logo
Search
Time Series Modelling and Forecasting Foreign Direct Investment using Linear and Nonlinear Models: The Case of Nigeria

DOI: https://doi.org/10.33003/jobasr-2023-v1i1-19

Awariefe C.

Ogbereyivwe O.

Information

For Readers For Authors For Librarians

Announcement

A Publication of the Faculty of Physical Sciences, Federal University Dutsin-Ma, Dutsin-Ma, Katsina State, Nigeria.
Abstract
Investment (FDIT) trends in Nigeria. FDIT plays a pivotal role in the country's economic growth and development efforts, driving industrialization, infrastructure enhancement, and job creation. However, predicting FDIT accurately is essential for policymakers, investors, and researchers to formulate effective strategies and decisions. This study conducts a comparative analysis of four FDIT forecasting models: Simple Exponential Smoothing (SES), Holt Exponential Smoothing (HES), ARIMA, and NNAR in Nigeria, utilizing R version 4.3.2 and forecast and nnetar packages for implementation. The FDIT dataset is partitioned into 80:20 training-test samples, with models applied to each subset for performance evaluation. Leveraging historical FDIT data, the study evaluates the predictive performance of each model over a specified period, employing error measures such as RMSE, MAE, MASE and MAPE to assess accuracy and reliability. Additionally, residual normality tests and visual inspections validate model assumptions. Results indicate varying levels of accuracy and predictive capability among the models, with HES and NNAR models displaying superior performance compared to SES and ARIMA, evidenced by measure error rates. Residual normality tests affirm the suitability of the models for FDIT forecasting. The study contributes empirical evidence to the existing literature, providing valuable insights into forecasting techniques for FDIT in Nigeria. These insights can guide policymakers, investors, and researchers in selecting appropriate models for forecasting FDIT trends and making informed decisions. Future research avenues could explore advanced modelling techniques and additional variables to further enhance the accuracy and reliability of FDIT forecasts within the Nigerian context.
References
Abdelkader, S., & Hamza, T. (2021). Comparison of ARDL And Artificial Neural Networks Models for Foreign Direct Investment Prediction in Algeria. Journal of Finance, Investment and Sustainable Development. 60(60), 388-400. https://https://www.asjp.cerist.dz Acquah, J., Nti, A. E., Ampofi, I., & Akorli, D. (2022). Artificial Neural Network Model for Predicting Exchange Rate in Ghana: A Case of GHS/USD. American Journal of Mathematical and Computer Modelling, 7(1), 1-11. doi: 10.11648/j.ajmcm.20220701.11 Akpensuen, S. H., Edeghagba, E. E., Alhaji, A. G., & Joel, S. (2019). Time Series ARIMA Model for Predicting Nigeria Net Foreign Direct Investment (FDI). World Scientific News, 128(2), 348-362. https://bibliotekanauki.pl/articles/1059523 Amelot, L. M. M., Subadar Agathee, U., & Sunecher, Y. (2021). Time series modelling, NARX neural network and hybrid KPCA–SVR approach to forecast the foreign exchange market in Mauritius. African Journal of Economic and Management Studies, 12(1), 18-54. https://doi.org/10.1108/AJEMS-04-2019-0161 Abd El-Aal, M. F., Algarni, A., Fayomi, A., Abdul Rahman, R., & Alrashidi, K. (2021). Forecasting foreign direct investment inflow to Egypt and determinates: Using machine learning algorithms and ARIMA model. Journal of Advanced Transportation, 2021, 1-7. https://doi.org/10.1155/2021/9614101 Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons. https://elib.vku.udn.vn/bitstream/123456789/2536/1/199 4 Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford University Press. https://d1wqtxts1xzle7.cloudfront.net/55593223 . Brownlee, J. (2020). Deep Learning for Time Series Forecasting. Machine Learning Mastery. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. Ghosh, B. (2017). Quest for behavioural traces the neural way: A study on BSE 100 along with its oscillators. Indian Journal of Research in Capital Markets, 4(1), 19- 25. https://www.researchgate.net/profile/BikramadityaGhosh/publication/31618605 Hajirahimi, Z., and Khashei, M. (2016). Improving the performance of financial forecasting using different combination architectures of ARIMA and ANN models. JIEMS Journal of Industrial Engineering and Management Studies. 3(2), 17-32. www.jiems.icms.ac.ir Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts. Idowu, A. (2021). Econometric Modelling and Forecasting Foreign Direct Investment Inflows in Nigeria: ARIMA Model Approach. Available at SSRN. https://ssrn.com/abstract=3837555 Jere, S., Kasense, B. and Chilyabanyama, O. (2017). Forecasting Foreign Direct Investment to Zambia: A Time Series Analysis. Open Journal of Statistics, 7, 122- 131. https://doi.org/10.4236/ojs.2017.71010 Kamruzzaman, J., & Sarker, R. A. (2004). ANN-based forecasting of foreign currency exchange rates. Neural Information Processing-Letters and Reviews, 3(2), 49-58. Kumar, R., Kumar, P., & Kumar, Y. (2020). Time series data prediction using IoT and machine learning technique. Procedia computer science, 167, 373-381. https://doi.org/10.1016/j.procs.2020.03.240 Mucaj, R., & Sinaj, V. (2017). Exchange Rate Forecasting using ARIMA, NAR and ARIMA-ANN Hybrid Model. Exchange, 4(10), 8581-8586. www.jmest.org Mowafy, M. A. A. S., & Ela, H. H. A. A. (2020). A Statistical Model for Forecasting Foreign Direct Investment in Egypt Using the Hybrid Approach of Ann and Arima Models. Advances in Fuzzy Sets and Systems. 25(1), 1-24. http://dx.doi.org/10.17654/FS025010001 Musora, T., Chazuka, Z., & Matarise, F. (2022). Foreign Direct Investment Inflow Modelling and Forecasting; A Case Study of Zimbabwe. Thomas Musora, Zviiteyi Chazuka, Florence Matarise. Foreign Direct Investment Inflow Modelling and Forecasting. https://inria.hal.science/hal-03704355 Najamuddin, M., & Fatima, S. (2022). A Hybrid BRNNARIMA Model for financial time series forecasting. Sukkur IBA Journal of Computing and Mathematical Sciences, 6(1), 62-71. https://doi.org/10.30537/sjcms.v6i1.1027 Pradhan, R. P. (2010). Forecasting Foreign Direct Investment in the Asian Economy: An Application of Neural Network Modeling. IUP Journal of Computational Mathematics, 3(1). https://openurl.ebsco.com Roy, S. S. (2021). Prediction Of Foreign Direct Investment: An Application of Long Short-Term Memory. Psychology And Education, 58(2), 4001-4015. www.psychologyandeducation.net Widrow, B., Rumelhart, D. E., & Lehr, M. A. (1994). Neural networks: applications in industry, business and science. Communications of the ACM, 37(3), 93-106. https://link.gale.com/apps/doc/ Ye, X. (2021, December). Forecasting and Analysis of Foreign Direct Investment in Shanghai Based on Time Series Model. In 2021 2nd International Conference on Big Data Economy and Information Management (BDEIM) (pp. 164-167). IEEE. https://doi:10.1109/BDEIM55082.2021.00040 Yusuf, M. A. (2022). Forecasting Foreign Direct Investment to Sub-Saharan Africa using Arima Model: A Comparative Analysis of Machine Learning Algorithms. International Journal of Innovative Science and Research Technology. 7(12). http://orcid.org/0000-0002-5521- 2495 Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, pages: 159–175. www.elsevier.com/locate/neucom
PDF