Machine Learning Time Series Regressions with an Application to Nowcasting

Andrii Babii, Eric Ghysels and Jonas Striaukas

Papers from arXiv.org

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.

Date: 2020-05, Revised 2020-12
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm, nep-ets and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

Downloads: (external link)
http://arxiv.org/pdf/2005.14057 Latest version (application/pdf)

Related works:
Journal Article: Machine Learning Time Series Regressions With an Application to Nowcasting (2022)
Working Paper: Machine Learning Time Series Regressions With an Application to Nowcasting (2021)
Working Paper: Machine Learning Time Series Regressions With an Application to Nowcasting (2021)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2005.14057

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().