Informing VAR(1) with qualitative dynamical features improves predictive accuracy. KU Leuven
The AR(1) model has been shown to outperform the general VAR(1) model on typical affective time series. Even in combination with a lasso penalty, the reduced VAR(1) model (VAR-lasso) is generally outperformed. A reason for the AR dominance is that the VAR-lasso selects models that are still too complex-the space of all possible VAR models includes simpler models but these are hard to select with a traditional lasso penalty. In this article, we ...