Number of predictors and multicollinearity: What are their effects on error and bias in regression?

The present Monte Carlo simulation study adds to the literature by analyzing parameter bias, rates of Type I and Type II error, and variance inflation factor (VIF) values produced under various multicollinearity conditions by multiple regressions with two, four, and six predictors. Findings indicate...

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Bibliographic Details
Published inCommunications in statistics. Simulation and computation Vol. 48; no. 1; pp. 27 - 38
Main Authors Lavery, Matthew Ryan, Acharya, Parul, Sivo, Stephen A., Xu, Lihua
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 02.01.2019
Taylor & Francis Ltd
Subjects
Bias
Collinearity
Computer simulation
Error analysis
Monte Carlo simulation
Monte Carlo simulation study
Multicollinearity
Multiple regression
Parameters
Regression analysis
Statistical methods
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Summary:The present Monte Carlo simulation study adds to the literature by analyzing parameter bias, rates of Type I and Type II error, and variance inflation factor (VIF) values produced under various multicollinearity conditions by multiple regressions with two, four, and six predictors. Findings indicate multicollinearity is unrelated to Type I error, but increases Type II error. Investigation of bias suggests that multicollinearity increases the variability in parameter bias, while leading to overall underestimation of parameters. Collinearity also increases VIF. In the case of all diagnostics however, increasing the number of predictors interacts with multicollinearity to compound observed problems.
Bibliography:ObjectType-Article-1
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ISSN:0361-0918
1532-4141
DOI:10.1080/03610918.2017.1371750