Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Régression par Moindres Carrés Ordinaires (MCO)× | Autorégression Vectorielle sur Données de Panel (Panel VAR)× | |
|---|---|---|
| Domaine | Économétrie | Économétrie |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 2019 | 1988 |
| Auteur d'origine≠ | Wooldridge (textbook treatment); classical least squares | Holtz-Eakin, Newey & Rosen |
| Type≠ | Linear regression | Panel vector autoregression |
| Source fondatrice≠ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Holtz-Eakin, D., Newey, W. & Rosen, H. S. (1988). Estimating Vector Autoregressions with Panel Data. Econometrica, 56(6), 1371-1395. DOI ↗ |
| Alias≠ | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | PVAR, panel vector autoregression, Panel VAR (PVAR) |
| Apparentées≠ | 5 | 3 |
| Résumé≠ | 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 VAR extends the vector autoregression model to panel data, modelling the dynamic interactions among several variables while controlling for cross-unit heterogeneity through fixed effects. It was introduced by Holtz-Eakin, Newey and Rosen in 1988 and produces impulse-response functions and variance decompositions at the panel level. |
| ScholarGateJeu de données ↗ |
|
|