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| 푸리에 토다-야마모토(FTY) 인과관계 검정× | Toda-Yamamoto (TY) 인과관계 검정× | |
|---|---|---|
| 분야 | 계량경제학 | 계량경제학 |
| 계열≠ | Regression model | Hypothesis test |
| 기원 연도≠ | 2019 | 1995 |
| 창시자≠ | Yilanci, Ozgur (building on Toda and Yamamoto 1995; Becker, Enders, and Hurn 2004) | Hiro Toda & Taku Yamamoto |
| 유형≠ | Granger causality test | Modified Wald test on augmented VAR |
| 원전≠ | Yilanci, V., & Ozgur, O. (2019). Testing the Fourier Toda-Yamamoto causality test with an application to energy demand. Energy Economics, 84, 104498. 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 ↗ |
| 별칭 | FTY causality, Fourier TY causality, Toda-Yamamoto causality with Fourier approximation, FTY Granger causality | TY Causality Test, Modified Wald Granger Causality, MWALD Test, Toda-Yamamoto Nedensellik Testi |
| 관련 | 3 | 3 |
| 요약≠ | The Fourier Toda-Yamamoto (FTY) causality test extends the classical Toda-Yamamoto procedure by embedding Fourier trigonometric terms in the augmented VAR to capture smooth, gradual structural breaks in the deterministic component. It retains the key advantage of the Toda-Yamamoto approach — Granger causality can be tested without pre-testing for integration or cointegration order — while dramatically improving size and power when breaks occur. | 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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