High dimensional VAR with low rank transition
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.
- Publication:
-
arXiv e-prints
- Pub Date:
- May 2019
- DOI:
- 10.48550/arXiv.1905.00959
- arXiv:
- arXiv:1905.00959
- Bibcode:
- 2019arXiv190500959A
- Keywords:
-
- Mathematics - Statistics Theory;
- Statistics - Methodology;
- Statistics - Machine Learning
- E-Print:
- Statistics and Computing, 2020, vol. 30, pp. 1139-1153