Machine Learning Time Series Regressions With an Application to Nowcasting
54 Pages Posted: 5 Jan 2020 Last revised: 12 Jul 2021
Date Written: December 12, 2019
Abstract
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. Our methodology is implemented in the R package \textbf{midasml}, available from CRAN.
Keywords: high-dimensional time series, heavy-tails, tau-mixing, sparse-group LASSO, mixed frequency data, textual news data
JEL Classification: C52, C55, C58
Suggested Citation: Suggested Citation