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| Hồi quy Bình phương Tối thiểu Thông thường (OLS)× | Hồi quy Lasso× | Mô hình Hiệu ứng Cố định Dữ liệu Bảng× | |
|---|---|---|---|
| Lĩnh vực≠ | Kinh tế lượng | Học máy | Kinh tế lượng |
| Họ≠ | Regression model | Machine learning | Regression model |
| Năm ra đời≠ | 2019 | 1996 | 2014 |
| Người khởi xướng≠ | Wooldridge (textbook treatment); classical least squares | Tibshirani, R. | Hsiao (textbook treatment); within transformation of panel data |
| Loại≠ | Linear regression | Regularized linear regression (L1 penalty) | Panel data regression |
| Công trình gốc≠ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ | Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press. DOI ↗ |
| Tên gọi khác | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization | fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli |
| Liên quan≠ | 5 | 4 | 5 |
| 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). | Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter. | The Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014). |
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