เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| การทดสอบสหการ (Johansen / Engle-Granger)× | การทดสอบขอบเขต ARDL (การทดสอบขอบเขต Pesaran)× | แบบจำลอง ARIMA (Autoregressive Integrated Moving Average)× | |
|---|---|---|---|
| สาขาวิชา | เศรษฐมิติ | เศรษฐมิติ | เศรษฐมิติ |
| ตระกูล | Regression model | Regression model | Regression model |
| ปีกำเนิด≠ | 1988 | 2001 | 2015 |
| ผู้ริเริ่ม≠ | Engle & Granger (1987); Johansen (1988) | Pesaran, Shin & Smith | Box & Jenkins (Box-Jenkins methodology) |
| ประเภท≠ | Time-series cointegration test | Cointegration test / Autoregressive distributed lag model | Univariate time-series model |
| 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 ↗ | Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021 |
| 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) | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli |
| 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. | ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015). |
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