Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/97412
Authors: 
Kaido, Hirokai
Year of Publication: 
2014
Series/Report no.: 
cemmap working paper, Centre for Microdata Methods and Practice CWP03/14
Abstract: 
This paper studies the identification and estimation of weighted average derivatives of conditional location functionals including conditional mean and conditional quantiles in settings where either the outcome variable or a regressor is interval-valued. Building on Manski and Tamer (2002) who study nonparametric bounds for mean regression with interval data, we characterize the identified set of weighted average derivatives of regression functions. Since the weighted average derivatives do not rely on parametric specifications for the regression functions, the identified set is well-defined without any parametric assumptions. Under general conditions, the identified set is compact and convex and hence admits characterization by its support function. Using this characterization, we derive the semiparametric efficiency bound of the support function when the outcome variable is interval-valued. We illustrate efficient estimation by constructing an efficient estimator of the support function for the case of mean regression with an interval censored outcome.
Subjects: 
Partial Identification
Weighted Average Derivative
Semiparametric Efficiency
Support Function
Interval Data
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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