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Robust Estimation of the Vector Autoregressive Model by a Least Trimmed Squares Procedure

In: Compstat 2008

Author

Listed:
  • Christophe Croux

    (Katholieke Universiteit Leuven, Faculty of Business and Economics)

  • Kristel Joossens

    (Katholieke Universiteit Leuven, Faculty of Business and Economics)

Abstract

The vector autoregressive model is very popular for modeling multiple time series. Estimation of its parameters is typically done by a least squares procedure. However, this estimation method is unreliable when outliers are present in the data, and therefore we propose to estimate the vector autoregressive model by using a multivariate least trimmed squares estimator. We also show how the order of the autoregressive model can be determined in a robust way. The robust procedure is illustrated on a real data set.

Suggested Citation

  • Christophe Croux & Kristel Joossens, 2008. "Robust Estimation of the Vector Autoregressive Model by a Least Trimmed Squares Procedure," Springer Books, in: Paula Brito (ed.), Compstat 2008, pages 489-501, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2084-3_40
    DOI: 10.1007/978-3-7908-2084-3_40
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