Sparse, Low-bias, and Scalable Estimation of High Dimensional Vector Autoregressive Models via Union of Intersections

29 Aug 2019 Ruiz Trevor Balasubramanian Mahesh Bouchard Kristofer E. Bhattacharyya Sharmodeep

Vector autoregressive (VAR) models are widely used for causal discovery and forecasting in multivariate time series analyses in fields as diverse as neuroscience, environmental science, and econometrics. In the high-dimensional setting, model parameters are typically estimated by L1-regularized maximum likelihood; yet, when applied to VAR models, this technique produces a sizable trade-off between sparsity and bias with the choice of the regularization hyperparameter, and thus between causal discovery and prediction... (read more)

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