Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/97404
Authors: 
D'Haultfoeuille, Xavier
Hoderlein, Stefan
Sasaki, Yuya
Year of Publication: 
2013
Series/Report no.: 
cemmap working paper, Centre for Microdata Methods and Practice CWP40/13
Abstract: 
This paper studies the identification of nonseparable models with continuous, endogenous regressors, also called treatments, using repeated cross sections. We show that several treatment effect parameters are identified under two assumptions on the effect of time, namely a weak stationarity condition on the distribution of unobservables, and time variation in the distribution of endogenous regressors. Other treatment effect parameters are set identified under curvature conditions, but without any function form restrictions. This result is related to the difference-in-differences idea, but imposes neither additive time effects nor exogenously defined control groups. Furthermore, we investigate two extrapolation strategies that allow us to point identify the entire model: using monotonicity of the error term, or imposing a linear correlated random coefficient structure. Finally, we illustrate our results by studying the effect of mother's age on infants' birth weight.
Subjects: 
identification
repeated cross sections
nonlinear models
continuous treatment
random coefficients
endogeneity
difference-in-differences
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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