Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Teste de Causalidade de Granger Não Linear× | Autoregressores Vetoriais (VAR)× | |
|---|---|---|
| Área | Econometria | Econometria |
| Família | Regression model | Regression model |
| Ano de origem≠ | 1992-2006 | 1980 |
| Autor original≠ | Baek & Brock (1992); Hiemstra & Jones (1994); Diks & Panchenko (2006) | Christopher A. Sims |
| Tipo≠ | Nonparametric causality test | Multivariate time-series model |
| Fonte seminal≠ | 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 ↗ | Sims, C. A. (1980). Macroeconomics and Reality. Econometrica, 48(1), 1–48. DOI ↗ |
| Outros nomes | nonlinear causality test, BDS-based causality, Diks-Panchenko test, nonparametric Granger causality | VAR, VAR model, vector autoregressive model, multivariate autoregression |
| Relacionados≠ | 6 | 5 |
| Resumo≠ | 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. | Vector Autoregression is a multivariate time-series model in which each variable is regressed on its own lags and the lags of all other variables in the system. Originally proposed by Sims (1980) as a data-driven alternative to large structural macroeconomic models, VAR has become the standard workhorse for dynamic analysis in empirical economics and finance. |
| ScholarGateConjunto de dados ↗ |
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