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| 贝叶斯格兰杰因果关系× | Toda-Yamamoto 因果检验× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1969 (frequentist); 1984 (Bayesian treatment) | 1995 |
| 提出者≠ | Clive W. J. Granger (frequentist basis, 1969); Bayesian extension by Geweke (1984) and subsequent literature | Toda, H. Y. and Yamamoto, T. |
| 类型≠ | Bayesian causal inference test | Causality test |
| 开创性文献≠ | Geweke, J. (1984). Inference and causality in economic time series models. Handbook of Econometrics, 2, 1101-1144. Elsevier. link ↗ | Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250. DOI ↗ |
| 别名 | Bayesian Granger test, Bayesian predictive causality, BGC, Bayesian causality in mean | Toda-Yamamoto test, TY causality test, modified Wald test for Granger causality, TY-MWALD |
| 相关≠ | 6 | 5 |
| 摘要≠ | Bayesian Granger causality tests whether past values of one time series carry predictive information about another, framing the hypothesis through Bayesian inference rather than frequentist p-values. It combines a vector autoregressive (VAR) structure with prior distributions over coefficients and evaluates causal claims via posterior probabilities or Bayes factors, providing a probabilistic and nuanced alternative to the classical Granger test. | The 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. |
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