Uporedite metode
Pregledajte izabrane metode jednu pored druge; redovi koji se razlikuju su istaknuti.
| Granger-ov test kauzaliteta× | Regresija običnih najmanjih kvadrata (OLS)× | |
|---|---|---|
| Oblast | Ekonometrija | Ekonometrija |
| Porodica | Regression model | Regression model |
| Godina nastanka≠ | 1969 | 2019 |
| Tvorac≠ | Clive W. J. Granger | Wooldridge (textbook treatment); classical least squares |
| Tip≠ | Time-series predictive causality test | Linear regression |
| Temeljni izvor≠ | 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 |
| Drugi nazivi | 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 |
| Srodne | 5 | 5 |
| Sažetak≠ | 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). |
| ScholarGateSkup podataka ↗ |
|
|