Machine Learning Time Series Regressions With an Application to Nowcasting

Andrii Babii, Eric Ghysels and Jonas Striaukas
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Eric Ghysels: Université catholique de Louvain, LIDAM/CORE, Belgium

No 2021010, LIDAM Reprints LFIN from Université catholique de Louvain, Louvain Finance (LFIN)

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 midasml, available from CRAN.

Keywords: high-dimensional time series; fat tails; tau-mixing; sparse-group LASSO; mixed frequency data; textual news data (search for similar items in EconPapers)
Pages: 13
Date: 2021-04-21
Note: In: Journal of Business and Economic Statistics, 2021
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Citations: View citations in EconPapers (31)

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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 (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:ajf:louvlr:2021010

DOI: 10.1080/07350015.2021.1899933

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