Machine Learning Time Series Regressions With an Application to Nowcasting
提出结构化机器学习回归方法处理高频混合频率时间序列数据，稀疏组LASSO估计量优于非结构化LASSO，并在美国GDP增长即时预测中表现良好，文本数据可补充传统数值数据。
This article 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. Our methodology is implemented in the R package midasml, available from CRAN.