Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Granger-causaliteitstest× | Gewone Kleinste Kwadraten (GKK) Regressie× | Vector Autoregressie (VAR)-model× | |
|---|---|---|---|
| Vakgebied | Econometrie | Econometrie | Econometrie |
| Familie | Regression model | Regression model | Regression model |
| Jaar van ontstaan≠ | 1969 | 2019 | 2005 |
| Grondlegger≠ | Clive W. J. Granger | Wooldridge (textbook treatment); classical least squares | Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition |
| Type≠ | Time-series predictive causality test | Linear regression | Multivariate time-series model |
| Oorspronkelijke bron≠ | Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424-438. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer. DOI ↗ |
| Aliassen | Granger causality test, Granger non-causality test, predictive causality test, Granger Nedensellik Testi | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon |
| Verwant≠ | 5 | 5 | 4 |
| Samenvatting≠ | The Granger causality test, introduced by Clive W. J. Granger in 1969, assesses whether the past values of one time series help predict another beyond what the latter's own past already explains. It defines causality in a strictly predictive sense rather than as a structural or physical cause. | 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). | 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). |
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