Open AccessEvaluation of Ridge, Elastic Net and Lasso Regression Methods in Precedence of Multicollinearity Problem: A Simulation Study
Author(s) -
Shady Altelbany
Publication year - 2021
Publication title -
journal of applied economics and business studies
Language(s) - English
Resource type - Journals
eISSN - 2663-693X
pISSN - 2523-2614
DOI - 10.34260/jaebs.517
Subject(s) - multicollinearity , elastic net regularization , variance inflation factor , lasso (programming language) , statistics , estimator , regression analysis , mathematics , regression , linear regression , mean squared error , sample size determination , ridge , computer science , geology , world wide web , paleontology
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 1, 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.