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Модель робастной векторной авторегрессии (Robust VAR)×Модель коррекции ошибок вектора (VECM)×
ОбластьЭконометрикаЭконометрика
СемействоRegression modelRegression model
Год появления1980s–2000s1987
Автор методаExtensions by Lutkepohl and others building on Sims (1980) VAR frameworkEngle & Granger
ТипMultivariate time-series model with robust estimationMultivariate time-series model
Основополагающий источникGoncalves, S., & Kilian, L. (2004). Bootstrapping autoregressions with conditional heteroskedasticity of unknown form. Journal of Econometrics, 123(1), 89-120. DOI ↗Engle, R. F. & Granger, C. W. J. (1987). Co-Integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55(2), 251-276. DOI ↗
Другие названияrobust VAR, outlier-robust VAR, heavy-tailed VAR, RVARvector error correction model, error correction model, cointegration model, VECM (Vektör Hata Düzeltme Modeli)
Связанные54
СводкаThe 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.The Vector Error Correction Model is a multivariate time-series model for cointegrated series that captures both their short-run dynamics and their long-run equilibrium relationship. It was introduced by Engle and Granger in 1987 as part of the cointegration and error-correction framework.
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ScholarGateСравнение методов: Robust VAR model · VECM. Получено 2026-06-17 из https://scholargate.app/ru/compare