Identifiability and estimation of the competing risks model under exclusion restrictions

TL;DR

This paper introduces a new method for identifying and estimating competing risks models using exclusion restrictions and copulas, allowing flexible marginal distributions and accurate dependence estimation.

Contribution

It proposes a semiparametric estimation approach combining exclusion restrictions with Archimedean copulas, avoiding parametric assumptions on marginals.

Findings

Effective estimation of risk dependence without parametric marginal restrictions

Simulation results confirm the method's accuracy and usefulness

Model provides a flexible framework for competing risks analysis

Abstract

The non-identifiability of the competing risks model requires researchers to work with restrictions on the model to obtain informative results. We present a new identifiability solution based on an exclusion restriction. Many areas of applied research use methods that rely on exclusion restrcitions. It appears natural to also use them for the identifiability of competing risks models. By imposing the exclusion restriction couple with an Archimedean copula, we are able to avoid any parametric restriction on the marginal distributions. We introduce a semiparametric estimation approach for the nonparametric marginals and the parametric copula. Our simulation results demonstrate the usefulness of the suggested model, as the degree of risk dependence can be estimated without parametric restrictions on the marginal distributions.