Time Series Modelling and Forecasting Foreign Direct Investment using Linear and Nonlinear Models: The Case of Nigeria
Awariefe C.
Ogbereyivwe O.
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