Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Panel Toda-Yamamoto Causalityetest× | Granger Causaliteitstest× | |
|---|---|---|
| Vakgebied | Econometrie | Econometrie |
| Familie | Regression model | Regression model |
| Jaar van ontstaan≠ | 1995 (panel extension from 2006) | 1969 |
| Grondlegger≠ | Toda & Yamamoto (1995); extended to panel settings by Konya (2006) and others | Clive W. J. Granger |
| Type≠ | Causality test (non-causality hypothesis) | Causality test (F-test on VAR) |
| Oorspronkelijke bron≠ | Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250. DOI ↗ | Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424–438. DOI ↗ |
| Aliassen | Panel TY causality test, Toda-Yamamoto panel causality, panel modified Wald causality test, panel MWALD causality | Granger test, GC test, predictive causality test, Granger non-causality test |
| Verwant | 5 | 5 |
| Samenvatting≠ | The Panel Toda-Yamamoto (PTY) causality test extends the Toda-Yamamoto modified Wald approach to panel data, allowing researchers to test Granger non-causality across multiple cross-sectional units without requiring pre-testing for cointegration or imposing a common causality direction on all units. | 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. |
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