摘要翻译：
非结构性经济和金融预测的一个流行方法是将大量的经济和金融变量包括在内，这已经证明可以显著地改进预测，例如动态因子模型。一个具有挑战性的问题是确定哪些变量及其滞后是相关的，尤其是当存在序列相关（时间动态）、高维（空间）依赖结构和中等样本容量（相对于维度和滞后）的混合时。为此，一个同时解决这三个挑战的\textIt{integrated}解决方案很有吸引力。本文用三种类型的估计来研究大向量自回归。我们将每个变量自身的滞后与其他变量的滞后区别对待，区分出随时间变化的各种滞后，并能够同时选择变量和滞后。首先给出了不考虑时间相关性而直接对时间序列使用Lasso型估计的结果。相比之下，在这种情况下，我们提出的方法仍然可以产生与\textIt{oracle}一样有效的估计。通过数据驱动的“滚动方案”方法选择调整参数以优化预测性能。考虑了一个宏观经济和金融预测问题，以说明其优于现有估计。
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英文标题：
《Large Vector Auto Regressions》
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作者：
Song Song and Peter J. Bickel
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最新提交年份：
2011
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分类信息：

一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
  One popular approach for nonstructural economic and financial forecasting is to include a large number of economic and financial variables, which has been shown to lead to significant improvements for forecasting, for example, by the dynamic factor models. A challenging issue is to determine which variables and (their) lags are relevant, especially when there is a mixture of serial correlation (temporal dynamics), high dimensional (spatial) dependence structure and moderate sample size (relative to dimensionality and lags). To this end, an \textit{integrated} solution that addresses these three challenges simultaneously is appealing. We study the large vector auto regressions here with three types of estimates. We treat each variable's own lags different from other variables' lags, distinguish various lags over time, and is able to select the variables and lags simultaneously. We first show the consequences of using Lasso type estimate directly for time series without considering the temporal dependence. In contrast, our proposed method can still produce an estimate as efficient as an \textit{oracle} under such scenarios. The tuning parameters are chosen via a data driven "rolling scheme" method to optimize the forecasting performance. A macroeconomic and financial forecasting problem is considered to illustrate its superiority over existing estimators.
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PDF链接：
https://arxiv.org/pdf/1106.3915