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Forecasting inflation using machine learning techniques

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  • Musa Nakorji
  • Umaru Aminu

Abstract

Inflation forecasting is key in achieving the Central Bank mandate of price stability the world over. Different traditional methods were used to forecast inflation with little or no attention given to the area of forecasting the inflation rate in Nigeria using machine learning techniques. Data was sourced from CBN statistical bulletin (2021) on monthly basis. The study found that ridge regression and Artificial Neural Networks are the best in forecasting inflation in Nigeria when compared with the LASSO, elastic net, and PLS. The study further reveals that the major drivers of headline inflation in Nigeria were food inflation, core inflation, prime lending rate, maximum lending rate, and the inter-bank rate. The study recommends that ridge regression and Artificial Neural Network machine learning techniques be used in forecasting the inflation rate in Nigeria. Also, recommended is the need for the monetary authorities to focus more on ways to improve food production by improving security.

Suggested Citation

  • Musa Nakorji & Umaru Aminu, 2022. "Forecasting inflation using machine learning techniques," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 14(1), pages 45-55, June.
    • Handle: RePEc:rfb:journl:v:14:y:2022:i:1:p:45-55
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    References listed on IDEAS

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    1. Ikechukwu Kelikume & Adedoyin Salami, 2014. "Time Series Modeling and Forecasting Information: Evidence from Nigeria," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 8(2), pages 41-51.
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