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.
|Journal||Journal of Business and Economic Statistics|
|Number of pages||13|
|Publication status||Published - 2022|
Bibliographical notePublished online: 21 Apr 2021.
- Fat tails
- High-dimensional time series
- Mixed-frequency data
- Sparse-group LASSO
- Textual news data