Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Random Effects Panel Model× | Hausman-i spetsifitseerimistest (FE vs RE)× | Tavaline vähimruutude (OLS) regressioon× | |
|---|---|---|---|
| Valdkond | Ökonomeetria | Ökonomeetria | Ökonomeetria |
| Perekond | Regression model | Regression model | Regression model |
| Tekkeaasta≠ | 1978 | 1978 | 2019 |
| Looja≠ | Baltagi (textbook treatment); Hausman specification test | Jerry A. Hausman | Wooldridge (textbook treatment); classical least squares |
| Tüüp≠ | Panel data regression | Specification test for panel data models | Linear regression |
| Algallikas≠ | Hausman, J. A. (1978). Specification Tests in Econometrics. Econometrica, 46(6), 1251-1271. DOI ↗ | 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 |
| Rööpnimetused | random effects panel regression, RE estimator, GLS panel estimator, Panel Rassal Etkiler Modeli | Hausman specification test, FE vs RE test, Durbin-Wu-Hausman test, Hausman Spesifikasyon Testi (FE vs RE) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Seotud | 5 | 5 | 5 |
| Kokkuvõte≠ | The random effects model is a panel data estimator that explains an outcome using both within-unit and between-unit variation, treating the unobserved unit-specific heterogeneity as a random, normally distributed term rather than a fixed parameter. Its validity is judged with the Hausman (1978) specification test, and it is developed in standard treatments such as Baltagi's Econometric Analysis of Panel Data. | The Hausman test is a specification test, introduced by Jerry A. Hausman in 1978, that decides between the fixed-effects (FE) and random-effects (RE) estimators in panel data models. The null hypothesis is that the random-effects estimator is consistent and efficient and should be preferred; the alternative is that random effects is inconsistent and fixed effects is required because the unit-specific effects are correlated with the explanatory variables. | 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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