Machine Learning Time Series Regressions With an Application to Nowcasting

Andrii Babii, Eric Ghysels*, Jonas Striaukas

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review


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.
Original languageEnglish
JournalJournal of Business and Economic Statistics
Issue number3
Pages (from-to)1094-1106
Number of pages13
Publication statusPublished - 2022
Externally publishedYes

Bibliographical note

Published online: 21 Apr 2021.


  • Fat tails
  • High-dimensional time series
  • Mixed-frequency data
  • Sparse-group LASSO
  • Tau-mixing
  • Textual news data

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