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Testul de Causalitate Nonlinear Toda-Yamamoto×Testul de Cointegrare (Johansen / Engle-Granger)×
DomeniuEconometrieEconometrie
FamilieRegression modelRegression model
Anul apariției1995 (base); nonlinear extensions 2000s–2010s1988
Autorul originalToda & Yamamoto (1995) for the linear base; nonlinear extension developed by subsequent researchers applying rank transformations or neural-network-augmented VAREngle & Granger (1987); Johansen (1988)
TipCausality testTime-series cointegration test
Sursa seminalăToda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250. DOI ↗Johansen, S. (1988). Statistical Analysis of Cointegration Vectors. Journal of Economic Dynamics and Control, 12(2-3), 231-254. DOI ↗
Denumiri alternativenonlinear TY causality, rank-based Toda-Yamamoto test, modified Wald nonlinear causality, NTY causality testJohansen cointegration test, Engle-Granger cointegration test, long-run equilibrium test, Eşbütünleşme Testi (Johansen/Engle-Granger)
Înrudite55
RezumatThe Nonlinear Toda-Yamamoto causality test extends the classic Toda-Yamamoto (1995) modified Wald procedure to detect causal linkages that are hidden in the means of series but manifest through nonlinear dynamics such as asymmetries, threshold effects, or volatility transmission. It fits an augmented VAR on rank-transformed or otherwise nonlinearly mapped series and applies a chi-squared Wald test on the extra-lag coefficients.The cointegration test examines whether non-stationary time series that each contain a unit root share a stable long-run equilibrium relationship. The single-equation residual approach was introduced by Engle and Granger (1987) and the system-based rank approach by Johansen (1988).
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  3. PUBLISHED

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ScholarGateCompară metode: Nonlinear Toda-Yamamoto Causality · Cointegration Test. Preluat la 2026-06-18 de pe https://scholargate.app/ro/compare