Abstract
Many popular estimators for duration models require independent competing risks or independent censoring. In contrast, copula-based estimators are also consistent in the presence of dependent competing risks. We suggest a computationally convenient extension of the copula graphic estimator to a model with more than two dependent competing risks. We analyse the applicability of this estimator by means of simulations and unemployment duration data from Germany. We obtain evidence that our estimator yields nice results if the dependence structure is known and that it is a powerful tool for the assessment of the relevance of (in-)dependence assumptions in applied duration research.
| Original language | English |
|---|---|
| Pages (from-to) | 359-376 |
| Number of pages | 18 |
| Journal | Journal of the Royal Statistical Society. Series C: Applied Statistics |
| Volume | 59 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - Mar 2010 |
| Externally published | Yes |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 8 Decent Work and Economic Growth
Keywords
- Archimedean copula
- Dependent censoring
- Duration of unemployment
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty
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