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.

References in corpus (3)

• Low Rank and Structured Modeling of High-dimensional Vector Autoregressions
• Factor modeling for high-dimensional time series: Inference for the number of factors
• Matrix factorization for multivariate time series analysis

Cited by in corpus (2)

• Deviation inequalities for stochastic approximation by averaging
• Tight Risk Bound for High Dimensional Time Series Completion