A Practical Asymptotic Variance Estimator For Two-step Semiparametric Estimators

The goal of this paper is to develop techniques to simplify semiparametric inference. The authors 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. They hope that this simplicity will promote the use of semiparametric procedures.

Provided by: Yale University Topic: Big Data Date Added: May 2011 Format: PDF

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