Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| Ikke-lineær Granger-kausalitetstest× | Ikke-lineær vektor feilkorreksjonsmodell (Ikke-lineær VECM)× | |
|---|---|---|
| Fagfelt | Økonometri | Økonometri |
| Familie | Regression model | Regression model |
| Opprinnelsesår≠ | 1992-2006 | 1989–1998 |
| Opphavsperson≠ | Baek & Brock (1992); Hiemstra & Jones (1994); Diks & Panchenko (2006) | Granger & Lee (1989); Enders & Granger (1998) |
| Type≠ | Nonparametric causality test | Nonlinear time-series model |
| Opprinnelig kilde≠ | Diks, C., & Panchenko, V. (2006). A new statistic and practical guidelines for nonparametric Granger causality testing. Journal of Economic Dynamics and Control, 30(9-10), 1647-1669. DOI ↗ | Enders, W., & Granger, C. W. J. (1998). Unit-root tests and asymmetric adjustment with an example using the term structure of interest rates. Journal of Business & Economic Statistics, 16(3), 304–311. DOI ↗ |
| Alias | nonlinear causality test, BDS-based causality, Diks-Panchenko test, nonparametric Granger causality | nonlinear VECM, NVECM, threshold VECM, asymmetric VECM |
| Relaterte≠ | 6 | 2 |
| Sammendrag≠ | Nonlinear Granger causality extends the classic linear Granger causality framework to detect predictive relationships that operate through nonlinear dynamics. Using nonparametric or semi-parametric statistics based on correlation integrals or kernel density estimation, it identifies whether past values of one variable improve forecasts of another beyond what any linear model can capture. | The Nonlinear VECM extends the standard linear VECM by allowing the speed of adjustment toward long-run equilibrium to differ depending on the sign, magnitude, or regime of deviations from that equilibrium. It captures asymmetric or threshold-driven dynamics in cointegrated time-series systems that a standard VECM would miss. |
| ScholarGateDatasett ↗ |
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