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Robust estimation for vector autoregressive models

Author

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  • Muler, Nora
  • Yohai, V´ictor J.

Abstract

A new class of robust estimators for VAR models is introduced. These estimators are an extension to the multivariate case of the MM-estimators based on a bounded innovation propagation AR model. They have a filtering mechanism that avoids the propagation of the effect of one outlier to the residuals of the subsequent periods. Besides, they are consistent and have the same asymptotic normal distribution as regular MM-estimators for VAR models. A Monte Carlo study shows that these estimators compare favorable with respect to other robust ones.

Suggested Citation

  • Muler, Nora & Yohai, V´ictor J., 2013. "Robust estimation for vector autoregressive models," Computational Statistics & Data Analysis, Elsevier, vol. 65(C), pages 68-79.
    • Handle: RePEc:eee:csdana:v:65:y:2013:i:c:p:68-79
    DOI: 10.1016/j.csda.2012.02.011
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    References listed on IDEAS

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    1. Ben, Marta García & Martínez, Elena & Yohai, Víctor J., 2006. "Robust estimation for the multivariate linear model based on a [tau]-scale," Journal of Multivariate Analysis, Elsevier, vol. 97(7), pages 1600-1622, August.
    2. Boudt, Kris & Croux, Christophe, 2010. "Robust M-estimation of multivariate GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2459-2469, November.
    3. Croux, Christophe & Gelper, Sarah & Mahieu, Koen, 2010. "Robust exponential smoothing of multivariate time series," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 2999-3006, December.
    4. Galeano, Pedro & Pena, Daniel & Tsay, Ruey S., 2006. "Outlier Detection in Multivariate Time Series by Projection Pursuit," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 654-669, June.
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    Cited by:

    1. Garciga, Christian & Verbrugge, Randal, 2021. "Robust covariance matrix estimation and identification of unusual data points: New tools," Research in Economics, Elsevier, vol. 75(2), pages 176-202.
    2. Grossi, Luigi & Heim, Sven & Waterson, Michael, 2014. "A vision of the European energy future? The impact of the German response to the Fukushima earthquake," The Warwick Economics Research Paper Series (TWERPS) 1047, University of Warwick, Department of Economics.
    3. Le Chang & Yanlin Shi, 2024. "A discussion on the robust vector autoregressive models: novel evidence from safe haven assets," Annals of Operations Research, Springer, vol. 339(3), pages 1725-1755, August.
    4. Fokianos, Konstantinos & Fried, Roland & Kharin, Yuriy & Voloshko, Valeriy, 2022. "Statistical analysis of multivariate discrete-valued time series," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    5. Gordon C. R. Kemp & Paulo M. D. C. Parente & J. M. C. Santos Silva, 2020. "Dynamic Vector Mode Regression," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(3), pages 647-661, July.
    6. Christian Garciga & Randal J. Verbrugge, 2020. "A New Tool for Robust Estimation and Identification of Unusual Data Points," Working Papers 20-08, Federal Reserve Bank of Cleveland.
    7. repec:esx:essedp:761 is not listed on IDEAS
    8. Pagnottoni, Paolo & Spelta, Alessandro, 2023. "The motifs of risk transmission in multivariate time series: Application to commodity prices," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).

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