Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Toda-Yamamotův test kauzality× | Model ARIMA (Autoregressive Integrated Moving Average)× | |
|---|---|---|
| Obor | Ekonometrie | Ekonometrie |
| Rodina | Regression model | Regression model |
| Rok vzniku≠ | 1995 | 1970 |
| Tvůrce≠ | Toda, H. Y. and Yamamoto, T. | George Box and Gwilym Jenkins |
| Typ≠ | Causality test | Time series forecasting model |
| Původní zdroj≠ | Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250. DOI ↗ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Další názvy | Toda-Yamamoto test, TY causality test, modified Wald test for Granger causality, TY-MWALD | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) |
| Příbuzné≠ | 5 | 6 |
| Shrnutí≠ | 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. | The ARIMA(p,d,q) model is the standard workhorse for univariate time series forecasting. It combines autoregressive terms (past values), differencing to induce stationarity, and moving average terms (past shocks) into a unified linear framework. Developed by Box and Jenkins (1970), it remains one of the most widely applied models in econometrics and applied statistics. |
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