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An Extensive Study on the Disturbances Generated by Collinearity in a Linear Regression Model with Three Explanatory Variables

Author

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  • FLORIN MARIUS PAVELESCU

    (Institute of National Economy, Romanian Academy)

Abstract

In econometric models, linear regressions with three explanatory variables are widely used. As examples can be cited: Cobb-Douglas production function with three inputs (capital, labour and disembodied technical change), Kmenta function used for approximation of CES production function parameters, error-correction models, etc. In case of multiple linear regressions, estimated parameters values and some statistical tests are influenced by collinearity between explanatory variables. In fact, collinearity acts as a noise which distorts the signal (proper parameter values). This influence is emphasized by the coefficients of alignment to collinearity hazard values. The respective coefficients have some similarities with the signal to noise ratio. Consequently, it may be used when the type of collinearity is determined. For these reasons, the main purpose of this paper is to identify all the modeling factors and quantify their impact on the above-mentioned indicator values in the context of linear regression with three explanatory variables.

Suggested Citation

  • Florin Marius Pavelescu, 2010. "An Extensive Study on the Disturbances Generated by Collinearity in a Linear Regression Model with Three Explanatory Variables," Romanian Journal of Economics, Institute of National Economy, vol. 31(2(40)), pages 65-93, December.
    • Handle: RePEc:ine:journl:v:2:y:2010:i:40:p:65-93
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    References listed on IDEAS

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    1. Pavelescu, Florin Marius, 2005. "Impact Of Collinearity On The Estimated Parameters And Classical Statistical Tests Values Of Multifactorial Linear Regressions In Conditions Of O.L.S," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 2(2), pages 50-71.
    2. Pavelescu, Florin Marius, 2009. "A Review Of Student Test Properties In Condition Of Multifactorial Linear Regression," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 6(1), pages 63-75, March.
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    Cited by:

    1. Zaman, Kazi Arif Uz, 2022. "Regional Cooperation for Improving Agriculture Production Efficiency: A Strategic Tool for Emission Reduction," ADBI Working Papers 1301, Asian Development Bank Institute.
    2. Gertrude Sebunya Muwanga, 2017. "Estimation Of Cob-Douglas And Translog Production Functions With Capital And Gender Disaggregated Labor Inputs In The Usa," Journal of Smart Economic Growth, , vol. 2(3), pages 55-105, December.
    3. Adenuga, A.H. & Muhammad-Lawal, A. & Rotimi, O.A., 2013. "Economics and Technical Efficiency of Dry Season Tomato Production in Selected Areas in Kwara State, Nigeria," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 5(1), pages 1-9, March.
    4. Florin-Marius PAVELESCU, 2014. "An Extension Of The Methodology Of Using The Student Test In Case Of A Linear Regression With Three Explanatory Variables," Romanian Journal of Economics, Institute of National Economy, vol. 38(1(47)), pages 89-106, June.
    5. Florin-Marius PAVELESCU, 2011. "Some aspects of the translog production function estimation," Romanian Journal of Economics, Institute of National Economy, vol. 32(1(41)), pages 131-150, June.

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    2. Gertrude Sebunya Muwanga, 2017. "Estimation Of Cob-Douglas And Translog Production Functions With Capital And Gender Disaggregated Labor Inputs In The Usa," Journal of Smart Economic Growth, , vol. 2(3), pages 55-105, December.
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    More about this item

    Keywords

    types of collinearity; coefficient of mediated correlation; rank of explanatory variable; order of attractor of collinearity; mediated collinearity; anticollinearity;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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