Mixed-frequency machine learning: Nowcasting and backcasting weekly initial claims with daily internet search volume data

Daniel Borup, David E. Rapach and Erik Christian Schütte

International Journal of Forecasting, 2023, vol. 39, issue 3, 1122-1144

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 nowcasts 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. Nowcasts and backcasts of weekly initial claims based on models that incorporate the information in the daily Google Trends search volume data substantially outperform those based on models that ignore the information. Predictive accuracy increases as the nowcasts and backcasts include more recent daily Google Trends data. The relevance of daily Google Trends data 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 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:39:y:2023:i:3:p:1122-1144

DOI: 10.1016/j.ijforecast.2022.05.005

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