Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models
59 Pages Posted: 31 Oct 2018
Date Written: September 16, 2018
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: Suggested Citation