Title: Evaluating the Impact of Multicollinearity on Regression
Abstract: In empirical regression analysis, the existence of high multicollinearity suggests that predictors may provide redundant information and cause a reduction in statistics 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 mitigate the effect of multicollinearity.
Publication Year: 2017
Publication Date: 2017-01-01
Language: en
Type: article
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