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

Author

Listed:
  • Abir HASSAN
  • Mahbubul Md. ALAM
  • Azmaine FAEIQUE

    (Bangladesh University of Professionals
    Bangladesh University of Professionals
    Bangladesh University of Professionals)

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.

Suggested Citation

  • 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.
    • Handle: RePEc:trp:01jefa:jefa0066
    DOI: 10.1991/jefa.v7i2.a61
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    References listed on IDEAS

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    1. 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.
    2. Vesna Karadzic & Bojan Pejovic, 2021. "Inflation Forecasting in the Western Balkans and EU: A Comparison of Holt-Winters, ARIMA and NNAR Models," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 23(57), pages 517-517.
    3. Qasim, Tahira Bano & Ali, Hina & Malik, Natasha & Liaquat, Malka, 2021. "Forecasting Inflation Applying ARIMA Model with GARCH Innovation: The Case of Pakistan," Journal of Accounting and Finance in Emerging Economies, CSRC Publishing, Center for Sustainability Research and Consultancy Pakistan, vol. 7(2), pages 313-324, June.
    4. Gebhard Kirchgässner & Jürgen Wolters & Uwe Hassler, 2013. "Introduction to Modern Time Series Analysis," Springer Texts in Business and Economics, Springer, edition 2, number 978-3-642-33436-8, August.
    5. Abdullah Ghazo, 2021. "Applying the ARIMA Model to the Process of Forecasting GDP and CPI in the Jordanian Economy," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 12(3), pages 70-77, May.
    6. McAdam, Peter & McNelis, Paul, 2005. "Forecasting inflation with thick models and neural networks," Economic Modelling, Elsevier, vol. 22(5), pages 848-867, September.
    7. Erkan Işığıçok & Ramazan Öz & Savaş Tarkun, 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 (IJEOE), IGI Global, vol. 9(4), pages 84-103, October.
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    More about this item

    Keywords

    C51; C53; E31; E37.;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • 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

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