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| LPM (OLS z odpornymi estymatorami odchylenia standardowego)× | Regresja metodą najmniejszych kwadratów (OLS)× | |
|---|---|---|
| Dziedzina | Ekonometria | Ekonometria |
| Rodzina | Regression model | Regression model |
| Rok powstania≠ | 1980 | 2019 |
| Twórca≠ | Halbert White | Wooldridge (textbook treatment); classical least squares |
| Typ≠ | Linear regression with robust inference | Linear regression |
| Źródło pierwotne≠ | White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Inne nazwy | HC robust regression, White robust OLS, sandwich estimator OLS, OLS with robust standard errors | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Pokrewne≠ | 6 | 5 |
| Podsumowanie≠ | 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. | 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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