Abstract
In this study, the problem of heteroscedasticity which is referred to as unequal variability in linear regression analysis is considered. Bootstrapping Two Step Least Squares (BWLS) for controlling heteroscedasticity in linear regression analysis was proposed. This method was compared with other existing methods (Ordinary Least Squares, Weighted Least Squares, Two Step Least Squares, Bootstrap Weighted Least) based on the Root Mean Square Error (RMSE) of the regression coefficient and the Euclidean norm was used to measure the accuracy across all coefficients. Simulated data was used in the comparison. The results show that the proposed bootstrap method performed better than the other methods in performing regression analysis in the presence of heteroscedasticity across all the sample sizes and at various levels of heteroscedasticity considered from the analysis. Real life data was used to demonstrate the results, which corresponds with result obtain from the simulation studies.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: International Journal of Innovative Research and Development
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.