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英文文献：Estimating High-Dimensional Time Series Models-估计高维时间序列模型
英文文献作者：Marcelo C. Medeiros,Eduardo F. Mendes
英文文献摘要：
We study the asymptotic properties of the Adaptive LASSO (adaLASSO) in sparse, high-dimensional, linear time-series models. We assume both the number of covariates in the model and candidate variables can increase with the number of observations and the number of candidate variables is, possibly, larger than the number of observations. We show the adaLASSO consistently chooses the relevant variables as the number of observations increases (model selection consistency), and has the oracle property, even when the errors are non-Gaussian and conditionally heteroskedastic. A simulation study shows the method performs well in very general settings. Finally, we consider two applications: in the first one the goal is to forecast quarterly US inflation one-step ahead, and in the second we are interested in the excess return of the S&P 500 index. The method used outperforms the usual benchmarks in the literature.
研究了自适应LASSO (adaLASSO)在稀疏、高维、线性时间序列模型中的渐近性质。我们假设模型中的协变量和候选变量的数量都可以随着观测次数的增加而增加，并且候选变量的数量可能大于观测次数。我们表明，adaLASSO一致地选择相关的变量随着观测数量的增加(模型选择一致性)，并具有oracle性质，即使当误差是非高斯和条件异方差。仿真研究表明，该方法在一般情况下都有良好的性能。最后，我们考虑两种应用:在第一种应用中，我们的目标是提前一步预测美国季度通胀;在第二种应用中，我们感兴趣的是标准普尔500指数(S&P 500 index)的超额回报率。所使用的方法优于文献中常用的基准。
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