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Instrumental Variable Models for Discrete Outcomes
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DOI：10.3982/ECTA7315.png)
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Abstract
En 中文
Single equation instrumental variable models for discrete outcomes are shown to be set identifying, not point identifying, for the structural functions that deliver the values of the discrete outcome. Bounds on identified sets are derived for a general nonparametric model and sharp set identification is demonstrated in the binary outcome case. Point identification is typically not achieved by imposing parametric restrictions. The extent of an identified set varies with the strength and support of instruments, and typically shrinks as the support of a discrete outcome grows. The paper extends the analysis of structural quantile functions with endogenous arguments to cases in which there are discrete outcomes.
Keywords:
Partial identification
nonparametric methods
nonadditive models
discrete distributions
ordered choice
endogeneity
instrumental variables
structural quantile functions
incomplete models
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