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Forecasting Foreign Direct Investment Inflow to Bangladesh: Using an Autoregressive Integrated Moving Average and a Machine Learning-Based Random Forest Approach

Author

Listed:
  • Md. Monirul Islam

    (Department of Agricultural Economics, Bangladesh Agricultural University, Mymensingh 2202, Bangladesh
    Commonwealth Scientific and Industrial Research Organisation—CSIRO, Waite Campus, Adelaide, SA 5064, Australia)

  • Arifa Jannat

    (Institute of Agribusiness and Development Studies (IADS), Bangladesh Agricultural University, Mymensingh 2202, Bangladesh
    School of Agriculture, Food and Wine, University of Adelaide, Urrbrae, SA 5064, Australia)

  • Kentaka Aruga

    (Graduate School of Humanities and Social Sciences, Saitama University, 255 Shimo-Okubo, Sakura-ku, Saitama 338-8570, Japan)

  • Md Mamunur Rashid

    (Senior Lecturer—ICT, School of Engineering & Technology, CQUniversity Australia, 120 Spencer Street, Melbourne, VIC 3000, Australia)

Abstract

This study focuses on the challenge of accurately forecasting foreign direct investment (FDI) inflows to Bangladesh, which are crucial for the country’s sustainable economic growth. Although Bangladesh has strong potential as an investment destination, recent FDI inflows have sharply declined due to global economic uncertainties and the impact of the COVID-19 pandemic. There is a clear gap in applying advanced forecasting models, particularly the autoregressive integrated moving average (ARIMA) model and machine learning techniques like random forest (RF), to predict FDI inflows in Bangladesh. This study aims to analyze and forecast FDI inflows in Bangladesh by employing a hybrid approach that integrates the ARIMA model and the RF algorithm. This study covers the period from 1986 to 2022. The analysis reveals that net FDI inflow in Bangladesh is integrated into the first order, and the ARIMA (3,1,2) model is identified as the most suitable based on the Akaike Information Criterion (AIC). Diagnostic tests confirm its consistency and appropriateness for forecasting net FDI inflows in the country. This study’s findings indicate a decreasing trend in net FDI inflows over the forecasted period, with an average of USD 1664 million, similar to recent values. The results from the RF model also support these findings, projecting average net FDI values of USD 1588.99 million. To achieve the aims of Vision 2041, which include eradicating extreme poverty and becoming a high-economic nation, an increasing trend of FDI inflow is crucial. The current forecasting trends provide insights into the potential trajectory of FDI inflows in Bangladesh, highlighting the importance of attracting higher FDI to accomplish their economic goals. Additionally, strengthening bilateral investment agreements and leveraging technology transfer through FDI will also be essential for fostering sustainable economic growth.

Suggested Citation

  • Md. Monirul Islam & Arifa Jannat & Kentaka Aruga & Md Mamunur Rashid, 2024. "Forecasting Foreign Direct Investment Inflow to Bangladesh: Using an Autoregressive Integrated Moving Average and a Machine Learning-Based Random Forest Approach," JRFM, MDPI, vol. 17(10), pages 1-20, October.
    • Handle: RePEc:gam:jjrfmx:v:17:y:2024:i:10:p:451-:d:1492844
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    References listed on IDEAS

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