Сравнение методов

	Кросс-секционный ARDL	Кросс-секционная распределенная задержка	Панельная модель VARX
Область	Эконометрика	Эконометрика	Эконометрика
Семейство	Regression model	Regression model	Regression model
Год появления	2006	2001	2013
Автор метода	Pesaran and colleagues	Pesaran, Shin, and Smith	Canova and Ciccarelli
Тип	Dynamic panel model	Distributed lag model	Multi-equation panel model
Основополагающий источник	Pesaran, M. H., & Smith, R. (2016). Testing weak cross-sectional dependence in large panels. Econometric Reviews, 34(6-10), 1089-1117.	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.	Canova, F., & Ciccarelli, M. (2013). Panel vector autoregressive models: A survey. Advances in Econometrics, 32, 205-246.
Другие названия	Panel ARDL with cross-sectional dependence	Panel distributed lag model	Panel VAR-X
Связанные	3	3	3
Сводка	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.	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.	Panel VARX extends vector autoregression to heterogeneous panels with exogenous variables, enabling simultaneous modeling of multiple endogenous variables alongside observed external factors across many units. Introduced by Holtz-Eakin et al. (1988) and advanced by Canova and Ciccarelli (2013), it captures dynamic relationships within units while allowing parameters to vary across units. This framework is essential for macroeconomic panels and understanding cross-unit heterogeneity in responses to common shocks.