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What does machine learning say about the drivers of inflation?

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  • Emanuel Kohlscheen

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. Partial effects of inflation expectations on CPI outcomes are also elicited in the paper. 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.

Suggested Citation

  • Emanuel Kohlscheen, 2022. "What does machine learning say about the drivers of inflation?," Papers 2208.14653, arXiv.org, revised Jan 2023.
    • Handle: RePEc:arx:papers:2208.14653
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    1. Kristin Forbes, 2019. "Has globalization changed the inflation process?," BIS Working Papers 791, Bank for International Settlements.
    2. Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan & Ziad Obermeyer, 2015. "Prediction Policy Problems," American Economic Review, American Economic Association, vol. 105(5), pages 491-495, May.
    3. Chakraborty, Chiranjit & Joseph, Andreas, 2017. "Machine learning at central banks," Bank of England working papers 674, Bank of England.
    4. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    5. Kamber, Güneş & Wong, Benjamin, 2020. "Global factors and trend inflation," Journal of International Economics, Elsevier, vol. 122(C).
    6. 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.
    7. Tommaso Monacelli & Luca Sala, 2009. "The International Dimension of Inflation: Evidence from Disaggregated Consumer Price Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(s1), pages 101-120, February.
    8. Christian Gillitzer & Martin McCarthy, 2019. "Does global inflation help forecast inflation in industrialized countries?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(5), pages 850-857, August.
    9. Claudio E. V. Borio & Andrew Filardo, 2007. "Globalisation and inflation: New cross-country evidence on the global determinants of domestic inflation," BIS Working Papers 227, Bank for International Settlements.
    10. Jeremy B. Rudd, 2021. "Why Do We Think That Inflation Expectations Matter for Inflation? (And Should We?)," Finance and Economics Discussion Series 2021-062, Board of Governors of the Federal Reserve System (U.S.).
    11. Haroon Mumtaz & Paolo Surico, 2012. "Evolving International Inflation Dynamics: World And Country-Specific Factors," Journal of the European Economic Association, European Economic Association, vol. 10(4), pages 716-734, August.
    12. Martina Jašová & Richhild Moessner & Elöd Takáts, 2019. "Exchange Rate Pass-Through: What Has Changed Since the Crisis?," International Journal of Central Banking, International Journal of Central Banking, vol. 15(3), pages 27-58, September.
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    Cited by:

    1. Douglas Kiarelly Godoy de Araujo, 2023. "gingado: a machine learning library focused on economics and finance," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Data science in central banking: applications and tools, volume 59, Bank for International Settlements.
    2. Araujo, Gustavo Silva & Gaglianone, Wagner Piazza, 2023. "Machine learning methods for inflation forecasting in Brazil: New contenders versus classical models," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 4(2).

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    More about this item

    JEL classification:

    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • F41 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Open Economy Macroeconomics

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