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Krahasoni metodat

Shqyrtoni metodat e zgjedhura krah për krah; rreshtat që ndryshojnë janë të theksuar.

Modeli Autoregresiv me Vonesë Jolineare (NARDL)×Testet kufizash ARDL (Testet kufizash të autoregresionit me vonesë)×
FushaEkonometriEkonometri
FamiljaRegression modelRegression model
Viti i origjinës20142001
KrijuesiShin, Yu, and Greenwood-NimmoPesaran, Shin & Smith
LlojiNonlinear cointegration modelCointegration test / Autoregressive distributed lag model
Burimi themeluesShin, 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 ↗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 ↗
Emërtime të tjeraNARDL, nonlinear ARDL, asymmetric ARDL, nonlinear bounds testPesaran bounds test, bounds testing approach, ARDL cointegration test, ARDL Sınır Testi (Pesaran Bounds Test)
Të lidhura44
PërmbledhjaThe 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 ARDL bounds test is an autoregressive distributed lag method that tests for a cointegrating (long-run level) relationship between time series, introduced by Pesaran, Shin and Smith in 2001. Unlike the Johansen procedure, it remains valid whether the variables are I(0), I(1) or a mix of the two, and it is more reliable than Johansen in small samples of roughly 30 to 80 observations.
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ScholarGateKrahasoni metodat: Nonlinear NARDL · ARDL Bounds Test. Marrë më 2026-06-17 nga https://scholargate.app/sq/compare