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

Christophe Croux () and Kristel Joossens
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Christophe Croux: Katholieke Universiteit Leuven, Faculty of Business and Economics
Kristel Joossens: Katholieke Universiteit Leuven, Faculty of Business and Economics

A chapter in COMPSTAT 2008, 2008, pp 489-501 from Springer

Abstract: 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.

Keywords: robustness; multivariate time series; outliers; trimming; vector autoregressive models (search for similar items in EconPapers)
Date: 2008
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-2084-3_40

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DOI: 10.1007/978-3-7908-2084-3_40

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