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| Panel Granger kausalitetstest× | Toda-Yamamoto Kausalitetstest× | |
|---|---|---|
| Fagområde | Økonometri | Økonometri |
| Familie | Regression model | Regression model |
| Oprindelsesår≠ | 1988–2012 | 1995 |
| Ophavsperson≠ | Holtz-Eakin, Newey & Rosen (1988); Dumitrescu & Hurlin (2012) | Toda, H. Y. and Yamamoto, T. |
| Type | Causality test | Causality test |
| Oprindelig kilde≠ | Dumitrescu, E.-I., & Hurlin, C. (2012). Testing for Granger non-causality in heterogeneous panels. Economic Modelling, 29(4), 1450–1460. 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 | panel causality test, Dumitrescu-Hurlin test, heterogeneous panel causality, panel Granger test | Toda-Yamamoto test, TY causality test, modified Wald test for Granger causality, TY-MWALD |
| Relaterede | 5 | 5 |
| Resumé≠ | The Panel Granger Causality test examines whether past values of one variable help predict another variable across multiple cross-sectional units observed over time. It extends the classical Granger causality framework to panel data, accounting for cross-sectional heterogeneity and enabling more powerful inference by pooling information across units. | 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. |
| ScholarGateDatasæt ↗ |
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