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Linganisha mbinu

Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.

Njia ya Wakati wa Regression ya Kiasi×NARDL Msalaba-Sehemu×
NyanjaEkonometrikiEkonometriki
FamiliaRegression modelRegression model
Mwaka wa asili20042014
MwanzilishiRoger Koenker and colleaguesYongcheol Shin and colleagues
AinaDistribution regressionAsymmetric panel model
Chanzo asiliaKoenker, R. (2004). Quantile regression for longitudinal data. Journal of Multivariate Analysis, 91(1), 74-89. DOI ↗Shin, 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 ↗
Majina mbadalaGMM quantile regressionNARDL panel
Zinazohusiana33
MuhtasariMethod of Moments Quantile Regression combines moment-based estimation (GMM) with quantile regression to estimate distribution parameters while handling endogeneity, panel structure, and dynamic relationships. Introduced by Koenker (2004) and developed by Machado and Mata (2005), it enables distributional analysis (not just mean regression) in complex settings like dynamic panels and instrumental-variable contexts. This approach is powerful for understanding heterogeneity in treatment effects and policy impacts.CS-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.
ScholarGateSeti ya data
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  1. v1
  2. 2 Vyanzo
  3. PUBLISHED

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ScholarGateLinganisha mbinu: Method of Moments Quantile Regression · CS-NARDL. Imepatikana 2026-06-19 kutoka https://scholargate.app/sw/compare