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Causalidad de Toda-Yamamoto con Parámetros Variables en el Tiempo×Modelo de Vectores Autorregresivos (VAR)×
CampoEconometríaEconometría
FamiliaRegression modelRegression model
Año de origen1995 (base); TVP variant emerged early 2000s–2010s2005
Autor originalToda & Yamamoto (1995); TVP extension by subsequent applied econometriciansLütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
TipoCausality test (time-varying)Multivariate time-series model
Fuente seminalToda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250. DOI ↗Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer. DOI ↗
AliasTVP-TY causality, time-varying Toda-Yamamoto, TVP Granger causality (Toda-Yamamoto), rolling/recursive Toda-Yamamoto causalityvector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
Relacionados34
ResumenThe TVP Toda-Yamamoto causality test combines Toda and Yamamoto's (1995) augmented VAR approach — which handles possibly integrated or cointegrated series without pre-testing for unit roots — with time-varying parameters, allowing causal relationships between variables to shift across different periods rather than remaining fixed throughout the sample.Vector Autoregression is a multivariate time-series model that treats several interdependent series symmetrically, letting each variable depend on its own past values and the past values of all the others. It is the standard tool for capturing mutual causality and joint dynamics, developed in the modern multiple-time-series tradition treated by Lütkepohl (2005).
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ScholarGateComparar métodos: Time-varying parameter Toda-Yamamoto causality · VAR Model. Recuperado el 2026-06-19 de https://scholargate.app/es/compare