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Econometrics 2016, 4(1), 7;

Multiple Discrete Endogenous Variables in Weakly-Separable Triangular Models

CAPCP and Department of Economics, Pennsylvania State University, 608 Kern Graduate Building, University Park, PA 16802, USA
Department of Economics, University of Texas at Austin, 78712 Austin, TX, USA
Department of Economics, University of Rochester, 222 Harkness Hall, Rochester, NY 14627, USA
Author to whom correspondence should be addressed.
Academic Editor: William Greene
Received: 7 September 2015 / Revised: 14 December 2015 / Accepted: 8 January 2016 / Published: 4 February 2016
(This article belongs to the Special Issue Discrete Choice Modeling)
Full-Text   |   PDF [353 KB, uploaded 4 February 2016]   |  


We consider a model in which an outcome depends on two discrete treatment variables, where one treatment is given before the other. We formulate a three-equation triangular system with weak separability conditions. Without assuming assignment is random, we establish the identification of an average structural function using two-step matching. We also consider decomposing the effect of the first treatment into direct and indirect effects, which are shown to be identified by the proposed methodology. We allow for both of the treatment variables to be non-binary and do not appeal to an identification-at-infinity argument. View Full-Text
Keywords: nonparametric identification; discrete endogenous regressors; triangular models nonparametric identification; discrete endogenous regressors; triangular models

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Jun, S.J.; Pinkse, J.; Xu, H.; Yıldız, N. Multiple Discrete Endogenous Variables in Weakly-Separable Triangular Models. Econometrics 2016, 4, 7.

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