Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/149772
Authors: 
Masten, Matthew A.
Poirier, Alexandre
Year of Publication: 
2016
Series/Report no.: 
cemmap working paper, Centre for Microdata Methods and Practice CWP26/16
Abstract: 
We analyze identification of nonseparable models under three kinds of exogeneity assumptions weaker than full statistical independence. The first is based on quantile independence. Selection on unobservables drives deviations from full independence. We show that such deviations based on quantile independence require non-monotonic and oscillatory propensity scores. Our second and third approaches are based on a distance-from-independence metric, using either a conditional cdf or a propensity score. Under all three approaches we obtain simple analytical characterizations of identified sets for various parameters of interest. We do this in three models: the exogenous regressor model of Matzkin (2003), the instrumental variable model of Chernozhukov and Hansen (2005), and the binary choice model with nonparametric latent utility of Matzkin (1992).
Subjects: 
Nonparametric Identification
Partial Identification
Sensitivity Analysis
Nonseparable Models
Selection on Unobservables
Instrumental Variables
Binary Choice
JEL: 
C14
C21
C25
C26
C51
Document Type: 
Working Paper

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