Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Grindžera koincidences tests× | Paplašinātais Dikija-Fullera (ADF) vienības saknes tests× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1969 | 1979–1984 |
| Autors≠ | Clive W. J. Granger | Said & Dickey (1984); building on Dickey & Fuller (1979) |
| Tips≠ | Causality test (F-test on VAR) | Hypothesis test (unit root) |
| Pirmavots≠ | Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424–438. DOI ↗ | Said, S. E., & Dickey, D. A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika, 71(3), 599–607. DOI ↗ |
| Citi nosaukumi | Granger test, GC test, predictive causality test, Granger non-causality test | ADF test, ADF unit root test, Dickey-Fuller test (augmented), Said-Dickey test |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | The Granger causality test is a statistical hypothesis test that determines whether past values of one time series help predict future values of another, beyond what that series' own past already explains. Introduced by Clive Granger in 1969, it is the standard approach for assessing predictive causality in VAR-based time-series analysis. | The Augmented Dickey-Fuller test is the standard procedure for determining whether a univariate time series contains a unit root — that is, whether the series is non-stationary. It extends the original Dickey-Fuller test by including lagged difference terms that absorb serial correlation in the residuals, making the test valid for a wide range of time-series processes encountered in economics and finance. |
| ScholarGateDatu kopa ↗ |
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