ASYMPTOTICALLY EFFICIENT ESTIMATION OF WEIGHTED AVERAGE DERIVATIVES WITH AN INTERVAL CENSORED VARIABLE

Hiroaki Kaido

Econometric Theory, 2017, vol. 33, issue 5, 1218-1241

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, Econometrica 70(2), 519–546) 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 functional-form 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. Using mean regression as an example, we further demonstrate that the support function can be estimated in a regular manner by a computationally simple estimator and that the efficiency bound can be achieved.

Date: 2017
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Related works:
Working Paper: Asymptotically efficient estimation of weighted average derivatives with an interval censored variable (2014)
Working Paper: Asymptotically efficient estimation of weighted average derivatives with an interval censored variable (2014)
Working Paper: Asymptotically Efficient Estimation of Weighted Average Derivatives with an Inverval Censored Variable (2013)
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