a bayesian semiparametric competing risk model with unobserved heterogeneity (replication data)
This paper generalizes existing econometric models for censored competing risks by introducing a new flexible specification based on a piecewise linear baseline hazard, time-varying regressors, and unobserved individual heterogeneity distributed as an infinite mixture of generalized inverse Gaussian (GIG) densities, nesting the gamma kernel as a special case. A common correlated latent time effect induces dependence among risks. Our model is based on underlying latent exit decisions in continuous time while only a time interval containing the exit time is observed, as is common in economic data. We do not make the simplifying assumption of discretizing exit decisions-our competing risk model setup allows for latent exit times of different risk types to be realized within the same time period. In this setting, we derive a tractable likelihood based on scaled GIG Laplace transforms and their higher-order derivatives. We apply our approach to analyzing the determinants of unemployment duration with exits to jobs in the same industry or a different industry among unemployment insurance recipients on nationally representative individual-level survey data from the US Department of Labor. Our approach allows us to conduct a counterfactual policy experiment by changing the replacement rate: we find that the impact of its change on the probability of exit from unemployment is inelastic.
Hausman, Jerry A.
A Bayesian Semiparametric Competing Risk Model with Unobserved Heterogeneity (replication data).
Journal of Applied Econometrics.
Burda, M., Harding, M. and Hausman, J. (2015), A Bayesian Semiparametric Competing Risk Model With Unobserved Heterogeneity, Journal of Applied Econometrics, 30(3), 353-376. https://doi.org/10.1002/jae.2368