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What is Multicollinearity

Encyclopedia of Information Science and Technology, Fourth Edition
This concept explains the situation in regression analysis when two or more of the independent variables are moderately to highly correlated. Consequently, the regression results are biased.
Published in Chapter:
Applications of Artificial Neural Networks in Economics and Finance
Iva Mihaylova (University of St. Gallen, Switzerland)
Copyright: © 2018 |Pages: 11
DOI: 10.4018/978-1-5225-2255-3.ch575
Artificial neural Networks (ANNs) are a powerful technique for multivariate dependence analysis. Originally inspired by neuroscience, ANNs are becoming an increasingly attractive analytic tool for applications in the area of economics and finance due to the flexible solutions they offer. The purpose of this article is to present such important applications with an emphasis on recent research trends. The contributions are grouped as follows: ANNs (1) for prediction, (2) for classification and (3) for modelling. The chapter concludes with the future trends in the ANNs research in economics and finance.
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Cross-Sectional Regression
When an independent variable is correlated with a range of other independent variables. This causes problems when, in an extreme case, it causes singularity, where two independent variables are perfectly correlated.
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Sustainable Approach towards Thai SMEs: The Effects of Country Philosophy of Sufficiency Economy and Government Support
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Resolving Issues and Troubleshooting Problems
A case where several of the variables in the model are highly correlated. This may include both the control variables as well as the variables of interest and used to test the hypotheses.
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An Analysis of Gender Inequality in Professional Tennis: A Study of the Cozening Sport
A state of very high intercorrelations or inter-associations among the independent variables.
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Factors Affecting Digital Healthcare Innovation in India: A Case Study of Delhi NCR
It is a situation when independent variables which are used to estimate the dependent variables in a regression model are themselves related to each other. In such a case the analysis or the estimates of the dependent variable are wrong or contains error.
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An Exploratory Factor Analysis of an Open-Access Virtual “Privilege Walk” Instrument
A situation in which a predictor variable in a multiple regression model can be predicted from other variables, which leads to difficulty in observing the effects of independent variables (IVs) on dependent variables (DVs).
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The Relationship Between CSR, Corporate Governance, and Firm Performance: Evidence From Moroccan Listed Firms
A state of very high inter-correlations or inter-associations among the independent variables. It is therefore a type of disturbance in the data, and if present in the data the statistical inferences made about the data may not be reliable.
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Age in the Consumer Behavior Change: Evidence From Awareness, Perceived Value, and Use of Mobile Banking
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