Adaptive lasso in sparse vector autoregressive models

This paper considers variable selection in the sparse vector autoregressive (sVAR) model where sparsitycomes from setting small coecients to exact zeros. In the estimation perspective, Davis et al. (2015)showed that the lasso type of regularization method is successful because it provides a simultaneous variableselection and parameter estimation even for time series data. However, their simulations study reports thatthe regular lasso overestimates the number of non-zero coecients, hence its nite sample performance needsimprovements. In this article, we show that the adaptive lasso signicantly improves the performance wherethe adaptive lasso nds the sparsity patterns superior to the regular lasso. Some tuning parameter selectionsin the adaptive lasso are also discussed from the simulations study.Keywords: sparse vector autoregressive model, adaptive lasso, high dimensional time series 1. . (vector autoregressive model; VAR). VAR (interdependence) (temporal depen-dence) .