เปรียบเทียบวิธี

	การถดถอยแบบควอนไทล์บนควอนไทล์แบบเบย์ (Bayesian Quantile-on-Quantile Regression)	การทดสอบ Bayesian ARDL Bounds Test
สาขาวิชา	เศรษฐมิติ	เศรษฐมิติ
ตระกูล	Regression model	Regression model
ปีกำเนิด	2015–2019	2001 (ARDL); Bayesian extension 2010s
ผู้ริเริ่ม	Bayesian QQ framework combines Sim & Zhou (2015) QQ regression with Bayesian quantile regression (Yu & Moyeed, 2001)	Pesaran, Shin & Smith (ARDL framework, 2001); Bayesian adaptation by subsequent literature
ประเภท	Nonparametric quantile regression with Bayesian estimation	Cointegration / bounds testing
แหล่งต้นตำรับ	Sim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking and Finance, 55, 1–8.	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.
ชื่อเรียกอื่น	Bayesian QQR, Bayesian QQ regression, Bayes quantile-on-quantile, BQQ regression	Bayesian ARDL, Bayesian bounds testing approach, Bayes ARDL cointegration, Bayesian PSS bounds test
ที่เกี่ยวข้อง	6	5
สรุป	Bayesian Quantile-on-Quantile (BQQ) Regression extends the Sim-Zhou quantile-on-quantile framework by replacing frequentist local linear estimation with Bayesian posterior inference. For each pair of quantiles (theta of the outcome, tau of the predictor), the method yields a full posterior distribution over the slope, enabling uncertainty quantification across the entire bivariate quantile surface — a key advantage when sample sizes are moderate and tail quantiles are sparse.	The Bayesian ARDL Bounds Test extends the classical Pesaran-Shin-Smith (2001) bounds testing approach to cointegration by embedding it within a Bayesian inferential framework. Instead of relying on frequentist F- and t-statistics with tabulated critical values, the researcher specifies prior distributions on the model parameters and derives posterior evidence of a long-run level relationship between variables that may be integrated of order zero or one.