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Now- and Backcasting Initial Claims with High-Dimensional Daily Internet Search-Volume Data

Daniel Borup (), David E. Rapach () and Erik Christian Schütte ()
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Daniel Borup: Aarhus University, CREATES and the Danish Finance Institute (DFI), Postal: Department of Economics and Business Economics, Fuglesangs Allé 4, 8210 Aarhus V, Denmark
David E. Rapach: Washington University in St. Louis and Saint Louis University, Postal: Chaifetz School of Business, 3674 Lindell Boulevard, St. Louis, MO 63108, USA

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

Abstract: We generate a sequence of now- and backcasts of weekly unemployment insurance initial claims (UI) based on a rich trove of daily Google Trends (GT) search-volume data for terms related to unemployment. To harness the information in a high-dimensional set of daily GT terms, we estimate predictive models using machine-learning techniques in a mixed-frequency framework. In a simulated out-of-sample exercise, now- and backcasts of weekly UI that incorporate the information in the daily GT terms substantially outperform models that ignore the information. The relevance of GT terms for predicting UI is strongly linked to the COVID-19 crisis.

Keywords: Unemployment insurance; Internet search; Mixed-frequency data; Penalized regression; Neural network; Variable importance (search for similar items in EconPapers)
JEL-codes: C45 C53 C55 E24 E27 J65 (search for similar items in EconPapers)
Pages: 48
Date: 2021-01-11
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm, nep-ias, nep-lab and nep-mac
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