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?:abstract
  • This paper applies Machine learning techniques to Google Trends data to provide real-time estimates of national average subjective well-being among 38 OECD countries since 2010. We make extensive usage of large custom micro databases to enhance the training of models on carefully pre-processed Google Trends data. We find that the best one-year-ahead prediction is obtained from a meta-learner that combines the predictions drawn from an Elastic Net with and without interactions, from a Gradient-Boosted Tree and from a Multi-layer Perceptron. As a result, across 38 countries over the 2010-2020 period, the out-of-sample prediction of average subjective well-being reaches an R2 of 0.830. (xsd:string)
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  • EVS-Bibliography (xsd:string)
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  • 2024 (xsd:gyear)
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  • 2024 (xsd:gyear)
?:doi
  • 10.1787/cbdfb5d9-en ()
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  • english (xsd:string)
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  • 27 (xsd:string)
?:name
  • Nowcasting subjective well-being with Google Trends: A meta-learning approach (xsd:string)
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  • techreport (xsd:string)
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  • (27), OECD Publishing, 2024 (xsd:string)
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  • European Values Study (EVS) (xsd:string)
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  • 2024 (xsd:string)
  • EVS (xsd:string)
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  • english (xsd:string)
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