Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Robustit yleistetyt pienimmät neliöt (Robust GLS)× | OLS-regressio (Ordinary Least Squares)× | Robust OLS (OLS, jossa robustit keskivirheet)× | |
|---|---|---|---|
| Tieteenala | Ekonometria | Ekonometria | Ekonometria |
| Menetelmäperhe | Regression model | Regression model | Regression model |
| Syntyvuosi≠ | 1936 / 1980 | 2019 | 1980 |
| Kehittäjä≠ | Aitken (GLS theory, 1936); White (robust covariance, 1980) | Wooldridge (textbook treatment); classical least squares | Halbert White |
| Tyyppi≠ | Robust linear regression | Linear regression | Linear regression with robust inference |
| Alkuperäislähde≠ | Greene, W. H. (2012). Econometric Analysis (7th ed.). Pearson. Chapter 9: The Generalized Regression Model and Heteroscedasticity. ISBN: 978-0131395381 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838. DOI ↗ |
| Rinnakkaisnimet | robust generalized least squares, GLS with robust standard errors, heteroscedasticity-consistent GLS, HC-GLS | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | HC robust regression, White robust OLS, sandwich estimator OLS, OLS with robust standard errors |
| Liittyvät≠ | 5 | 5 | 6 |
| Tiivistelmä≠ | Robust GLS extends classical Generalized Least Squares by pairing GLS coefficient estimation with heteroscedasticity- and autocorrelation-consistent (HAC) standard errors, or by using M-estimation within the GLS framework. It corrects for non-spherical errors — heteroscedasticity, autocorrelation, or both — while also guarding inference against misspecification of the error covariance structure. | 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). | Robust OLS applies ordinary least squares to estimate coefficients and then replaces the classical standard errors with heteroscedasticity-consistent (HC) standard errors — commonly called White standard errors. This leaves the point estimates unchanged while yielding valid t-statistics and confidence intervals even when the error variance is not constant across observations. |
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