A vector autoregressive (VAR) model is a powerful tool for analyzing multivariate time series data. The recent increase in the availability of such time series ...

Jul 4, 2021 · Our simulation results show that the proposed modification or msVAR is a preferred choice when the goal is to learn the structure of the AR ...

Jul 4, 2021 · Our simulation results show that the proposed modification or msVAR is a preferred choice when the goal is to learn the structure of the AR ...

It relies on the more recent and powerful technique of time series graphical lasso to estimate sparse inverse spectral density matrices in the first stage, and ...

The Glasso algorithm is explored and a new graphiclasso command for the large inverse-covariance matrix estimation is introduced and provided, ...

Estimates the graphical VAR (Wild et al., 2010) model through LASSO estimation coupled with extended Bayesian information criterion for choosing the optimal ...

Dec 1, 2022 · A two-stage sparse vector autoregression method is proposed. It relies on the more recent and powerful technique of time series graphical ...

Jul 4, 2021 · A sparsity scheme for high-dimensional VAR models is proposed which is found to be more appropriate for the time series setting considered.

The graphical lasso, proposed in Friedman, Hastie, and Tibshirani (2008), uses the block coordinate descent algorithm to estimate sparse inverse-covariance ...

Graphical modeling of high-dimensional time series. The tsGLASSO command estimates sparse inverse spectral density matrix for high-dimensional time series ...