Partially identifying competing risks models: An application to the war on cancer

Abstract Competing risks models for discretely measured durations are partially identifying due to the unknown dependence structure between risks and the discrete nature of the outcome. This article develops a highly tractable bounds approach for underlying distributions of latent durations by exploiting the discreteness. Bounds are obtained from a system of nonlinear (in)equalities. I devise a sequential solution method that requires much less computational burden than existing methods. Asymptotic properties of bound estimators and a simple bootstrap procedure are provided. I apply the proposed approach to re-evaluate trends in cancer mortality extending the data studied in Honore and Lleras-Muney (2006). Estimated patterns differ from the original findings.

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