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Features Of The Ordinary Least Square (Ols) Method. Implications For The Estimation Methodology

Author

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  • Pavelescu, Florin Marius

    (Institute of National Economy)

Abstract

The ordinary least square method (OLS) is frequently used for the parameters estimation of different functional relationships. Having in view a series of properties revealed by the author, this paper presents the factors that determine the size of the estimated parameters and coefficients of determination, and also the calculated values of the Fisher test, used for validation and of the Student test, used for assessing the relevance of each estimated parameter. The cases of unifactorial linear regression and that of multifactorial linear regression are presented. A special attention is paid to bifactorial linear regression, because in this way one may emphasize, with a small volume of computations, the correlations between the parameters’ estimated values and some indicators that test the relevance of the obtained results can be emphasized. Also, the impact of multicolinearity on values of estimated parameters is revealed with the help of alignment coefficient. In the end, a methodology for the acceptation of a new explanatory variable in a linear regression model is proposed.

Suggested Citation

  • Pavelescu, Florin Marius, 2004. "Features Of The Ordinary Least Square (Ols) Method. Implications For The Estimation Methodology," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 1(2), pages 85-101, May.
    • Handle: RePEc:rjr:romjef:v:1:y:2004:i:2:p:85-101
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    Citations

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    Cited by:

    1. 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.
    2. Pavelescu, Florin Marius, 2010. "An Analysis Model for the Disturbances Generated by Collinearity in the Context of the OLS Method," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 245-264, July.
    3. Florin Marius PAVELESCU, 2016. "Economic Cycles and Disembodied Technical Change of Romania’s Economy During the Period 1863-1913," Romanian Journal of Economics, Institute of National Economy, vol. 43(2(52)), pages 133-155, december.
    4. Md Al Masum Bhuiyan & Ramanjit K. Sahi & Md Romyull Islam & Suhail Mahmud, 2021. "Machine Learning Techniques Applied to Predict Tropospheric Ozone in a Semi-Arid Climate Region," Mathematics, MDPI, vol. 9(22), pages 1-13, November.

    More about this item

    Keywords

    intercept of a linear regression; coefficient of determination; proper e and derived estimated value of a multifactorial linear regression parameters; alignment coefficients; multicolinearity; Fisher test; Student test;
    All these keywords.

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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