手法を比較

	クロスセクショナルNARDL	クロスセクショナル分布ラグ	Quantile ARDL
分野	計量経済学	計量経済学	計量経済学
系統	Regression model	Regression model	Regression model
提唱年	2014	2001	2006
提唱者	Yongcheol Shin and colleagues	Pesaran, Shin, and Smith	Roger Koenker and Zhijie Xiao
種類	Asymmetric panel model	Distributed lag model	Conditional distribution model
原典	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.	Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships and dynamics. Journal of Applied Econometrics, 16(3), 289-326.	Koenker, R., & Xiao, Z. (2006). Quantile autoregression. Journal of the American Statistical Association, 101(475), 980-990.
別名	NARDL panel	Panel distributed lag model	Quantile ARDL
関連	3	3	3
概要	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.	CS-DL (Cross-Sectional Distributed Lag) is a simplified dynamic panel model regressing outcomes on current and lagged explanatory variables without explicit autoregressive terms, while accounting for cross-sectional dependence. Built on Pesaran et al. (2001) and extended by Chudik et al. (2014), it estimates dynamic effects more parsimoniously than ARDL when autocorrelated lags are less critical. This approach is valuable for short-horizon effects and policy impact analysis.	QARDL (Quantile Autoregressive Distributed Lag) combines quantile regression with ARDL modeling to estimate conditional relationships at different points of the distribution, revealing heterogeneous short-run and long-run effects. Introduced by Koenker and Xiao (2006) and refined by Cho et al. (2015), it captures how the effect of explanatory variables on outcomes varies across quantiles, essential for understanding tail behavior and distributional impacts rather than just mean effects.