Comparar métodos

	Modelo de Vector Autorregresivo Estructural Robusto (Robust SVAR)	Modelo ARIMA Robusto
Campo	Econometría	Econometría
Familia	Regression model	Regression model
Año de origen	2000s–2010s	1986–1993
Autor original	Extension of Sims (1980) SVAR with robust inference methods	Tsay (1986); Chen & Liu (1993)
Tipo	Structural time series model	Robust time series model
Fuente seminal	Lutkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer. ISBN: 978-3540401728	Tsay, R. S. (1986). Time series model specification in the presence of outliers. Journal of the American Statistical Association, 81(393), 132–141.
Alias	robust SVAR, robust structural VAR, heteroscedasticity-robust SVAR, outlier-robust structural VAR	robust ARIMA, outlier-resistant ARIMA, robust time series estimation, ARIMA with outlier detection
Relacionados	6	4
Resumen	The Robust SVAR model extends the classical Structural VAR framework by incorporating robust estimation and inference methods that remain valid in the presence of heteroscedasticity, non-Gaussian errors, or outliers. By combining structural identification with robust statistical procedures, it produces reliable impulse responses and forecast error variance decompositions even when standard SVAR assumptions are violated in macroeconomic data.	Robust ARIMA extends the classical ARIMA framework to detect and correct the influence of outliers and structural breaks during estimation. By jointly identifying anomalous observations and re-estimating model parameters, it produces coefficient estimates and forecasts that are far less distorted by isolated shocks or data errors than standard ARIMA.