 Forecasting Inflation in Russia Using Neural NetworksEvgeny Pavlov ()
Russian Journal of Money and Finance, 2020, vol. 79, issue 1, 57-73 Abstract: Forecasting Russian inflation is an important practical task. This paper applies two benchmark machine learning models to this task. Although machine learning in general has been an active area of research for the past 20 years, those methods began gaining popularity in the literature on inflation forecasting only recently. In this paper, I employ neural networks and support-vector machines to forecast inflation in Russia. I also apply Shapley decomposition to obtain economic interpretation of inflation forecasts. The performance of these two models is then compared with the performance of more conventional approaches that serve as benchmark forecasts. These are an autoregression and a linear regression with regularisation (a.k.a. ridge regression). My empirical findings suggest that both machine learning models forecast inflation no worse than the conventional benchmarks and that the Shapley decomposition is a suitable framework that yields a meaningful interpretation to the neural network forecast. I conclude that machine learning methods offer a promising tool of inflation forecasting. Keywords: inflation forecast; machine learning; ridge regression; neural networks; support-vector machines (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bkr:journl:v:79:y:2020:i:1:p:57-73 Access Statistics for this article Russian Journal of Money and Finance is currently edited by Ksenia Yudaeva More articles in Russian Journal of Money and Finance from Bank of Russia Contact information at EDIRC. |
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