What does machine learning say about the drivers of inflation?

Emanuel Kohlscheen

No 980, BIS Working Papers from Bank for International Settlements

Abstract: This paper examines the drivers of CPI inflation through the lens of a simple, but computationally intensive machine learning technique. More specifically, it predicts inflation across 20 advanced countries between 2000 and 2021, relying on 1,000 regression trees that are constructed based on six key macroeconomic variables. This agnostic, purely data driven method delivers (relatively) good outcome prediction performance. Out of sample root mean square errors (RMSE) systematically beat even the in-sample benchmark econometric models, with a 28% RMSE reduction relative to a naïve AR(1) model and a 8% RMSE reduction relative to OLS. Overall, the results highlight the role of expectations for inflation outcomes in advanced economies, even though their importance appears to have declined somewhat during the last 10 years.

Keywords: expectations; forecast; inflation; machine learning; oil price; output gap; Phillips curve (search for similar items in EconPapers)
JEL-codes: E27 E30 E31 E37 E52 F41 (search for similar items in EconPapers)
Pages: 22 pages
Date: 2021-11
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cwa, nep-mac and nep-mon
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Related works:
Working Paper: What does machine learning say about the drivers of inflation? (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:bis:biswps:980

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