We study the asymptotic distribution of three-step estimators of a finite-dimensional parameter vector where the second step consists of one or more nonparametric regressions on a regressor that is estimated in the first step. The first-step estimator is either parametric or nonparametric. Using Newey's (1994) path-derivative method, we derive the contribution of the first-step estimator to the influence function. In this derivation, it is important to account for the dual role that the first-step estimator plays in the second-step nonparametric regression, that is, that of conditioning variable and that of argument.
Authors
Geert Ridder
Jinyong Hahn
Journal article details
- DOI
- 10.3982/ECTA9609
- Publisher
- Wiley Online Library
- Issue
- Volume 81, Issue 1, January 2013
Suggested citation
Hahn, J and Ridder, G. (2013). 'The asymptotic variance of semi-parametric estimators with generated regressors' 81(1/2013)
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