Economic and Financial Review


Central Bank of Nigeria


Big data, Forecast, Inflation


The success of monetary policy is substantially predicated on the availability of reliable forecast of inflation. However, the shocks arising from COVID-19 and the Russia-Ukraine war have brought about significant economic uncertainties; thus, necessitating the fine-tuning of existing forecasting models of the Central Bank of Nigeria. This study explores the usefulness of public sentiments obtained using machine learning methods to improve the predictive power of the existing short-term inflation forecasting model (STIF) in Nigeria. Findings indicate that, for all components of inflation, models that include the computed sentiment index perform better in both in-sample and out-sample forecasts than those excluding the index. Thus, we conclude that sentiment-based inflation forecasting models are useful for improving the headline inflation forecast and suggest the use of forward guidance monetary instruments in the form of “open mouth operations”, to ensure economic agents’ sentiments are well anchored.

Author Bio

Adebiyi, M. A. is the Director, Research Department, while Adenuga, A. O., Olusegun, T. S., and Mbutor, O. O. are staff of the Department, Central Bank of Nigeria.

Publication Title

CBN Economic and Financial Review





First Page


Last Page



C53, C55, E31

Recommended Citation

Adebiyi, M.A., Adenuga, A.O., Olusegun, T.S. & Mbutor, O.O. (2022). Big Data and Inflation Forecasting in Nigeria: a text mining application. CBN Economic and Financial Review, 60(1). 1 - 23.



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