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| Hồi quy Bình phương Tối thiểu Thông thường (OLS)× | Bình phương tối thiểu tổng quát cho dữ liệu bảng (Panel GLS)× | OLS mạnh mẽ (OLS với sai số chuẩn mạnh mẽ)× | |
|---|---|---|---|
| Lĩnh vực | Kinh tế lượng | Kinh tế lượng | Kinh tế lượng |
| Họ | Regression model | Regression model | Regression model |
| Năm ra đời≠ | 2019 | 1935 / developed for panels 1980s–1990s | 1980 |
| Người khởi xướng≠ | Wooldridge (textbook treatment); classical least squares | Aitken (1935); extended to panel data by Baltagi and others | Halbert White |
| Loại≠ | Linear regression | Generalized linear regression | Linear regression with robust inference |
| Công trình gốc≠ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 978-0262232586 | White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838. DOI ↗ |
| Tên gọi khác | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | Panel GLS, Generalized Least Squares for panel data, FGLS panel, feasible GLS panel | HC robust regression, White robust OLS, sandwich estimator OLS, OLS with robust standard errors |
| Liên quan≠ | 5 | 3 | 6 |
| Tóm tắt≠ | 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). | Panel GLS is a regression method for longitudinal data that explicitly models the non-spherical error structure — heteroscedasticity across units and serial correlation within units — to recover efficient coefficient estimates. Unlike OLS, it weights observations by the inverse of the error covariance matrix, yielding the Best Linear Unbiased Estimator when the error structure is correctly specified. | 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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