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Modelling and forecasting inflation rate in Nigeria using ARIMA models

Author

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  • Olalude, Gbenga Adelekan
  • Olayinka, Hammed Abiola
  • Ankeli, Uchechi Constance

Abstract

This study modelled and forecast inflation in Nigeria using the monthly Inflation rate series that spanned January 2003 to October 2020 and provided three years monthly forecast for the inflation rate in Nigeria. We examined 169 ARMA, 169 ARIMA, 1521 SARMA, and 1521 SARIMA models to identify the most appropriate model for modelling the inflation rate in Nigeria. Our findings indicate that out of the 3380 models examined, SARMA (3, 3) x (1, 2)12 is the best model for forecasting the monthly inflation rate in Nigeria. We selected the model based on the lowest Akaike Information Criteria (AIC) and Schwarz Information Criterion (SIC) values, volatility, goodness of fit, and forecast accuracy measures, such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The AIC and SIC of the model are 3.3992 and 3.5722, respectively with an adjusted R2 value of 0.916. Our diagnostic tests (Autocorrelation and Normality of Residuals) and forecast accuracy measures indicate that the presented model, SARMA (3, 3)(1, 2)12, is good and reliable for forecasting. Finally, the three years monthly forecast was made, which shows that the Inflation rate in Nigeria would continue to decrease but maintain a 2 digits value for the next two years, but is likely to rise again in 2023. This study is of great relevance to policymakers as it provides a foresight of the likely future inflation rates in Nigeria. Keywords: Inflation; Modelling, Forecasting; ARMA; ARIMA; SARMA; SARIMA;

Suggested Citation

  • Olalude, Gbenga Adelekan & Olayinka, Hammed Abiola & Ankeli, Uchechi Constance, 2020. "Modelling and forecasting inflation rate in Nigeria using ARIMA models," MPRA Paper 105342, University Library of Munich, Germany, revised Dec 2020.
  • Handle: RePEc:pra:mprapa:105342
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    References listed on IDEAS

    as
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    Cited by:

    1. Abir HASSAN & Mahbubul Md. ALAM & Azmaine FAEIQUE, 2023. "Forecasting Monthly Inflation in Bangladesh: A Seasonal Autoregressive Moving Average (SARIMA) Approach," Journal of Economics and Financial Analysis, Tripal Publishing House, vol. 7(2), pages 25-43.

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    More about this item

    Keywords

    Inflation; Modelling; Forecasting; ARMA; ARIMA; SARMA; SARIMA;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • 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
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications

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