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非线性自回归分布式滞后(NARDL)模型×分位数回归×系统GMM(Arellano-Bover / Blundell-Bond)×
领域计量经济学计量经济学计量经济学
方法族Regression modelRegression modelRegression model
起源年份201419781998
提出者Shin, Yu & Greenwood-NimmoKoenker & BassettArellano & Bover (1995); Blundell & Bond (1998)
类型Asymmetric cointegration / error-correction modelConditional quantile regressionDynamic panel data estimator
开创性文献Shin, Y., Yu, B. & Greenwood-Nimmo, M. (2014). Modelling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework. In: Sickles, R. & Horrace, W. (Eds.), Festschrift in Honor of Peter Schmidt. Springer. DOI ↗Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗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 ↗
别名nonlinear ARDL, asymmetric ARDL, Doğrusal Olmayan ARDL (NARDL)conditional quantile regression, regression quantiles, Kantil RegresyonArellano-Bover estimator, Blundell-Bond estimator, dynamic panel GMM, Sistem GMM (Arellano-Bover / Blundell-Bond)
相关454
摘要The NARDL model, introduced by Shin, Yu and Greenwood-Nimmo in 2014, extends the ARDL framework to capture asymmetric long-run and short-run relationships, testing whether positive and negative changes in a regressor affect the dependent variable differently.Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.System GMM is a generalized method of moments estimator for dynamic panel models that contain a lagged dependent variable. Introduced by Blundell and Bond (1998), building on Arellano and Bover, it augments the differenced equation of the earlier difference GMM (Arellano-Bond) with the equation in levels to deliver consistent estimates when N is large and T is small.
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ScholarGate方法对比: NARDL Model · Quantile Regression · System GMM. 于 2026-06-18 检索自 https://scholargate.app/zh/compare