Machine Learning Time Series Regressions With an Application to Nowcasting

54 Pages Posted: 5 Jan 2020 Last revised: 12 Jul 2021

See all articles by Andrii Babii

Andrii Babii

University of North Carolina at Chapel Hill

Eric Ghysels

University of North Carolina Kenan-Flagler Business School; University of North Carolina (UNC) at Chapel Hill - Department of Economics

Jonas Striaukas

Louvain Finance; UC Louvain and F.R.S.-FNRS

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

Babii, Andrii and Ghysels, Eric and Striaukas, Jonas and Striaukas, Jonas, Machine Learning Time Series Regressions With an Application to Nowcasting (December 12, 2019). Journal of Business and Economic Statistics (forthcoming), Available at SSRN: https://ssrn.com/abstract=3503191 or http://dx.doi.org/10.2139/ssrn.3503191

Andrii Babii

University of North Carolina at Chapel Hill ( email )

Gardner Hall, CB 3305
Chapel Hill, NC 27514
United States

Eric Ghysels (Contact Author)

University of North Carolina Kenan-Flagler Business School ( email )

Kenan-Flagler Business School
Chapel Hill, NC 27599-3490
United States

University of North Carolina (UNC) at Chapel Hill - Department of Economics ( email )

Gardner Hall, CB 3305
Chapel Hill, NC 27599
United States
919-966-5325 (Phone)
919-966-4986 (Fax)

HOME PAGE: http://https://eghysels.web.unc.edu/

Jonas Striaukas

UC Louvain and F.R.S.-FNRS ( email )

34 Voie du Roman Pays
B-1348 Louvain-la-Neuve
Louvain la Neuve, 1348
Belgium

HOME PAGE: http://sites.google.com/site/striaukasj/

Louvain Finance ( email )

34 Voie du Roman Pays
B-1348 Louvain-la-Neuve, b-1348
Belgium
+3210479429 (Phone)
+3210479429 (Fax)

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