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 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.
Available from June 28, 2024
In series:OECD Papers on Well-being and Inequalitiesview more titles