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

59 Pages Posted: 31 Oct 2018

See all articles by Ying-Ying Lee

Ying-Ying Lee

University of California, Irvine - Department of Economics

Date Written: September 16, 2018

Abstract

Partial mean with generated regressors arises in several 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 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 two examples: the generalized propensity score for a continuous treatment (Hirano and Imbens, 2004) and control variables in triangular models (Newey, Powell, and Vella, 1999; Imbens and Newey, 2009). An empirical application to the Job Corps program evaluation demonstrates the usefulness of the method.

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

JEL Classification: C13, C14, C31

Suggested Citation

Lee, Ying-Ying, Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models (September 16, 2018). Available at SSRN: https://ssrn.com/abstract=3250485 or http://dx.doi.org/10.2139/ssrn.3250485

Ying-Ying Lee (Contact Author)

University of California, Irvine - Department of Economics ( email )

3151 Social Science Plaza
Irvine, CA 92697-5100
United States

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