Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Modelo de Autoregresión Vectorial Robusta (VAR Robusto)× | VAR Cuantil× | |
|---|---|---|
| Campo | Econometría | Econometría |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1980s–2000s | 2006 |
| Autor original≠ | Extensions by Lutkepohl and others building on Sims (1980) VAR framework | Koenker and Xiao |
| Tipo≠ | Multivariate time-series model with robust estimation | Distribution impulse response |
| Fuente seminal≠ | Goncalves, S., & Kilian, L. (2004). Bootstrapping autoregressions with conditional heteroskedasticity of unknown form. Journal of Econometrics, 123(1), 89-120. DOI ↗ | Koenker, R., & Xiao, Z. (2006). Quantile autoregression. Journal of the American Statistical Association, 101(475), 980-990. DOI ↗ |
| Alias≠ | robust VAR, outlier-robust VAR, heavy-tailed VAR, RVAR | Quantile-based impulse response |
| Relacionados≠ | 5 | 3 |
| Resumen≠ | The Robust VAR model extends the classical Vector Autoregression framework by replacing ordinary least squares estimation with robust estimators — such as M-estimators or median-based methods — to reduce the influence of outliers, structural breaks, and heavy-tailed shocks common in financial and macroeconomic time series. | Quantile VAR estimates impulse responses of multivariate systems conditional on different quantiles of the distribution, revealing how shocks propagate heterogeneously across the conditional distribution. Introduced by Koenker and Xiao (2006) and applied to risk measurement by White et al. (2015), it reveals tail behavior and contagion effects invisible to mean-based VAR analysis. This is essential for risk management and understanding how crises propagate differently than normal times. |
| ScholarGateConjunto de datos ↗ |
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