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Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models

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  • Ying-Ying Lee
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    Abstract

    Partial mean processes with generated regressors arise in several important econometric problems, such as the distribution of potential outcomes with continuous treatments and the quantile structural function in a nonseparable triangular model.� This paper proposes a fully nonparametric estimator for the partial mean process, where the second step consists of a kernel regression on regressors that are estimated in the first step.� The main contribution is a uniform expansion that characterizes in detail how the estimation error associated with the generated regressor affects the limiting distribution of the marginal integration estimator.� The general results are illustrated with three examples: control variables in triangular models (Newey, Powell, and Vella, 1999; Imbens and Newey, 2009), the generalized propensity score for a continuus treatment (Hirano and Imbens, 2004), and the propensity score for sample selection (Das, Newey, and Vella, 2003).

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    Paper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 706.

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    Date of creation: 13 May 2014
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    Handle: RePEc:oxf:wpaper:706

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    Keywords: Continuous treatment; partial means; nonseparable models; generated regressors; control function;

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