ScholarGate
المساعد

قارن الطرق

راجع الطرق التي اخترتها جنبًا إلى جنب؛ الصفوف المختلفة مميَّزة.

الانحدار البايزي للكميات على الكميات×اختبار الحدود البايزي لنموذج ARDL×
المجالالاقتصاد القياسيالاقتصاد القياسي
العائلةRegression modelRegression model
سنة النشأة2015–20192001 (ARDL); Bayesian extension 2010s
صاحب الطريقةBayesian 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
النوعNonparametric quantile regression with Bayesian estimationCointegration / bounds testing
المصدر التأسيسيSim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking and Finance, 55, 1–8. 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 ↗
الأسماء البديلةBayesian QQR, Bayesian QQ regression, Bayes quantile-on-quantile, BQQ regressionBayesian ARDL, Bayesian bounds testing approach, Bayes ARDL cointegration, Bayesian PSS bounds test
ذات صلة65
الملخصBayesian 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.
ScholarGateمجموعة البيانات
  1. v1
  2. 2 المصادر
  3. PUBLISHED
  1. v1
  2. 2 المصادر
  3. PUBLISHED

انتقل إلى البحث تنزيل الشرائح

ScholarGateقارن الطرق: Bayesian Quantile-on-Quantile Regression · Bayesian ARDL Bounds Test. استُرجع بتاريخ 2026-06-18 من https://scholargate.app/ar/compare