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Machine Learning Time Series Regressions With an Application to Nowcasting

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.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF BUSINESS AND ECONOMIC STATISTICS; Vol.40 No.3 July 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 1094-1106

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

MIXED-FREQUENCY DATA
HIGH-DIMENSIONAL TIME SERIES
FAT TAILS
SPARSE-GROUP LASSO
TAU-MIXING
TEXTUAL NEWS DATA

Info Detail Spesifik

https://doi.org/10.1080/07350015.2021.1899933

Pernyataan Tanggungjawab

Andrii Babii, Eric Ghysels

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