Evaluation of Ridge, Elastic Net and Lasso Regression Methods in Precedence of Multicollinearity Problem: A Simulation Study

Main Article Content

Shady Altelbany

Abstract

This study aims at performance evaluation of Ridge, Elastic Net and Lasso Regression Methods in handling different degrees of multicollinearity in a multiple regression analysis of independent variables using simulation data. The researcher simulated a collection of data with sample size n=200, 1000, 10000, 50000 and 100000, independent variables p=10. The researcher compared the performances of the three methods using Mean Square Errors (MSE). The study found that Elastic Net method outperforms Ridge and Lasso methods to estimate the regression coefficients when a degree of multicollinearity is low, moderate and high for any sample size. While, Lasso method is the most accurate regression coefficients estimator when data containing severe multicollinearity at sample size less than 10000 observations.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Article Details

How to Cite
Altelbany, S. (2021). Evaluation of Ridge, Elastic Net and Lasso Regression Methods in Precedence of Multicollinearity Problem: A Simulation Study. Journal of Applied Economics and Business Studies, 5(1), 131-142. https://doi.org/10.34260/jaebs.517
Section
Articles