IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v65y2013icp68-79.html
   My bibliography  Save this article

Robust estimation for vector autoregressive models

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S016794731200093X
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2012.02.011?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Boudt, Kris & Croux, Christophe, 2010. "Robust M-estimation of multivariate GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2459-2469, November.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Grossi, Luigi & Heim, Sven & Waterson, Michael, 2014. "A vision of the European energy future? The impact of the German response to the Fukushima earthquake," ZEW Discussion Papers 14-051, ZEW - Leibniz Centre for European Economic Research.
    2. 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.
    3. 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.
    4. repec:esx:essedp:761 is not listed on IDEAS
    5. 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).
    6. 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.
    7. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Trucíos, Carlos & Hotta, Luiz K. & Valls Pereira, Pedro L., 2019. "On the robustness of the principal volatility components," Journal of Empirical Finance, Elsevier, vol. 52(C), pages 201-219.
    2. Gambacciani, Marco & Paolella, Marc S., 2017. "Robust normal mixtures for financial portfolio allocation," Econometrics and Statistics, Elsevier, vol. 3(C), pages 91-111.
    3. Trucíos, Carlos, 2019. "Forecasting Bitcoin risk measures: A robust approach," International Journal of Forecasting, Elsevier, vol. 35(3), pages 836-847.
    4. Francisco Javier Duque-Pintor & Manuel Jesús Fernández-Gómez & Alicia Troncoso & Francisco Martínez-Álvarez, 2016. "A New Methodology Based on Imbalanced Classification for Predicting Outliers in Electricity Demand Time Series," Energies, MDPI, vol. 9(9), pages 1-10, September.
    5. Grané, Aurea & Veiga, Helena, 2010. "Outliers in Garch models and the estimation of risk measures," DES - Working Papers. Statistics and Econometrics. WS ws100502, Universidad Carlos III de Madrid. Departamento de Estadística.
    6. Trucíos, Carlos & Mazzeu, João H.G. & Hotta, Luiz K. & Valls Pereira, Pedro L. & Hallin, Marc, 2021. "Robustness and the general dynamic factor model with infinite-dimensional space: Identification, estimation, and forecasting," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1520-1534.
    7. Eli Bouri & Andre Eid & Imad Kachacha, 2014. "The Dynamic Behaviour and Determinants of Linkages among Middle Eastern and North African Stock Exchanges," Economic Issues Journal Articles, Economic Issues, vol. 19(1), pages 1-22, March.
    8. Gatfaoui, Hayette, 2013. "Translating financial integration into correlation risk: A weekly reporting's viewpoint for the volatility behavior of stock markets," Economic Modelling, Elsevier, vol. 30(C), pages 776-791.
    9. Roelant, E. & Van Aelst, S. & Croux, C., 2009. "Multivariate generalized S-estimators," Journal of Multivariate Analysis, Elsevier, vol. 100(5), pages 876-887, May.
    10. Audrino, Francesco, 2014. "Forecasting correlations during the late-2000s financial crisis: The short-run component, the long-run component, and structural breaks," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 43-60.
    11. Laurent, Sébastien & Lecourt, Christelle & Palm, Franz C., 2016. "Testing for jumps in conditionally Gaussian ARMA–GARCH models, a robust approach," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 383-400.
    12. Alonso, Andrés M. & Galeano, Pedro & Peña, Daniel, 2020. "A robust procedure to build dynamic factor models with cluster structure," Journal of Econometrics, Elsevier, vol. 216(1), pages 35-52.
    13. Aguilar, Mike & Hill, Jonathan B., 2015. "Robust score and portmanteau tests of volatility spillover," Journal of Econometrics, Elsevier, vol. 184(1), pages 37-61.
    14. Galeano, Pedro, 2007. "The use of cumulative sums for detection of changepoints in the rate parameter of a Poisson Process," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6151-6165, August.
    15. João A. Bastos & Jorge Caiado, 2021. "On the classification of financial data with domain agnostic features," Working Papers REM 2021/0185, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    16. 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.
    17. Andrea Bergesio & María Eugenia Szretter Noste & Víctor J. Yohai, 2021. "A robust proposal of estimation for the sufficient dimension reduction problem," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(3), pages 758-783, September.
    18. Grané, Aurea & Veiga, Helena, 2009. "Wavelet-based detection of outliers in volatility models," DES - Working Papers. Statistics and Econometrics. WS ws090403, Universidad Carlos III de Madrid. Departamento de Estadística.
    19. repec:esx:essedp:761 is not listed on IDEAS
    20. Santos, André A.P. & Moura, Guilherme V., 2014. "Dynamic factor multivariate GARCH model," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 606-617.
    21. Keisuke Yoshihara & Kei Takahashi, 2022. "A simple method for unsupervised anomaly detection: An application to Web time series data," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-25, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:65:y:2013:i:c:p:68-79. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.