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

Abstract

This study addresses the crucial need for accurate forecasting of Foreign Direct 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.