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Retardo Distribuido Transversal×ARDL de Sección Cruzada×Proyecciones Locales×
CampoEconometríaEconometríaEconometría
FamiliaRegression modelRegression modelRegression model
Año de origen200120062005
Autor originalPesaran, Shin, and SmithPesaran and colleaguesOscar Jorda
TipoDistributed lag modelDynamic panel modelMulti-horizon regression
Fuente seminalPesaran, 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. DOI ↗Pesaran, M. H., & Smith, R. (2016). Testing weak cross-sectional dependence in large panels. Econometric Reviews, 34(6-10), 1089-1117. link ↗Jorda, O. (2005). Estimation and inference of impulse responses by local projections. American Economic Review, 95(1), 161-182. DOI ↗
AliasPanel distributed lag modelPanel ARDL with cross-sectional dependenceLP-IR, Multi-horizon regression
Relacionados333
ResumenCS-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.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.Local Projections (LP) is a semi-parametric method for estimating impulse responses directly via multi-horizon regressions, bypassing VAR-model specification. Introduced by Jorda (2005), it projects outcomes h periods ahead onto current shocks and lags, producing impulse-response functions without assuming a particular lag structure or VAR order. This flexibility has made it the dominant approach in applied macroeconomics for measuring policy effects and shock transmission.
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ScholarGateComparar métodos: CS-DL · CS-ARDL · Local Projections. Recuperado el 2026-06-19 de https://scholargate.app/es/compare