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A Practical Asymptotic Variance Estimator for Two-Step Semiparametric Estimators

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Listed:
  • Daniel Ackerberg

    (University of Michigan)

  • Xiaohong Chen

    (Yale University)

  • Jinyong Hahn

    (UCLA)

Abstract

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 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. © 2012 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.

Suggested Citation

  • Daniel Ackerberg & Xiaohong Chen & Jinyong Hahn, 2012. "A Practical Asymptotic Variance Estimator for Two-Step Semiparametric Estimators," The Review of Economics and Statistics, MIT Press, vol. 94(2), pages 481-498, May.
  • Handle: RePEc:tpr:restat:v:94:y:2012:i:2:p:481-498
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    More about this item

    Keywords

    semiparametric inference;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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