Inflation Forecasting Using Machine Learning Methods

Ivan Baybuza ()
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Ivan Baybuza: Ludwig Maximilian University of Munich

Russian Journal of Money and Finance, 2018, vol. 77, issue 4, 42-59

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.

Keywords: inflation forecast; machine learning; boosting; random forest (search for similar items in EconPapers)
JEL-codes: C53 E37 (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:bkr:journl:v:77:y:2018:i:4:p:42-59

DOI: 10.31477/rjmf.201804.42

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