Vertaile menetelmiä
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| Näennäisesti riippumattomat regressiot (SUR)× | OLS-regressio (Ordinary Least Squares)× | |
|---|---|---|
| Tieteenala | Ekonometria | Ekonometria |
| Menetelmäperhe | Regression model | Regression model |
| Syntyvuosi≠ | 1962 | 2019 |
| Kehittäjä≠ | Arnold Zellner | Wooldridge (textbook treatment); classical least squares |
| Tyyppi≠ | System regression (multi-equation) | Linear regression |
| Alkuperäislähde≠ | Zellner, A. (1962). An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias. Journal of the American Statistical Association, 57(298), 348-368. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Rinnakkaisnimet | SUR, Zellner's SUR, seemingly unrelated regression equations, Görünürde İlişkisiz Regresyon (SUR) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Liittyvät | 5 | 5 |
| Tiivistelmä≠ | Seemingly Unrelated Regressions, introduced by Arnold Zellner in 1962, is a system regression method that estimates several linear equations jointly when their error terms are correlated across equations. By exploiting that cross-equation correlation through generalized least squares, it is more efficient than estimating each equation separately by OLS. | 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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