เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| การทดสอบสหการ (Johansen / Engle-Granger)× | การทดสอบขอบเขต ARDL (การทดสอบขอบเขต Pesaran)× | การทดสอบความเป็นเหตุเป็นผลแบบแกรนเจอร์ (Granger Causality Test)× | |
|---|---|---|---|
| สาขาวิชา | เศรษฐมิติ | เศรษฐมิติ | เศรษฐมิติ |
| ตระกูล | Regression model | Regression model | Regression model |
| ปีกำเนิด≠ | 1988 | 2001 | 1969 |
| ผู้ริเริ่ม≠ | Engle & Granger (1987); Johansen (1988) | Pesaran, Shin & Smith | Clive W. J. Granger |
| ประเภท≠ | Time-series cointegration test | Cointegration test / Autoregressive distributed lag model | Time-series predictive causality test |
| แหล่งต้นตำรับ≠ | Johansen, S. (1988). Statistical Analysis of Cointegration Vectors. Journal of Economic Dynamics and Control, 12(2-3), 231-254. DOI ↗ | Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds Testing Approaches to the Analysis of Level Relationships. Journal of Applied Econometrics, 16(3), 289–326. DOI ↗ | Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424-438. DOI ↗ |
| ชื่อเรียกอื่น | Johansen cointegration test, Engle-Granger cointegration test, long-run equilibrium test, Eşbütünleşme Testi (Johansen/Engle-Granger) | Pesaran bounds test, bounds testing approach, ARDL cointegration test, ARDL Sınır Testi (Pesaran Bounds Test) | Granger causality test, Granger non-causality test, predictive causality test, Granger Nedensellik Testi |
| ที่เกี่ยวข้อง≠ | 5 | 4 | 5 |
| สรุป≠ | The cointegration test examines whether non-stationary time series that each contain a unit root share a stable long-run equilibrium relationship. The single-equation residual approach was introduced by Engle and Granger (1987) and the system-based rank approach by Johansen (1988). | The ARDL bounds test is an autoregressive distributed lag method that tests for a cointegrating (long-run level) relationship between time series, introduced by Pesaran, Shin and Smith in 2001. Unlike the Johansen procedure, it remains valid whether the variables are I(0), I(1) or a mix of the two, and it is more reliable than Johansen in small samples of roughly 30 to 80 observations. | The Granger causality test, introduced by Clive W. J. Granger in 1969, assesses whether the past values of one time series help predict another beyond what the latter's own past already explains. It defines causality in a strictly predictive sense rather than as a structural or physical cause. |
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