Vertaile menetelmiä
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| Varianssin inflaatiokerroin (VIF)× | OLS-regressio (Ordinary Least Squares)× | |
|---|---|---|
| Tieteenala | Ekonometria | Ekonometria |
| Menetelmäperhe | Regression model | Regression model |
| Syntyvuosi≠ | 1970 | 2019 |
| Kehittäjä≠ | Donald Marquardt | Wooldridge (textbook treatment); classical least squares |
| Tyyppi≠ | Diagnostic statistic | Linear regression |
| Alkuperäislähde≠ | Marquardt, D. W. (1970). Generalized inverses, ridge regression, biased linear estimation, and nonlinear estimation. Technometrics, 12(3), 591–612. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Rinnakkaisnimet | VIF, Variance Inflation Index, Multicollinearity Inflation Factor, Varyans Enflasyon Faktörü | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Liittyvät≠ | 3 | 5 |
| Tiivistelmä≠ | The Variance Inflation Factor (VIF) is a scalar diagnostic statistic proposed by Donald Marquardt (1970) that quantifies how much the variance of an estimated regression coefficient increases due to linear dependence—multicollinearity—among the predictors in an ordinary least squares model. It is routinely applied in econometrics, social science, and biomedical research whenever analysts suspect that two or more independent variables move together closely enough to destabilize coefficient estimates. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
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