เปรียบเทียบวิธี

	การทดสอบขอบเขต ARDL (การทดสอบขอบเขต Pesaran)	การทดสอบความเป็นเหตุเป็นผลแบบแกรนเจอร์ (Granger Causality Test)	การถดถอยกำลังสองน้อยที่สุดสามัญ (OLS)
สาขาวิชา	เศรษฐมิติ	เศรษฐมิติ	เศรษฐมิติ
ตระกูล	Regression model	Regression model	Regression model
ปีกำเนิด	2001	1969	2019
ผู้ริเริ่ม	Pesaran, Shin & Smith	Clive W. J. Granger	Wooldridge (textbook treatment); classical least squares
ประเภท	Cointegration test / Autoregressive distributed lag model	Time-series predictive causality test	Linear regression
แหล่งต้นตำรับ	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.	Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424-438.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
ชื่อเรียกอื่น	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	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
ที่เกี่ยวข้อง	4	5	5
สรุป	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).