Identification and estimation of triangular models with a binary treatment

Santiago Pereda-Fernández

No 1210, Temi di discussione (Economic working papers) from Bank of Italy, Economic Research and International Relations Area

Abstract: I study the identification and estimation of a nonseparable triangular model with an endogenous binary treatment. Unlike other studies, I do not impose rank invariance or rank similarity on the unobservable of the outcome equation. Instead, I achieve identification using continuous variation of the instrument and a shape restriction on the distribution of the unobservables, which is modeled with a copula. The latter captures the endogeneity of the model and is one of the components of the marginal treatment effect, making it informative about the effects of extending the treatment to untreated individuals. The estimation is a multi-step procedure based on rotated quantile regression. Finally, I use the estimator to revisit the effects of Work First Job Placements on future earnings.

Keywords: copula; endogeneity; policy analysis; quantile regression; unconditional distributional effects (search for similar items in EconPapers)
JEL-codes: C31 C36 (search for similar items in EconPapers)
Date: 2019-03
New Economics Papers: this item is included in nep-ecm
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Journal Article: Identification and estimation of triangular models with a binary treatment (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:bdi:wptemi:td_1210_19

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