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Furjē nelineārā ARDL (Fourier NARDL)×Arellano-Bond GMM novērtētājs×
NozareEkonometrijaEkonometrija
SaimeRegression modelRegression model
Izcelsmes gads2014–2020s1991
AutorsExtension of Shin, Yu & Greenwood-Nimmo (2014) NARDL, incorporating Fourier terms from Becker, Enders & Lee (2006)Manuel Arellano and Stephen Bond
TipsNonlinear cointegrating model with smooth break approximationGMM estimator for dynamic panel data
PirmavotsShin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In R. C. Sickles & W. C. Horrace (Eds.), Festschrift in Honor of Peter Schmidt (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 ↗
Citi nosaukumiFourier NARDL, Fourier nonlinear ARDL, F-NARDL, Fourier asymmetric ARDLAB-GMM, Difference GMM, first-difference GMM, Arellano-Bond estimator
Saistītās65
KopsavilkumsFourier NARDL extends the Nonlinear ARDL (NARDL) bounds-testing framework by adding Fourier trigonometric terms to the error-correction equation, allowing the model to capture smooth, gradual structural breaks in the long-run relationship without requiring the researcher to know or specify the break date in advance.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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  3. PUBLISHED

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ScholarGateSalīdzināt metodes: Fourier NARDL · Arellano-Bond GMM estimator. Izgūts 2026-06-19 no https://scholargate.app/lv/compare