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arXiv · 1905.00959

High dimensional VAR with low rank transition

Pierre Alquier·Karine Bertin·Paul Doukhan·Rémy Garnier
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Abstract

We propose a vector auto-regressive (VAR) model with a low-rank constraint on the transition matrix. This new model is well suited to predict high-dimensional series that are highly correlated, or that are driven by a small number of hidden factors. We study estimation, prediction, and rank selection for this model in a very general setting. Our method shows excellent performances on a wide variety of simulated datasets. On macro-economic data from Giannone et al. (2015), our method is competitive with state-of-the-art methods in small dimension, and even improves on them in high dimension.

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BibTeXRIS

Pierre Alquier, Karine Bertin, Paul Doukhan, Rémy Garnier. 2019-05-02. High dimensional VAR with low rank transition. https://doi.org/10.1007/s11222-020-09929-7

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