Partial identification in nonseparable count data instrumental variable models

This paper investigates undesirable limitations of widely used count data instrumental variable models. To overcome the limitations, I propose a partially identifying single-equation model that requires neither strong separability of unobserved heterogeneity nor a triangular system. Sharp bounds (identified sets) of structural features are characterised by conditional moment inequalities. Numerical examples show that the size of an identified set can be very small when the support of an outcome is rich or instruments are strong. An algorithm for estimation and inference is presented. I illustrate the usefulness of the proposed model in an empirical application to effects of supplemental insurance on healthcare utilisation.