Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Modelul de panel cu efecte fixe× | Regresia prin metoda celor mai mici pătrate ordinare (OLS)× | |
|---|---|---|
| Domeniu | Econometrie | Econometrie |
| Familie | Regression model | Regression model |
| Anul apariției≠ | 2005 | 2019 |
| Autorul original≠ | Baltagi (textbook treatment); Hausman test for FE vs RE choice | Wooldridge (textbook treatment); classical least squares |
| Tip≠ | Panel data regression | Linear regression |
| Sursa seminală≠ | Hausman, J. A. (1978). Specification Tests in Econometrics. Econometrica, 46(6), 1251–1271. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Denumiri alternative | within estimator, panel fixed effects, entity fixed effects model, Panel Sabit Etkiler Modeli | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Înrudite | 5 | 5 |
| Rezumat≠ | The fixed effects panel model estimates relationships in panel data (many units observed over time) by exploiting only the within-unit variation, so that unobserved time-invariant heterogeneity is controlled away. It is the central within estimator developed in Baltagi's Econometric Analysis of Panel Data (2005), and the choice between it and the random effects model is settled by the Hausman (1978) test. | 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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