ℓ 1 -regularization of high-dimensional time-series models with non-Gaussian and heteroskedastic errors
非高斯与异方差误差下高维时间序列模型的ℓ₁正则化
We study the asymptotic properties of the Adaptive LASSO (adaLASSO) in sparse, high-dimensional, linear time-series models. The adaLASSO is a one-step implementation of the family of folded concave penalized least-squares. We assume that both the number of covariates in the model and the number of candidate variables can increase with the sample size (polynomially or geometrically). In other words, we let the number of candidate variables to be larger than the number of…