Nowcasting with panels and alternative data: The OECD weekly tracker

Nicolas Woloszko

International Journal of Forecasting, 2024, vol. 40, issue 4, 1302-1335

Abstract: Alternative data are timely but messy. They can provide policymakers with real-time information, but their use is constrained by the complexity of their relationship with official statistics. Data from credit card transactions, search engines, or traffic have been made available since only recently, which makes it more difficult to precisely gauge their relationship with national accounts. This paper aims at solving this problem by compensating their short history with their large country coverage. It introduces a heterogeneous panel model approach where a neural network learns the relationship between Google Trends data and GDP growth from data pooled from 46 countries. The resulting “OECD Weekly Tracker” yields real-time estimates of weekly GDP, which are proven to be accurate using forecast simulations. It is a valuable compass for policymaking in turbulent waters.

Keywords: Nowcasting; Google trends; High-frequency; Machine learning; Neural network; Interpretability; COVID-19 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:40:y:2024:i:4:p:1302-1335

DOI: 10.1016/j.ijforecast.2023.11.005

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