Nowcasting subjective well-being with Google Trends: A meta-learning approach
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 Googl...
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| Main Author: | |
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| Other Authors: | |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Paris
OECD Publishing
2024
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| Series: | OECD Papers on Well-being and Inequalities
no.27 |
| Subjects: | |
| Online Access: | DE-862 DE-863 |
| Summary: | 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. |
| Physical Description: | 1 Online-Ressource (27 Seiten) 21 x 28cm. |
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| spelling | Murtin, Fabrice VerfasserIn aut Nowcasting subjective well-being with Google Trends A meta-learning approach Fabrice, Murtin and Max, Salomon-Ermel Paris OECD Publishing 2024 1 Online-Ressource (27 Seiten) 21 x 28cm. Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier OECD Papers on Well-being and Inequalities no.27 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. Social Issues/Migration/Health Salomon-Ermel, Max MitwirkendeR ctb |
| spellingShingle | Murtin, Fabrice Nowcasting subjective well-being with Google Trends A meta-learning approach Social Issues/Migration/Health |
| title | Nowcasting subjective well-being with Google Trends A meta-learning approach |
| title_auth | Nowcasting subjective well-being with Google Trends A meta-learning approach |
| title_exact_search | Nowcasting subjective well-being with Google Trends A meta-learning approach |
| title_full | Nowcasting subjective well-being with Google Trends A meta-learning approach Fabrice, Murtin and Max, Salomon-Ermel |
| title_fullStr | Nowcasting subjective well-being with Google Trends A meta-learning approach Fabrice, Murtin and Max, Salomon-Ermel |
| title_full_unstemmed | Nowcasting subjective well-being with Google Trends A meta-learning approach Fabrice, Murtin and Max, Salomon-Ermel |
| title_short | Nowcasting subjective well-being with Google Trends |
| title_sort | nowcasting subjective well being with google trends a meta learning approach |
| title_sub | A meta-learning approach |
| topic | Social Issues/Migration/Health |
| topic_facet | Social Issues/Migration/Health |
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