We consider the exit routes of older employees out of employment around retirement age. Our administrative data cover weekly information about the Danish population from 2004 to 2016 and 397 variables from 16 linked administrative registers. We use a flexible dependent competing risks quantile regression model to identify how early and late retirement transitions are related to the information in various registers. Our model selection is guided by machine learning methods, in particular statistical regularization. We use the (adaptive) group bridge to identify the relevant administrative registers and variables in heterogeneous and high-dimensional data, while maintaining the oracle property. By applying state-of-the-art statistical methods, we obtain detailed insights into conditional distributions of transition times into the main pension programs in Denmark.
Bibliographical noteEpub ahead of print. Published online: 28. August
- Adaptive group bridge
- Competing risks
- Quantile regression
- Statistical learning