Robust estimation of the vector autoregressive model by a least trimmed squares procedure

被引：22

作者：

Croux, Christophe ^{[1
]}

Joossens, Kristel ^{[1
]}

机构：

[1] Katholieke Univ Leuven, Fac Business & Econ, B-3000 Louvain, Belgium

来源：

COMPSTAT 2008: PROCEEDINGS IN COMPUTATIONAL STATISTICS | 2008年

关键词：

robustness; multivariate time series; outliers; trimming; vector autoregressive models;

D O I：

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

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

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.

引用

页码：489 / 501

页数：13

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