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| Modèle à effets fixes× | Régression par Moindres Carrés Ordinaires (MCO)× | |
|---|---|---|
| Domaine | Économétrie | Économétrie |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 1971–1978 | 2019 |
| Auteur d'origine≠ | Mundlak (1978); Nerlove (1971); classical panel econometrics | Wooldridge (textbook treatment); classical least squares |
| Type≠ | Panel regression estimator | Linear regression |
| Source fondatrice≠ | Baltagi, B. H. (2021). Econometric Analysis of Panel Data (6th ed.). Springer. ISBN: 978-3030538002 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Alias | FE model, within estimator, least squares dummy variable, LSDV regression | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Apparentées | 5 | 5 |
| Résumé≠ | The fixed effects (FE) model is the workhorse estimator for panel data when unobserved unit-specific characteristics are suspected to correlate with the regressors. By absorbing each entity's time-invariant heterogeneity into a separate intercept, FE isolates the causal effect of within-unit variation and eliminates omitted-variable bias from time-constant confounders. | 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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