Asymptotic Variance Estimator for Two-Step Semiparametric Estimators
Daniel A. Ackerberg, Xiaohong Chen, Jinyong Hahn
Abstract
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Daniel A. Ackerberg, Xiaohong Chen, Jinyong Hahn
Abstract
Open-access reader
The goal of this paper is to develop techniques to simplify semiparametric inference. We do this by deriving a number of numerical equivalence results. These illustrate that in many cases, one can obtain estimates of semiparametric variances using standard formulas derived in the already-well-known parametric literature. This means that for computational purposes, an empirical researcher can ignore the semiparametric nature of the problem and do all calculations "as if" it were a parametric situation. We hope that this simplicity will promote the use of semiparametric procedures.
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The goal of this paper is to develop techniques to simplify semiparametric inference. We do this by deriving a number of numerical equivalence results. These illustrate that in many cases, one can obtain estimates of semiparametric variances using standard formulas derived in the already-well-known parametric literature. This means that for computational purposes, an empirical researcher can ignore the semiparametric nature of the problem and do all calculations "as if" it were a parametric situation. We hope that this simplicity will promote the use of semiparametric procedures.
Key concepts: Semiparametric regression, Semiparametric model, Estimator, Parametric statistics, Inference, Econometrics, Equivalence (formal languages), Variance (accounting)