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Krahasoni metodat

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

Modeli Paneli Autoregresiv me Vonesë të Shpërndarë Jolineare (Panel NARDL)×Model meefekteve fikse të të dhënave panel×
FushaEkonometriEkonometri
FamiljaRegression modelRegression model
Viti i origjinës2014–20182014
KrijuesiShin, Yu & Greenwood-Nimmo (2014), extended to panel settings by subsequent authorsHsiao (textbook treatment); within transformation of panel data
LlojiNonlinear dynamic panel modelPanel data regression
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 (pp. 281–314). Springer. DOI ↗Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press. DOI ↗
Emërtime të tjeraPanel Nonlinear ARDL, panel asymmetric ARDL, panel NARDL bounds test, nonlinear panel cointegration modelfixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli
Të lidhura45
PërmbledhjaPanel NARDL extends the time-series NARDL framework of Shin, Yu and Greenwood-Nimmo (2014) to a panel data setting, allowing researchers to detect asymmetric long-run and short-run relationships between variables across multiple cross-sections simultaneously. By decomposing the regressor into positive and negative partial sums, the model tests whether increases and decreases in an explanatory variable have different effects on the outcome.The Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014).
ScholarGateSeti i të dhënave
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ScholarGateKrahasoni metodat: Panel NARDL · Panel Fixed Effects. Marrë më 2026-06-17 nga https://scholargate.app/sq/compare