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Asymmetric drivers of inflation: new evidence from machine learning and quantile method

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  • Imandojemu, Kingsley
  • Habib, Adetutu Omotola
  • Showunmi, Omozele Lynda
  • Agboola, Loveth Oribhabor

Abstract

This paper investigates the complex, nonlinear forces behind price movements in Nigeria by applying quantile econometric techniques. Using monthly data from December 2012 to August 2024, the analysis applies Elastic Net Regression for variable selection and employs Quantile-on-Quantile Kernel Regularized Least Squares (QQKRLS) alongside Quantile-on-Quantile Granger Causality (QQGC) tests. The results show that while money supply consistently drives inflation, the effects of other variables are regime-dependent; for instance, private sector credit fuels inflation in moderate-to-high periods, while bank reserves can dampen it in moderate ones. Furthermore, the analysis confirms a directional causality from these variables of interest to inflation, with the strength of the relationship varying significantly across quantiles. The results reveal that uniform policies are inadequate. Policymakers should, therefore, adopt quantile-specific and context-sensitive fiscal and monetary strategies to ensure durable price stability in Nigeria.

Suggested Citation

  • Imandojemu, Kingsley & Habib, Adetutu Omotola & Showunmi, Omozele Lynda & Agboola, Loveth Oribhabor, 2026. "Asymmetric drivers of inflation: new evidence from machine learning and quantile method," The North American Journal of Economics and Finance, Elsevier, vol. 81(C).
    • Handle: RePEc:eee:ecofin:v:81:y:2026:i:c:s1062940825001913
    DOI: 10.1016/j.najef.2025.102551
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    References listed on IDEAS

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    1. Tule, Moses K. & Salisu, Afees A. & Chiemeke, Charles C., 2019. "Can agricultural commodity prices predict Nigeria's inflation?," Journal of Commodity Markets, Elsevier, vol. 16(C).
    2. Udo Emmanuel Smauel & Ben Etim Udoh & Abner Ishaku Prince & Okoh, Johnson Ifeanyi & Okolo Marvis Ndu, 2019. "Money Supply and Inflation Rate in Nigeria: The Missing Link," Humanities and Social Sciences Letters, Conscientia Beam, vol. 7(3), pages 156-166.
    3. Kenneth S. Rogoff, 2003. "Globalization and global disinflation," Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, pages 77-112.
    4. Jongrim Ha & M. Ayhan Kose & Franziska Ohnsorge & Hakan Yilmazkuday, 2023. "Understanding the Global Drivers of Inflation: How Important are Oil Prices?," Koç University-TUSIAD Economic Research Forum Working Papers 2301, Koc University-TUSIAD Economic Research Forum.
    5. Moses Tule & Afees A. Salisu & Charles Chimeke, 2018. "You are what you eat: The role of oil price in Nigeria inflation forecast," Working Papers 040, Centre for Econometric and Allied Research, University of Ibadan.
    6. T.O. Akinbobola, 2012. "The dynamics of money supply, exchange rate and inflation in Nigeria," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 2(4), pages 1-8.
    7. Olumide O. Olaoye & O. J. Omokanmi & Mosab I. Tabash & S. O. Olofinlade & M. O. Ojelade, 2024. "Soaring inflation in sub-Saharan Africa: A fiscal root?," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(1), pages 987-1009, February.
    8. Ayinde, Opeyemi Eyitayo & Olatunji, G.B. & Omotesho, O.A. & Ayinde, Kayode, 2010. "Determinants Of Inflation In Nigeria: A Co- Integration Approach," 2010 AAAE Third Conference/AEASA 48th Conference, September 19-23, 2010, Cape Town, South Africa 94301, African Association of Agricultural Economists (AAAE).
    9. Olatunji A. SHOBANDE & Lanre R. IBRAHIM, 2018. "Modelling dynamics of inflation in Nigeria," Journal of Economics Library, EconSciences Journals, vol. 5(3), pages 229-240, September.
    10. Gary G. Moser, 1995. "The Main Determinants of Inflation in Nigeria," IMF Staff Papers, Palgrave Macmillan, vol. 42(2), pages 270-289, June.
    11. Afees A. Salisu & Elias A. Udeaja & Silva Opuala-Charles, 2022. "Central Bank Independence and Price Stability Under Alternative Political Regimes: A Global Evidence," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 25(2), pages 155-172, August.
    12. Afees Salisu & Sulaiman Salisu & Subair Salisu, 2024. "A news-based economic policy uncertainty index for Nigeria," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(5), pages 4987-5002, October.
    13. Qi, Chaoping & Ma, Yu & Du, Meng & Ma, Xiaoxian & Xu, Yuan & Zhou, Xiangjun, 2025. "Impacts of climate change on inflation: An analysis based on long and short term effects and pass-through mechanisms," International Review of Economics & Finance, Elsevier, vol. 98(C).
    14. Olatunji, G.B. & Omotesho, O.A. & Ayinde, Opeyemi Eyitayo & Ayinde, Kayode, 2010. "Determinants of Inflation in Nigeria: A Co- Integration Approach," 2010 AAAE Third Conference/AEASA 48th Conference, September 19-23, 2010, Cape Town, South Africa 96162, African Association of Agricultural Economists (AAAE).
    15. ODUSOLA, Ayodele & AKINLO, Anthony, 2001. "Output, Inflation, And Exchange Rate In Developing Countries: An Application To Nigeria," UNDP Africa Economists Working Papers 307343, United Nations Development Programme (UNDP).
    16. Omid Asadollah & Linda Schwartz Carmy & Md. Rezwanul Hoque & Hakan Yilmazkuday, 2024. "Geopolitical risk, supply chains, and global inflation," The World Economy, Wiley Blackwell, vol. 47(8), pages 3450-3486, August.
    17. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    18. Sikiru, Adeyemi Abidemi & Salisu, Afees A., 2025. "Transition to inflation targeting monetary policy framework in Nigeria," MPRA Paper 123528, University Library of Munich, Germany.
    19. Yilmazkuday, Hakan, 2022. "Drivers of Turkish inflation," The Quarterly Review of Economics and Finance, Elsevier, vol. 84(C), pages 315-323.
    20. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    21. Eregha, Bright & Mesagan, Ekundayo & Ayoola, Olawale, 2015. "Petroleum Products Prices and Inflationary Dynamics in Nigeria," MPRA Paper 70251, University Library of Munich, Germany.
    22. Ahmed, Walid M.A. & Sleem, Mohamed A.E., 2023. "Short- and long-run determinants of the price behavior of US clean energy stocks: A dynamic ARDL simulations approach," Energy Economics, Elsevier, vol. 124(C).
    23. Salisu, Afees A. & Isah, Kazeem O. & Oyewole, Oluwatomisin J. & Akanni, Lateef O., 2017. "Modelling oil price-inflation nexus: The role of asymmetries," Energy, Elsevier, vol. 125(C), pages 97-106.
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