Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/195549 
Year of Publication: 
2017
Citation: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 8 [Issue:] 2 [Publisher:] The Econometric Society [Place:] New Haven, CT [Year:] 2017 [Pages:] 589-610
Publisher: 
The Econometric Society, New Haven, CT
Abstract: 
This paper establishes nonparametric identification of individual treatment effects in a nonseparable model with a binary endogenous regressor. The outcome variable may be continuous, discrete, or a mixture of both, while the instrumental variable can take binary values. First, we study the case where the model includes a selection equation for the binary endogenous regressor. We establish point identification of the individual treatment effects and the structural function when the latter is continuous and strictly monotone in the latent variable. The key to our results is the identification of a so-called counterfactual mapping that links each outcome of the dependent variable with its counterfactual. Second, we extend our identification argument when there is no selection equation. Last, we generalize our identification results to the case where the outcome variable has a probability mass in its distribution such as when the outcome variable is censored or binary.
Subjects: 
Nonparametric identification
nonseparable models
discrete endogenous variable
counterfactual mapping
individual treatment effects
JEL: 
C14
C18
C30
C36
C50
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

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