2024.
    6. Nikita Fokin & Andrey Polbin, 2019. "Forecasting Russia's Key Macroeconomic Indicators with the VAR-LASSO Model," Russian Journal of Money and Finance, Bank of Russia, vol. 78(2), pages 67-93, June.
    7. Urmat Dzhunkeev, 2022. "Forecasting Unemployment in Russia Using Machine Learning Methods," Russian Journal of Money and Finance, Bank of Russia, vol. 81(1), pages 73-87, March.
    8. Kirillov, Andrew, 2021. "A study on spatial autocorrelation: Case of Russian regional inflation," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 64, pages 5-22.
    9. Maiorova, Ksenia & Fokin, Nikita, 2020. "Наукастинг Темпов Роста Стоимостных Объемов Экспорта И Импорта По Товарным Группам [Nowcasting the growth rates of the export and import by commodity groups]," MPRA Paper 109557, University Library of Munich, Germany.
    10. Mikhail Gareev, 2020. "Use of Machine Learning Methods to Forecast Investment in Russia," Russian Journal of Money and Finance, Bank of Russia, vol. 79(1), pages 35-56, March.
    11. Felipe Leal & Carlos Molina & Eduardo Zilberman, 2020. "Proyección de la Inflación en Chile con Métodos de Machine Learning," Working Papers Central Bank of Chile 860, Central Bank of Chile.
    12. Viacheslav Kramkov, 2023. "Does CPI disaggregation improve inflation forecast accuracy?," Bank of Russia Working Paper Series wps112, Bank of Russia.

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    More about this item

    Keywords

    inflation forecast; machine learning; boosting; random forest;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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