Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| OLS-regressio (Ordinary Least Squares)× | Vektorivirheenkorjausmalli (VECM)× | |
|---|---|---|
| Tieteenala | Ekonometria | Ekonometria |
| Menetelmäperhe | Regression model | Regression model |
| Syntyvuosi≠ | 2019 | 1987 |
| Kehittäjä≠ | Wooldridge (textbook treatment); classical least squares | Engle & Granger |
| Tyyppi≠ | Linear regression | Multivariate time-series model |
| Alkuperäislähde≠ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Engle, R. F. & Granger, C. W. J. (1987). Co-Integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55(2), 251-276. DOI ↗ |
| Rinnakkaisnimet | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | vector error correction model, error correction model, cointegration model, VECM (Vektör Hata Düzeltme Modeli) |
| Liittyvät≠ | 5 | 4 |
| Tiivistelmä≠ | 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). | The Vector Error Correction Model is a multivariate time-series model for cointegrated series that captures both their short-run dynamics and their long-run equilibrium relationship. It was introduced by Engle and Granger in 1987 as part of the cointegration and error-correction framework. |
| ScholarGateAineisto ↗ |
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