Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Modelis ar nejaušiem efektiem panelī× | Regulētā lineārā regresija (Ridge Regression)× | |
|---|---|---|
| Nozare≠ | Ekonometrija | Mašīnmācīšanās |
| Saime≠ | Regression model | Machine learning |
| Izcelsmes gads≠ | 2021 | 1970 |
| Autors≠ | Baltagi (textbook treatment); classical random-effects panel estimator | Hoerl, A.E. & Kennard, R.W. |
| Tips≠ | Panel data regression | L2-regularized linear regression |
| Pirmavots≠ | Baltagi, B. H. (2021). Econometric Analysis of Panel Data (6th ed.). Springer. DOI ↗ | Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. DOI ↗ |
| Citi nosaukumi | random effects panel model, RE estimator, GLS random effects, Panel Veri — Rassal Etkiler Modeli | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| Saistītās≠ | 5 | 4 |
| Kopsavilkums≠ | The Random Effects model is a panel-data regression that treats unobserved individual heterogeneity as a random component drawn from a common distribution, rather than a separate parameter for each unit. It is a standard estimator in panel econometrics, developed in textbook treatments such as Baltagi's Econometric Analysis of Panel Data (2021). | Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated. |
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