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نموذج NARDL البيزي: نموذج الانحدار الذاتي الموزع غير الخطي مع تقدير بيزي×مقدّر الأرلينو-بوند العام للعزوم×
المجالالاقتصاد القياسيالاقتصاد القياسي
العائلةRegression modelRegression model
سنة النشأة2014 (NARDL); Bayesian extension c. 2015–20201991
صاحب الطريقةShin, Yu & Greenwood-Nimmo (NARDL base); Bayesian extension developed in subsequent applied literatureManuel Arellano and Stephen Bond
النوعNonlinear cointegrating model with Bayesian inferenceGMM estimator for dynamic panel data
المصدر التأسيسيShin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In W. C. Horrace & R. C. Sickles (Eds.), Festschrift in Honor of Peter Schmidt: Econometric Methods and Applications (pp. 281–314). Springer. link ↗Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58(2), 277-297. DOI ↗
الأسماء البديلةBayesian NARDL, Bayesian nonlinear ARDL, Bayesian asymmetric ARDL, B-NARDLAB-GMM, Difference GMM, first-difference GMM, Arellano-Bond estimator
ذات صلة65
الملخصBayesian NARDL combines the Nonlinear Autoregressive Distributed Lag framework of Shin, Yu, and Greenwood-Nimmo (2014) with Bayesian posterior inference. It models asymmetric long-run cointegration — allowing positive and negative shocks to a regressor to have different equilibrium effects — while incorporating prior knowledge and producing full posterior distributions over all parameters, including the asymmetry gap.The Arellano-Bond GMM estimator is the standard approach for dynamic panel data models in which the lagged dependent variable appears as a regressor. By first-differencing to remove fixed effects and using deeper lags as instruments, it yields consistent estimates even when the error is serially correlated and regressors are endogenous.
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  1. v1
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  1. v1
  2. 2 المصادر
  3. PUBLISHED

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ScholarGateقارن الطرق: Bayesian NARDL · Arellano-Bond GMM estimator. استُرجع بتاريخ 2026-06-19 من https://scholargate.app/ar/compare