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Mittelineaarne autoregressiivne distributiivsete viivituste mudel (NARDL)×Granger'i põhjuslikkuse test×
ValdkondÖkonomeetriaÖkonomeetria
PerekondRegression modelRegression model
Tekkeaasta20141969
LoojaShin, Yu, and Greenwood-NimmoClive W. J. Granger
TüüpNonlinear cointegration modelTime-series predictive causality test
AlgallikasShin, 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: Econometric Methods and Applications (pp. 281-314). Springer. DOI ↗Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424-438. DOI ↗
RööpnimetusedNARDL, nonlinear ARDL, asymmetric ARDL, nonlinear bounds testGranger causality test, Granger non-causality test, predictive causality test, Granger Nedensellik Testi
Seotud45
KokkuvõteThe Nonlinear ARDL (NARDL) model extends the linear ARDL bounds-testing framework to allow asymmetric long-run and short-run relationships. By decomposing an explanatory variable into its positive and negative partial sums, it tests whether increases and decreases in a regressor have different effects on the dependent variable — a feature that linear cointegration methods cannot capture.The Granger causality test, introduced by Clive W. J. Granger in 1969, assesses whether the past values of one time series help predict another beyond what the latter's own past already explains. It defines causality in a strictly predictive sense rather than as a structural or physical cause.
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ScholarGateVõrdle meetodeid: Nonlinear NARDL · Granger Causality. Loetud 2026-06-17 aadressilt https://scholargate.app/et/compare