Modelling and forecasting inflation rate in Nigeria using ARIMA models

Olalude, Gbenga Adelekan and Olayinka, Hammed Abiola and Ankeli, Uchechi Constance (2020): Modelling and forecasting inflation rate in Nigeria using ARIMA models. Published in: KASU Journal of Mathematical Sciences , Vol. 1, No. 2 (6 January 2021): pp. 127-143.

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;

• Item Type: MPRA Paper
• Language: English
• Keywords: Inflation; Modelling; Forecasting; ARMA; ARIMA; SARMA; SARIMA
• Subjects: C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation, Validation, and Selection; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E31 - Price Level ; Inflation ; Deflation; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications; E - Macroeconomics and Monetary Economics > E4 - Money and Interest Rates > E47 - Forecasting and Simulation: Models and Applications

References

Adebiyi, M. A., Adenuga, A. O., Abeng, M. O., Omanukwe, P. N., & Ononugbo, M. C. (2010). Inflation forecasting models for Nigeria, Central Bank of Nigeria Occasional Paper No. 36, Abuja, Research and Statistics Department.

Adenekan, A. T., and Nwanna, G. A. (2004). Inflation Dynamics in a developing economy: An error correction approach. African Review of Money Finance and Banking, 77-99

Balcilar, M., R. Gupta, & K. Kotze (2015). Forecasting Macroeconomic Data for an Emerging Market with a Nonlinear DSGE Model, Economic Modelling 44, 215-228.

Chen, Y.C., S. J. Turnovsky, & E. Zivot (2014). Forecasting Inflation Using Commodity Price Aggregates, Journal of Econometrics 183, 117-134.

Doguwa, S. I. and Alade, S. O. (2013): Short-term Inflation Forecasting Models for Nigeria. CBN Journal of Applied Statistics, 4 (2), 1-29.

Etuk, E. H., Uchendu, B. & Victoredema, U. A. (2012). Forecasting Nigeria Inflation Rates by a Seasonal ARIMA Model, Canadian Journal of Pure and Applied Sciences, 6 (3), 2179-2185.

Fwaga S. O., Orwa, G & Athiany, H (2017). Modeling Rates of Inflation in Kenya: An Application of GARCH and EGARCH models, Mathematical Theory and Modelling, 7(5): 75 – 83.

Hadrat, Y. M, Isaac E, N., & Eric E, S. (2015) Inflation Forecasting in Ghana Artificial Neural Network Model Approach, Int. J. Econ. Manag. Sci 4: 274.

Iftikhar, N. & Iftikhar-ul-amin (2013). Forecasting the Inflation in Pakistan – The Box-Jenkins Approach, World Applied Sciences Journal 28 (11): 1502-1505.

Imimole, B., and Enoma, A. (2011). Exchange rate depreciation and inflation in Nigeria (1986-2008), Business and Economics Journal, Vol. 2011: BEJ-28.

Inam, U. S. (2017). Forecasting Inflation in Nigeria: A Vector Autoregression Approach, International Journal of Economics, Commerce and Management, 5(1), 92-104.

Ingabire. J & Mung'atu. J. K. (2016). Measuring the Performance of Autoregressive Integrated Moving Average and Vector Autoregressive Models in Forecasting Inflation Rate in Rwanda, International Journal of Mathematics and Physical Sciences Research, 4(1) 15-25

Jere, S., & Siyanga, M. (2016). Forecasting inflation rate of Zambia using Holt's exponential smoothing. Open Journal of Statistics, 6(02), 363.

John, E. E., & Patrick, U. U. (2016). Short-term forecasting of Nigeria inflation rates using seasonal ARIMA Model. Science Journal of Applied Mathematics and Statistics, 4(3), 101-107.

Kabukcuoglu, A. and E. Martnez-Garca (2018). Inflation as a Global Phenomenon: Some Implications for Inflation Modelling and Forecasting, Journal of Economic Dynamics and Control 87(2), 46-73.

Kelikume, I., & Salami, A. (2014). Time Series Modelling and Forecasting Inflation: Evidence from Nigeria. The International Journal of Business and Finance Research,8(2) 91-98.

Lidiema, C. (2017). Modeling and Forecasting Inflation Rate in Kenya Using SARIMA and Holt-Winters Triple Exponential Smoothing. American Journal of Theoretical and Applied Statistics, 6(3): 161-169.

Mandalinci, Z. (2017). Forecasting Inflation in Emerging Markets: An Evaluation of Alternative Models, International Journal of Forecasting 33(4), 1082-1104.

Molebatsi, K., & Raboloko, M. (2016). Time Series Modelling of Inflation in Botswana Using Monthly Consumer Price Indices, International Journal of Economics and Finance, 8(3), 15.

Nyoni, T & Bonga, W. G (2018). What Determines Economic Growth In Nigeria? DRJ – Journal of Business and Management, 1 (1): 37 – 47.

Nyoni, T. & Nathaniel, S. P. (2019). Modeling Rates of Inflation in Nigeria: An Application of ARMA, ARIMA, and GARCH models. Online at https://mpra.ub.uni-muenchen.de/91351/MPRA Paper No. 91351

Nyoni, T (2018). Modeling and Forecasting Inflation in Zimbabwe: a Generalized Autoregressive Conditionally Heteroscedastic (GARCH) approach, Munich University Library – Munich Personal RePEc Archive (MPRA), Paper No. 88132.

Okafor, C., & Shaibu, I. (2013). Application of ARIMA models to Nigerian inflation dynamics, Research Journal of Finance and Accounting, 4(3), 138-150.

Otu, A. O., Osuji, G. A., Opara, J., Mbachu, H. I., & Iheagwara, A. I. (2014). Application of Sarima models in modeling and forecasting Nigeria's inflation rates, American Journal of Applied Mathematics and Statistics, 2(1), 16-28.

Pincheira, P. M. & C. A. Medel (2015). Forecasting Inflation with a Simple and Accurate Benchmark: The Case of the U.S. and a Set of Inflation Targeting Countries, Czech Journal of Economics and Finance 65(1).

Uwilingiyimana, C., Munga'tu, J., & Harerimana, J. D. D. (2015). Forecasting Inflation in Kenya Using Arima-Garch Models, International Journal of Management and Commerce Innovations, 3(2):15-27.

Yaya, O.S. & Fashae, O.A. (2014). Seasonal fractional integrated time series models for rainfall data in Nigeria. J. Theoret. Appl. Climatol. 120(2), 99–108

Yusif, M. H., Eshun Nunoo, I. K., & Effah Sarkodie, E. (2015). Inflation Forecasting in Ghana-Artificial Neural Network Model Approach. International Journal of Economics & Management Sciences, 4: 274, doi:10.4172/216263