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Toda-Yamamoto Kauzalitātes tests×Vektora autoregresija (VAR)×
NozareEkonometrijaEkonometrija
SaimeRegression modelRegression model
Izcelsmes gads19951980
AutorsToda, H. Y. and Yamamoto, T.Christopher A. Sims
TipsCausality testMultivariate time-series model
PirmavotsToda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250. DOI ↗Sims, C. A. (1980). Macroeconomics and Reality. Econometrica, 48(1), 1–48. DOI ↗
Citi nosaukumiToda-Yamamoto test, TY causality test, modified Wald test for Granger causality, TY-MWALDVAR, VAR model, vector autoregressive model, multivariate autoregression
Saistītās55
KopsavilkumsThe Toda-Yamamoto (TY) causality test is a modified Wald procedure for testing Granger causality in vector autoregressions (VARs) estimated in levels, even when variables are nonstationary or cointegrated. By intentionally over-fitting the VAR with extra lags equal to the maximum integration order, it restores the standard chi-squared asymptotic distribution of the Wald statistic without requiring prior unit-root or cointegration pretesting.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.
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ScholarGateSalīdzināt metodes: Toda-Yamamoto causality test · Vector Autoregression. Izgūts 2026-06-18 no https://scholargate.app/lv/compare