เปรียบเทียบวิธี

	การทดสอบสหสัมพันธ์ร่วมของโยฮันเซนและแบบจำลองการปรับแก้ความคลาดเคลื่อนแบบเวกเตอร์	การทดสอบขอบเขต ARDL (การทดสอบขอบเขต Pesaran)	แบบจำลอง ARIMA (Autoregressive Integrated Moving Average)
สาขาวิชา	การเงิน	เศรษฐมิติ	เศรษฐมิติ
ตระกูล	Regression model	Regression model	Regression model
ปีกำเนิด	1991	2001	2015
ผู้ริเริ่ม	Søren Johansen	Pesaran, Shin & Smith	Box & Jenkins (Box-Jenkins methodology)
ประเภท	Multivariate cointegration / vector error correction model	Cointegration test / Autoregressive distributed lag model	Univariate time-series model
แหล่งต้นตำรับ	Johansen, S. (1991). Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models. Econometrica, 59(6), 1551-1580.	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.	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 test, VECM, vector error correction model, multivariate cointegration	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	3	4	5
สรุป	The Johansen procedure is a multivariate cointegration framework, introduced by Søren Johansen in 1991, that tests for long-run equilibrium relationships among several I(1) time series. It determines how many cointegrating vectors link the series and then builds a Vector Error Correction Model (VECM) to describe the short-run dynamics around that equilibrium.	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).