High dimensional VAR with low rank transition
Statistics Theory
2022-01-17 v2 Methodology
Machine Learning
Statistics Theory
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.
Cite
@article{arxiv.1905.00959,
title = {High dimensional VAR with low rank transition},
author = {Pierre Alquier and Karine Bertin and Paul Doukhan and Rémy Garnier},
journal= {arXiv preprint arXiv:1905.00959},
year = {2022}
}