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Bayesian Toda-Yamamoto Causality

The Bayesian Toda-Yamamoto causality procedure combines the Toda-Yamamoto VAR augmentation strategy — which sidesteps the need for pre-testing integration and cointegration — with Bayesian prior-posterior updating. It tests Granger non-causality between time series that may be integrated or cointegrated without requiring differencing or error-correction modeling, while incorporating prior information and producing full posterior distributions over the causal parameters.

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Bayesian Toda-Yamamoto Granger Causality Test

Taksonoomiline meetodikirje · regression-model / econometrics

Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250. · DOI 10.1016/0304-4076(94)01616-8
Zellner, A. (1971). An Introduction to Bayesian Inference in Econometrics. Wiley. · ISBN 978-0471982326

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Same method familyGranger Causalitymachine-suggested · Relational suggestion, not evidence.Used in the same domainToda-Yamamoto Causalitymachine-suggested · Relational suggestion, not evidence.Taxonomic bucketVector Autoregressionmachine-suggested · Relational suggestion, not evidence.