论文标题
机器学习时间序列序列回归，并通过启用启示
Machine Learning Time Series Regressions with an Application to Nowcasting
论文作者
论文摘要
本文介绍了可能以不同频率采样的高维时间序列数据的结构化机器学习回归。稀疏的组套索估计器可以利用这种时间序列数据结构，并优于非结构化的套索。我们在一个框架内建立了稀疏组套索估计器的甲骨文不平等，该框架允许混合过程，并认识到财务和宏观经济数据可能比指数尾巴更重。与其他替代方案相比，估算器的实证应用程序表明，估计器的性能优惠，并且文本数据可能是更传统的数值数据的有用补充。
This paper introduces structured machine learning regressions for high-dimensional time series data potentially sampled at different frequencies. The sparse-group LASSO estimator can take advantage of such time series data structures and outperforms the unstructured LASSO. We establish oracle inequalities for the sparse-group LASSO estimator within a framework that allows for the mixing processes and recognizes that the financial and the macroeconomic data may have heavier than exponential tails. An empirical application to nowcasting US GDP growth indicates that the estimator performs favorably compared to other alternatives and that text data can be a useful addition to more traditional numerical data.
