Porównaj metody
Przeglądaj wybrane metody obok siebie; wiersze, które się różnią, są wyróżnione.
| Nieliniowy test przyczynowości Grangera× | Nieliniowy model VAR× | |
|---|---|---|
| Dziedzina | Ekonometria | Ekonometria |
| Rodzina | Regression model | Regression model |
| Rok powstania≠ | 1992-2006 | 1990s–2000s |
| Twórca≠ | Baek & Brock (1992); Hiemstra & Jones (1994); Diks & Panchenko (2006) | Tsay (1998); Krolzig (1997); Tong (1990) for threshold framework |
| Typ≠ | Nonparametric causality test | Multivariate nonlinear time series model |
| Źródło pierwotne≠ | 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 ↗ | Tsay, R. S. (1998). Testing and modeling multivariate threshold models. Journal of the American Statistical Association, 93(443), 1188–1202. DOI ↗ |
| Inne nazwy | nonlinear causality test, BDS-based causality, Diks-Panchenko test, nonparametric Granger causality | NLVAR, nonlinear vector autoregression, threshold VAR, TVAR |
| Pokrewne≠ | 6 | 4 |
| Podsumowanie≠ | 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 VAR (NLVAR) model extends the standard vector autoregression by allowing the dynamic relationships among multiple time series to switch or change smoothly depending on an observed threshold variable, a latent regime state, or a smooth transition function. It is used when economic systems exhibit asymmetric responses, regime shifts, or state-dependent dynamics that a linear VAR cannot capture. |
| ScholarGateZbiór danych ↗ |
|
|