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Principal Components To Overcome Multicollinearity Problem

Author

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  • Abubakari S.Gwelo

    (Department of Mathematics and Statistics studies, Mzumbe University, Tanzania)

Abstract

The impact of ignoring collinearity among predictors is well documented in a statistical literature. An attempt has been made in this study to document application of Principal components as remedial solution to this problem. Using a sample of six hundred participants, linear regression model was fitted and collinearity between predictors was detected using Variance Inflation Factor (VIF). After confirming the existence of high relationship between independent variables, the principal components was utilized to find the possible linear combination of variables that can produce large variance without much loss of information. Thus, the set of correlated variables were reduced into new minimum number of variables which are independent on each other but contained linear combination of the related variables. In order to check the presence of relationship between predictors, dependent variables were regressed on these five principal components. The results show that VIF values for each predictor ranged from 1 to 3 which indicates that multicollinearity problem was eliminated. Finally another linear regression model was fitted using Principal components as predictors. The assessment of relationship between predictors indicated that no any symptoms of multicollinearity were observed. The study revealed that principal component analysis is one of the appropriate methods of solving the collinearity among variables. Therefore this technique produces better estimation and prediction than ordinary least squares when predictors are related. The study concludes that principal component analysis is appropriate method of solving this matter.

Suggested Citation

  • Abubakari S.Gwelo, 2019. "Principal Components To Overcome Multicollinearity Problem," Oradea Journal of Business and Economics, University of Oradea, Faculty of Economics, vol. 4(1), pages 79-91, March.
    • Handle: RePEc:ora:jrojbe:v:4:y:2019:i:1:p:79-91
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    References listed on IDEAS

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    1. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    2. Carl Mela & Praveen Kopalle, 2002. "The impact of collinearity on regression analysis: the asymmetric effect of negative and positive correlations," Applied Economics, Taylor & Francis Journals, vol. 34(6), pages 667-677.
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    Cited by:

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    2. Kumar, Bant & Sharma, Manish & Bhat, Anil & Kumar, Pawan, . "An analysis of Indian agricultural workers: a ridge regression approach," Agricultural Economics Research Review, Agricultural Economics Research Association (India), vol. 34(01).
    3. Abhishek Yadav, 2024. "A Comparative Study of Time Series, Machine Learning, and Deep Learning Models for Forecasting Global Price of Wheat," SN Operations Research Forum, Springer, vol. 5(4), pages 1-24, December.

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    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics

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