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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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    Cited by:

    1. Victor Chernozhukov & Iván Fernández‐Val & Blaise Melly, 2013. "Inference on Counterfactual Distributions," Econometrica, Econometric Society, pages 2205-2268.
    2. Victor Aguirregabiria & Arvind Magesan, 2013. "Euler Equations for the Estimation of Dynamic Discrete Choice Structural Models," Working Papers tecipa-489, University of Toronto, Department of Economics.
    3. Victor Aguiar & Roberto Serrano, 2015. "Slutsky Matrix Norms and Revealed Preference Tests of Consumer Behaviour," Working Papers 2015-1, Brown University, Department of Economics.
    4. Victor Aguirregabiria & Arvind Magesan, "undated". "Soultion and Estimation of Dynamic Discrete Choice Structural Models Using Euler Equations," Working Papers 2016-32, Department of Economics, University of Calgary, revised 24 May 2016.
    5. Sokbae Lee & Myung Hwan Seo & Youngki Shin, 2016. "The lasso for high dimensional regression with a possible change point," Journal of the Royal Statistical Society Series B, Royal Statistical Society, pages 193-210.
    6. Daniel Ackerberg & Xiaohong Chen & Jinyong Hahn & Zhipeng Liao, 2014. "Asymptotic Efficiency of Semiparametric Two-step GMM," Review of Economic Studies, Oxford University Press, pages 919-943.
    7. Aguirregabiria, Victor & Magesan, Arvind, 2013. "Euler Equations for the Estimation of Dynamic Discrete Choice Structural," MPRA Paper 46056, University Library of Munich, Germany.
    8. Hong, Han & Mahajan, Aprajit & Nekipelov, Denis, 2015. "Extremum estimation and numerical derivatives," Journal of Econometrics, Elsevier, vol. 188(1), pages 250-263.
    9. Xiaohong Chen & Yin Jia Qiu, 2016. "Methods for Nonparametric and Semiparametric Regressions with Endogeneity: a Gentle Guide," Cowles Foundation Discussion Papers 2032, Cowles Foundation for Research in Economics, Yale University.
    10. repec:eee:csdana:v:113:y:2017:i:c:p:53-63 is not listed on IDEAS
    11. Maican, Florin & Orth, Matilda, 2015. "A dynamic analysis of entry regulations and productivity in retail trade," International Journal of Industrial Organization, Elsevier, vol. 40(C), pages 67-80.
    12. Chen, Xiaohong & Liao, Zhipeng, 2015. "Sieve semiparametric two-step GMM under weak dependence," Journal of Econometrics, Elsevier, vol. 189(1), pages 163-186.
    13. Le‐Yu Chen & Sokbae Lee & Myung Jae Sung, 2014. "Maximum score estimation with nonparametrically generated regressors," Econometrics Journal, Royal Economic Society, vol. 17(3), pages 271-300, October.
    14. Wu, Ximing & Sickles, Robin, 2014. "Semiparametric Estimation under Shape Constraints," Working Papers 15-021, Rice University, Department of Economics.
    15. Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey, 2016. "Locally robust semiparametric estimation," CeMMAP working papers CWP31/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    16. Gupta, A, 2015. "Nonparametric specification testing via the trinity of tests," Economics Discussion Papers 15619, University of Essex, Department of Economics.
    17. Armstrong, Timothy B. & Bertanha, Marinho & Hong, Han, 2014. "A fast resample method for parametric and semiparametric models," Journal of Econometrics, Elsevier, vol. 179(2), pages 128-133.
    18. Abadir, Karim M. & Distaso, Walter & Žikeš, Filip, 2014. "Design-free estimation of variance matrices," Journal of Econometrics, Elsevier, vol. 181(2), pages 165-180.
    19. repec:esx:essedp:774 is not listed on IDEAS
    20. Joachim Freyberger & Bradley J. Larsen, 2017. "Identification in Ascending Auctions, with an Application to Digital Rights Management," NBER Working Papers 23569, National Bureau of Economic Research, Inc.
    21. Maican, Florin G., 2012. "From Boom to Bust and Back Again: A dynamic analysis of IT services," Working Papers in Economics 543, University of Gothenburg, Department of Economics.

    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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