Testing for monotonicity in unobservables under unconfoundedness
Halbert White and
Thomas Tao Yang
Journal of Econometrics, 2016, vol. 193, issue 1, 183-202
Monotonicity in a scalar unobservable is a common assumption when modeling heterogeneity in structural models. Among other things, it allows one to recover the underlying structural function from certain conditional quantiles of observables. Nevertheless, monotonicity is a strong assumption and in some economic applications unlikely to hold, e.g., random coefficient models. Its failure can have substantive adverse consequences, in particular inconsistency of any estimator that is based on it. Having a test for this hypothesis is hence desirable. This paper provides such a test for cross-section data. We show how to exploit an exclusion restriction together with a conditional independence assumption, which in the binary treatment literature is commonly called unconfoundedness, to construct a test. Our statistic is asymptotically normal under local alternatives and consistent against global alternatives. Monte Carlo experiments show that a suitable bootstrap procedure yields tests with reasonable level behavior and useful power. We apply our test to study the role of unobserved ability in determining Black–White wage differences and to study whether Engel curves are monotonically driven by a scalar unobservable.
Keywords: Control variables; Conditional exogeneity; Endogenous variables; Monotonicity; Nonparametrics; Nonseparable; Specification test; Unobserved heterogeneity (search for similar items in EconPapers)
JEL-codes: C12 C14 C21 C26 (search for similar items in EconPapers)
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Working Paper: Testing for Monotonicity in Unobservables under Unconfoundedness (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:193:y:2016:i:1:p:183-202
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