Abstract
Utilizing Nigeria as a case study, the investigators explored the linkages between FDI inflows and some selected macroeconomic indicators (exports, gross capital formation, trade openness, inflation, and economic growth) utilizing yearly data spanning between 1981 and 2018. The study used ARDL technique to capture the linkages between FDI inflows and its determining indicators. Furthermore, the wavelet coherence techniques was used. The main novelty of wavelet coherence is that it can obtain information on dynamic correlation and/or causality between economic variables at different frequencies and different time periods. Additionally, the FMOLS and the DOLS are employed as a robustness check to the ARDL long-run estimation. The findings from the ARDL long-run estimate reveal that exports and trade openness exert positive impact on FDI inflows. The findings from the FMOLS and DOLS backed ARDL results. Furthermore, the results of the wavelet coherence-based causality and wavelet correlation techniques further provide supportive evidence to the ARDL technique. To the authors' knowledge, no previous studies have used the wavelet coherence and wavelet correlation techniques to explore these dynamics. Based on these findings, policy directions were initiated.
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Notes
Note: very strong correlation (above 0.9 or − 0.9), strong correlation (0.7 to 0.9 or − 0.7 to − 0.9), moderate correlation (0.5 to 0.7 or − 0.5 to − 0.7), low correlation (0.2 to 0.5 or − 0.2 to − 0.5), and very low correlation (0 to 0.2 or − 0 to − 0.2).
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Conceptualization: TSA and KO. Data curation: TSA. Formal analysis: TSA. Investigation: KO and GDA. Methodology: TSA. Project administration: KO. Resources: GDA. Software: TSA. Supervision: GDA and KO. Validation: TSA. Visualization: TSA and GDA. Roles/writing—original draft: KO and TSA. Writing—review and editing. KO and GDA.
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Adebayo, T.S., Onyibor, K. & Akinsola, G.D. The impact of major macroeconomic variables on foreign direct investment in Nigeria: evidence from a wavelet coherence technique. SN Bus Econ 1, 11 (2021). https://doi.org/10.1007/s43546-020-00018-5
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DOI: https://doi.org/10.1007/s43546-020-00018-5