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The non-identifiability of the competing risks model requires researchers towork with restrictions on the model to obtain informative results. We present anew identifiability solution based on an exclusion restriction. Many areas ofapplied research use methods that rely on exclusion restrcitions. It appearsnatural to also use them for the identifiability of competing risks models. Byimposing the exclusion restriction couple with an Archimedean copula, we areable to avoid any parametric restriction on the marginal distributions. Weintroduce a semiparametric estimation approach for the nonparametric marginalsand the parametric copula. Our simulation results demonstrate the usefulness ofthe suggested model, as the degree of risk dependence can be estimated withoutparametric restrictions on the marginal distributions.

Identifiability and estimation of the competing risks model under exclusion restrictions