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dc.contributor.authorHahn, Jinyongen_US
dc.contributor.authorRidder, Geerten_US
dc.description.abstractWe 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 non-parametric. 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 non-parametric regression, i.e., that of conditioning variable and that of argument. We consider three examples in more detail: the partial linear regression model estimator with a generated regressor, the Heckman, Ichimura and Todd (1998) estimator of the Average Treatment Effect and a semi-parametric control variable estimator.en_US
dc.publisher|aCentre for Microdata Methods and Practice (cemmap) |cLondonen_US
dc.relation.ispartofseries|acemmap working paper |xCWP23/10en_US
dc.subject.keywordsemi-parametric estimationen_US
dc.subject.keywordgenerated regressorsen_US
dc.subject.keywordasymptotic varianceen_US
dc.titleThe asymptotic variance of semi-parametric estimators with generated regressorsen_US
dc.typeWorking Paperen_US

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