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Author(s): Umoh Uduak David, Usoro Anthony Effiong.

Volume/Issue: Volume 8, Issue 3 (2025)

Page No: 186-203

Journal: African Journal of Mathematics and Statistics Studies (AJMSS)

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Abstract:

This study compares the performances of ordinary least squares(OLS) and some robust regression estimation methods in the presence of outliers, as they are applied to examine the impact of economic variables (FDI, Money Supply, GDP per Capita, Population Growth and Real Interest rate) on Inflation rates in Nigeria over the period, 2006 to 2023. The Robust Regression methods adopted are M-estimation, Bi-square Estimation, Iteratively Re-weighted Least Squares (IRLS), S-Estimation. Based on the analysis, it is found that M-estimation and Iteratively Re-weighted Least Squares (IRLS) give similar result as Bi-square robust estimation method as the most appropriate methods, since they give the least standard error of residuals and higher coefficient of determination (). This is followed by S- estimation, while OLS performs the worst. From the results of OLS regression it is found that Money Supply shows a positive significant impact on inflation rates, while Real Interest Rates shows a negative significant impact on inflation rates.. Robust regression estimation method using bi-square estimation(M- estimation, IRLS) methods indicates that money supply shows significantpositive impacts on inflation rates; while GDP per capita and real interest rate shows negative significant impact on inflation rates. S-robust regression analysis method showed that FDI, GDP per capita, population growth rates and real interest rates have negative and significant impacts on inflation rates.

Keywords:

OLS Regression, Robust Regression, FDI, Money Supply, GDP per Capita, Population Growth and Real Interest rate.

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