Partial Independence in Nonseparable Models
86 Pages Posted: 20 Jun 2016
Date Written: June 17, 2016
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).
Keywords: Nonparametric Identification, Partial Identification, Sensitivity Analysis, Nonseparable Models, Selection on Unobservables, Instrumental Variables, Binary Choice
JEL Classification: C14, C21, C25, C26, C51
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