International Journal of Mathematics Trends and Technology
Volume 69 | Issue 2 | Year 2023 | Article Id. IJMTT-V69I2P508 | DOI : https://doi.org/10.14445/22315373/IJMTT-V69I2P508
| Received | Revised | Accepted | Published |
|---|---|---|---|
| 28 Dec 2022 | 01 Feb 2023 | 12 Feb 2023 | 20 Feb 2023 |
This study finds multicollinearity between various economic variables and the gross domestic product (GDP) (Exchange Rate, Labour Force, Market Capitalization and All Shared Index). For the actual and logarithm transformation data sets, simple and multiple linear regression models are fitted between dependent and independent variables. Then, using the correlation matrix, variance inflation factor (VIF), and Eigenvalues, the presence of multicollinearity is evaluated. The outcome demonstrates that multicollinearity exists in some of the regression coefficients of the models, making the predicted coefficients on GDP irrelevant. A significant multicollinearity is indicated by VIF values greater than 5. Three independent variables (Exchange Rate, Market Capitalization, and All Shared Index) are significantly correlated with GDP, but only Labour Force is not, according to a comparison of the actual and logarithm models. However, when it came to R-square, VIF, and Akaike Information Criteria, the logarithm model outperforms the actual model (AIC). In light of this, this study suggests using the logarithm model to estimate GDP using various economic independent variables (Exchange Rate, Market Capitalization and All Shared Index).
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Naluba, Richard Zornata, Orumie Ukamaka Cynthia, Biu, Emmanuel Oyinebifun, "Modelling and Detecting Multicollinearity of Some Economic Variables on Gross Domestic Product using Variance Inflation Factor," International Journal of Mathematics Trends and Technology (IJMTT), vol. 69, no. 2, pp. 61-69, 2023. Crossref, https://doi.org/10.14445/22315373/IJMTT-V69I2P508
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