Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Vektorien autoregressiomalli (VAR-malli)× | OLS-regressio (Ordinary Least Squares)× | |
|---|---|---|
| Tieteenala | Ekonometria | Ekonometria |
| Menetelmäperhe | Regression model | Regression model |
| Syntyvuosi≠ | 2005 | 2019 |
| Kehittäjä≠ | Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition | Wooldridge (textbook treatment); classical least squares |
| Tyyppi≠ | Multivariate time-series model | Linear regression |
| Alkuperäislähde≠ | Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Rinnakkaisnimet | vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Liittyvät≠ | 4 | 5 |
| Tiivistelmä≠ | Vector Autoregression is a multivariate time-series model that treats several interdependent series symmetrically, letting each variable depend on its own past values and the past values of all the others. It is the standard tool for capturing mutual causality and joint dynamics, developed in the modern multiple-time-series tradition treated by Lütkepohl (2005). | 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). |
| ScholarGateAineisto ↗ |
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