Mixed-Frequency Machine Learning: Now- and Backcasting Weekly Initial Claims with Daily Internet Search-Volume Data

52 Pages Posted: 13 Sep 2020 Last revised: 29 Jul 2021

See all articles by Daniel Borup

Daniel Borup

Aarhus University, CREATES, DFI

David Rapach

Saint Louis University; Washington University in St. Louis

Erik Christian Montes Schütte

Aarhus University - CREATES; DFI

Date Written: July 28, 2021

Abstract

We propose an out-of-sample prediction approach that combines unrestricted mixed-data sampling with machine learning (mixed-frequency machine learning, MFML). We use the MFML approach to generate a sequence of now- and backcasts of weekly unemployment insurance initial claims based on a rich trove of daily Google Trends search-volume data for terms related to unemployment. The predictions are based on linear models estimated via the LASSO and elastic net, nonlinear models based on artificial neural networks, and ensembles of linear and nonlinear models. Now- and backcasts of weekly initial claims based on models that incorporate the information in the daily Google Trends terms substantially outperform those based on models that ignore the information, and predictive accuracy increases as the now- and backcasts include more recent daily Google Trends data. The relevance of daily Google Trends terms for predicting weekly initial claims is strongly linked to the COVID-19 crisis.

Keywords: Mixed-frequency data, LASSO, Elastic net, Neural network, Unemployment insurance, Internet search, Variable importance

JEL Classification: C45, C53, C55, E24, E27, J65

Suggested Citation

Borup, Daniel and Rapach, David and Schütte, Erik Christian Montes, Mixed-Frequency Machine Learning: Now- and Backcasting Weekly Initial Claims with Daily Internet Search-Volume Data (July 28, 2021). Available at SSRN: https://ssrn.com/abstract=3690832 or http://dx.doi.org/10.2139/ssrn.3690832

Daniel Borup

Aarhus University, CREATES, DFI ( email )

School of Business and Social Sciences
Fuglesangs Alle 4
Aarhus V, 8210
Denmark

David Rapach (Contact Author)

Saint Louis University ( email )

3674 Lindell Blvd
St. Louis, MO 63108-3397
United States

HOME PAGE: http://https://sites.google.com/slu.edu/daverapach

Washington University in St. Louis

One Brookings Drive
Campus Box 1133
St. Louis, MO 63130-4899
United States

HOME PAGE: http://https://sites.google.com/slu.edu/daverapach

Erik Christian Montes Schütte

Aarhus University - CREATES ( email )

School of Economics and Management
Building 1322, Bartholins Alle 10
DK-8000 Aarhus C
Denmark

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