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| Robust Granger kausalitetstest× | Toda-Yamamoto Granger-kausalitetstest× | |
|---|---|---|
| Fagområde | Økonometri | Økonometri |
| Familie≠ | Regression model | Hypothesis test |
| Oprindelsesår≠ | 2006 (robust variant); 1969 (original Granger) | 1995 |
| Ophavsperson≠ | Hacker & Hatemi-J (robust bootstrap variant); Granger (original causality concept) | Hiro Toda & Taku Yamamoto |
| Type≠ | Hypothesis test | Modified Wald test on augmented VAR |
| Oprindelig kilde≠ | Hacker, R. S., & Hatemi-J, A. (2006). Tests for causality between integrated variables using asymptotic and bootstrap distributions: Theory and application. Applied Economics, 38(13), 1489–1500. DOI ↗ | Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1–2), 225–250. DOI ↗ |
| Aliasser | bootstrap Granger causality, heteroscedasticity-robust Granger causality, non-asymptotic Granger causality test, RGC | TY Causality Test, Modified Wald Granger Causality, MWALD Test, Toda-Yamamoto Nedensellik Testi |
| Relaterede≠ | 4 | 3 |
| Resumé≠ | Robust Granger causality extends the classic Granger causality framework by using bootstrap-based or heteroscedasticity-robust critical values rather than asymptotic chi-squared tables. This makes the test reliable in finite samples and when the data exhibit non-normality, heteroscedasticity, or near-integration, settings where the standard F- or Wald-based test is known to over-reject. | The Toda-Yamamoto (TY) causality test, introduced by Toda and Yamamoto (1995), provides a robust procedure for testing Granger non-causality in vector autoregressive (VAR) models when the variables may be integrated or cointegrated of arbitrary order. By intentionally over-fitting the VAR with extra lags equal to the maximum integration order, the method bypasses the need for pre-testing cointegration and preserves the standard asymptotic chi-squared distribution of the Wald statistic. |
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