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Consideratii privind eficienta adaugării unei noi variabile explicative intr-un model de regresie liniara

Author

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  • Pavelescu, Florin Marius

    (Institutul de Economie Nationala, Academia Romana)

Abstract

În cuprinsul prezentei lucrări sunt examinate valenţele indicatorului “coeficientul de determinare standardizat”, propus de autor, pentru cuantificarea eficienţei utilizării variabilelor explicative şi a ierarhizării condiţiilor care trebuie îndeplinite în cazul unor teste statistice prin care se cuantifică de regulă eficienţa adăugării unei noi variabile explicative în cadrul unui model de regresie liniară. De asemenea, este scos în evidenţă rolul valorilor calculate ale testului Fisher ca premisă pentru ca adăugarea unei noi variabile explicative în modelul de regresie liniară să fie eficientă.

Suggested Citation

  • Pavelescu, Florin Marius, 2010. "Consideratii privind eficienta adaugării unei noi variabile explicative intr-un model de regresie liniara," Working Papers of Macroeconomic Modelling Seminar 102302, Institute for Economic Forecasting.
  • Handle: RePEc:rjr:wpmems:102302
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    Keywords

    coeficient de determinare standardizat; creştere normalizată a coeficientului de determinare; testul Fisher; coeficient de determinare ajustat; criteriul informaţional Akaike; criteriul informaţional Schwarz; ierarhizarea criteriilor de determinare a adăugării unei noui variabile explicative în ecuaţia de regresie liniară.;

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: 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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