Abstract
In a multiple linear regression model, some regressors may be correlated. When regressors are highly correlated the problem of multicollinearity appears. Multicollinearity is one of several problems in regression analysis. The term multicollinearity was first introduced by Frisch (1934). This chapter examines the regression model when the assumption of independence among the independent variables is violated. The concept of multicollinearity and its consequences on the least squares estimators are explained. The detection of multicollinearity and alternatives for handling the problem are also discussed in this chapter.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Freund, R., and R. Littell. 2000. SAS System for Regression. London: Wiley.
Frisch, R. 1934. Statistical Confluence Analysis by Means of Complete Regression Systems. Publication 5, University Institute of Economics.
Greene, W.H. 2000. Econometric Analysis. Prentice Hall.
Klein, L.R. 1962. An Introduction to Econometrics. Prentice-Hall.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Das, P. (2019). Analysis of Collinear Data: Multicollinearity. In: Econometrics in Theory and Practice. Springer, Singapore. https://doi.org/10.1007/978-981-32-9019-8_5
Download citation
DOI: https://doi.org/10.1007/978-981-32-9019-8_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-32-9018-1
Online ISBN: 978-981-32-9019-8
eBook Packages: Economics and FinanceEconomics and Finance (R0)