An Efficient Estimation Technique for investigating Economic Growth and its Determinants for Nigeria in the Presence of Multicollinearity

Ayooluwade Ebiwonjumi, Retius Chifurira and Knowledge Chinhamu
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Ayooluwade Ebiwonjumi: School of Mathematics, Statistics and Computer Science, University of kwaZulu-Natal, Durban, South Africa
Retius Chifurira: School of Mathematics, Statistics and Computer Science, University of kwaZulu-Natal, Durban, South Africa
Knowledge Chinhamu: School of Mathematics, Statistics and Computer Science, University of kwaZulu-Natal, Durban, South Africa.

International Journal of Finance & Banking Studies, 2022, vol. 11, issue 1, 107-119

Abstract: This study aimed to investigate economic growth (RGDP) and its determinants such as internal debt (INDT), external debt (EXDT), interest rate (RINR), exchange rate (REXR) and trade openness (OPEN). Quarterly data from 1986-2021 used for this investigation were extracted from Central Bank of Nigeria. Exploratory data analysis (EDA) revealed linear dependence of RGDP and the aforementioned determinants. The EDA and the variance inflation factor revealed the presence of multicollinearity caused by the INDT. As a result of this, the ridge regression method was used and it was found that the multicollinearity problem was addressed with appropriate ridge constant K= 0.29. Hence, ridge regression technique with constant K= 0.29 was a robust method to predict economic growth in Nigeria using the explanatory variables under consideration. Thus, served as a great benefit to the policy makers as the study provided a better understanding of the economic growth and the aforementioned determinants in terms of relationship and in particular the set back caused by EXDT on the economy of Nigeria. Also, the researchers benefited from this study as it engendered the understanding of the appropriate estimation technique to be used when the presence of multicollinearity was established in fitting a linear model.

Keywords: Multicollinearity; Least Square Regression; Variance Inflation Factor; Ridge Regression; Ridge Trace (search for similar items in EconPapers)
Date: 2022
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