ScholarGate
Asisten

Bandingkan metode

Tinjau metode pilihan Anda berdampingan; baris yang berbeda akan disorot.

NARDL Lintas Bagian×ARDL Lintas-Seksi×
BidangEkonometrikaEkonometrika
KeluargaRegression modelRegression model
Tahun asal20142006
PencetusYongcheol Shin and colleaguesPesaran and colleagues
TipeAsymmetric panel modelDynamic panel model
Sumber perintisShin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a system of nonlinear autoregressive distributed lag equations. Econometric Reviews, 33(1), 56-87. link ↗Pesaran, M. H., & Smith, R. (2016). Testing weak cross-sectional dependence in large panels. Econometric Reviews, 34(6-10), 1089-1117. link ↗
AliasNARDL panelPanel ARDL with cross-sectional dependence
Terkait33
RingkasanCS-NARDL extends the nonlinear autoregressive distributed lag (NARDL) model to panel data, capturing asymmetric long-run and short-run relationships where positive and negative changes in explanatory variables have differential effects. Introduced by Shin et al. (2014) and adapted to panels, it allows studying how cross-sectional units respond differently to positive versus negative shocks while maintaining cointegrating relationships. This approach is essential for understanding economic asymmetries in commodity markets, monetary transmission, and labor markets.CS-ARDL (Cross-Sectional ARDL) applies the ARDL framework to panel data while explicitly accounting for cross-sectional dependence—correlation of shocks and relationships across units (countries, firms, regions). Introduced by Pesaran and colleagues (2016), it extends panel ARDL methods to handle common factors or global shocks affecting all units simultaneously. This is crucial for realistic modeling of internationally integrated economies and firm networks.
ScholarGateSet data
  1. v1
  2. 2 Sumber
  3. PUBLISHED
  1. v1
  2. 2 Sumber
  3. PUBLISHED

Ke halaman pencarian Unduh salindia

ScholarGateBandingkan metode: CS-NARDL · CS-ARDL. Diakses 2026-06-17 dari https://scholargate.app/id/compare