Evaluating the Impact of Multicollinearity on Regression

Document Type

Article

Publication Date

2017

Abstract

In empirical regression analysis, the existence of high multicollinearity suggests that predictors may provide redundant information and cause a reduction in statistical power. Meanwhile, dropping correlated variables may result in mis-specified models with biased parameters. Unlike previous studies that are focused on guidelines to diagnose and manage multicollinearity, this paper proposes a practical Monte-Carlo simulation method to determine whether to keep a correlated variable for an empirical model when other factors such as sample size and over-all fitting accuracy could mitigate the effect of multicollinearity.

Publication

American Journal of Business Research

Publisher

American Institute of Higher Education

Volume

9

Issue

1

Pages

63-72

Department

College of Business and Management

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