Journal of Economics and Financial Analysis, 7 (2), pp. 25-43, [2023]
URI: https://ojs.tripaledu.com/jefa/article/view/85/95

Forecasting Monthly Inflation in Bangladesh: A Seasonal Autoregressive Moving Average (SARIMA) Approach


Bangladesh University of Professionals, Bangladesh


Bangladesh University of Professionals, Bangladesh


Bangladesh University of Professionals, Bangladesh


DOI: http://dx.doi.org/10.1991/jefa.v7i2.a61

Abstract

The objective of this study is to forecast the trend of inflation in Bangladesh by utilizing past inflation data. To achieve this objective, we employed the Seasonal Autoregressive Integrated Moving Average (SARIMA) model which is an extension of the Autoregressive Integrated Moving Average (ARIMA) model. Monthly inflation data used for forecasting were derived from the Consumer Price Index (CPI) data obtained from the International Monetary Fund (IMF) database, covering the period from January 2010 to January 2023. Our analysis reveals that the SARIMA (2,0,0)×(1,0,1)12 model is the most appropriate fit. Based on this finding, we predicted the inflation trend in Bangladesh from February 2023 to December 2024. A comparison of our predicted values with the actual values indicates a high degree of correlation between the two. Although a few discrepancies were observed, they did not undermine our prediction since the parameters of the model lay within the 95% confidence interval.

Keywords

C51, C53, E31, E37.

JEL Classification

Inflation, Seasonality, SARIMA, Bangladesh.

Full Text:

PDF

References

Banerjee, A. (2021). Forecasting Price Levels in India: An ARIMA Framework. Academy of Marketing Studies Journal, 25(1), 1-5.

Box, G.E., & Jenkins, G.M. (1976). Time series analysis: Forecasting and Control. San Francisco. Calif: Holden-Day.

Enke, D., & Mehdiyev, N. (2014). A Hybrid Neuro-fuzzy Model to Forecast Inflation. Procedia Computer Science, 36, 254-260. doi:https://doi.org/10.1016/j.procs.2014.09.088

Farida, E., & As’ad, M. (2021). The Forecasting of Monthly Inflation in Malang City Using An Autoregressive Integrated Moving Average. International Journal of Economics, Business and Accounting Research, 5(2).

Faulina, R., Lusia, D., Otok, B., & Kuswanto, H. (2012). Ensemble method based on anfis-arima for rainfall prediction. 2012 International Conference on Statistics in Science, Business and Engineering, (pp. 1-4). Langkawi: IEEE.

Gautam, R.S., & Kanoujiya, J. (2022). Inflation Targeting: An Application of ARIMA Modelling Using Forecasting of CPI and WPI. Iconic Research and Engineering Journals, 5(11), 195-198.

Ghazo, A. (2021). Applying the ARIMA Model to the Process of Forecasting GDP and CPI in the Jordanian Economy. International Journal of Financial Research, 12(3), 70.

Hurtado, J., & Cortes-Fregoso, J. (2013). Forecasting Mexican inflation using neural networks. 23rd International Conference on Electronics, Communications and Computing (pp. 32-35). Mexico: IEEE.

Hurvich, C.M., & Tsai, C.L. (1989). Regression and time series model selection in small samples. Biometrika, 76(2), 297-307.

Işığıçok, E., Öz, R., & Tarkun, S. (2020). Forecasting and technical comparison of inflation in Turkey with box-jenkins (ARIMA) models and the artificial neural network. International Journal of Energy Optimization and Engineering, 9(4), 84--103.

Karadzic, V., & Pejovic, B. (2021). Inflation Forecasting in the Western Balkans and EU: A Comparison of Holt-Winters, ARIMA and NNAR Models. Academy of Economic Studies, 23(57), 517.

Kharimah, F., Usman, M., Widiarti, W., & Elfaki, F. (2015). Time series modeling and forecasting of the consumer price index Bandar Lampung. Science International, 27(5B), 4619-4624.

Kleiber, C., & Zeileis, A. (2008). Applied econometrics with R. New York: Springer Science & Business Media, LLC.

Lidiema, C. (2017). Modelling 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.

Liko, R., Ramosaco, M., & Kashuri, A. (2016). Modeling the Behavior of Inflation Rate in Albania Using Time Series. Journal of Advance in Mathematics, 13(3), 7257-7263.

Marpaung, G.N., Soesilowati, E., Rahman, Y.A., Tegar, Y.D., & Yuliani, R. (2022). Forecasting the Inflation Rate in Central Java Using ARIMA Model. Indonesian Journal of Development Economics, 5(2), 163-173.

McNelis, P.D., & McAdam, P. (2004). Forecasting Inflation with Thick Models and Neural Networks. Frankfurt: European central Bank.

Mia, M.S., Nabeen, A.H., & Akter, M.M. (2019). Modelling and forecasting the consumer price index in Bangladesh through econometric models. American Scientific Research Journal for Engineering, Technology, and Sciences, 59(1), 118-127.

Ngailo, E., Luvanda, E., & Massawe, E.S. (2014). Time Series Modelling with Application to Tanzania Inflation Data. Journal of Data Analysis and Information Processing, 2(2), 49-59.

Nyoni, T. (2019). Munich Personal RePEc Archive (MPRA). Retrieved April 1, 2023, from https://mpra.ub.uni-muenchen.de/92429/

Okyere, F., & Mensah, C. (2014). Empirical Modelling and Model Selection for Forecasting Monthly Inflation of Ghana. Mathematical Theory and Modeling, 4(3), 99-106.

Olalude, G.A., Olayinka, H.A., & Constance, A.U. (2020). Modelling and forecasting inflation rate in Nigeria using ARIMA models. KASU Journal of Mathematical Science, 1(2), 127--143.

Otu, O.A., Osuji, G.A., Jude, O., Ifeyinwa, M.H., & Iheagwara, A.I. (2014). Application of Sarima models in modelling and forecasting Nigeria’s inflation rates. American Journal of Applied Mathematics and Statistics, 2(1), 16-28.

Qasim, T.B., Ali, H., Malik, N., & Liaquat, M. (2021). Forecasting Inflation Applying ARIMA Model with GARCH Innovation: The Case of Pakistan. Journal of Accounting and Finance in Emerging Economies, 7(2), 313--324.

Rahman, M.L., & Islam, M.M. (2020). Modeling Inflation in Bangladesh. Open Journal of Satistics, 10(6), 998.

Suleman, N., & Sarpong, S. (2012). Empirical Approach to Modelling and Forecasting Inflation in Ghana. Current Research Journal of Economic Theory, 4(3), 83-87.

Wati, D.A., Eltivia, N., & Djajanto, L. (2021). Inflation Forecasting by Commodity Using the Autoreggressive Integrated Moving Average (ARIMA) Method. In Proceedings of 2nd Annual Management, Business and Economic Conference (AMBEC 2020) (pp. 226-231). Atlantis Press.

Wolters, J., & Kirchgässner, G. (2007). Introduction to modern time series analysis (2nd ed., Vol. 271). Verlag Berlin Heidelberg: Springer.




Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.