Title: Robust estimation of the vector autoregressive model by a trimmed least squares procedure
Authors: Joossens, Kristel
Croux, Christophe
Issue Date: 2004
Publisher: K.U.Leuven - Departement toegepaste economische wetenschappen
Series Title: DTEW Research Report 0467 pages:1-23
Abstract: The vector autoregressive model is very popular for modeling multiple time series. Estimation of its parameters is done by a least squares procedure. However, this estimation method is unreliable when outliers are present in the data, and there is a need for robust alternatives. In this paper we propose to estimate the vector autoregressive model by using a trimmed least squares estimator. We show how the order of the autoregressive model can be determined in a robust way, and how confidence bounds around the robustly estimated impulse response functions can be constructed. The resistance of the estimators to outliers is studied on real and simulated data.
Publication status: published
KU Leuven publication type: IR
Appears in Collections:Research Center for Operations Research and Business Statistics (ORSTAT), Leuven

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