High dimensional VAR with low rank transition
This work addresses forecasting in high-dimensional correlated time series, such as macro-economic data, but appears incremental as it builds on existing VAR models with a low-rank constraint.
The authors tackled the problem of predicting high-dimensional time series by proposing a vector auto-regressive model with a low-rank constraint on the transition matrix, which showed competitive performance on macro-economic data and improvements in high dimensions.
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.