IDEAS home Printed from https://ideas.repec.org/p/ifs/cemmap/14-18.html

Inference on a distribution from noisy draws

Author

Listed:
  • Koen Jochmans

    (Institute for Fiscal Studies and University of Cambridge)

  • Martin Weidner

    (Institute for Fiscal Studies and University College London)

Abstract

We consider a situation where a distribution is being estimated by the empirical distribution of noisy measurements. The measurements errors are allowed to be heteroskedastic and their variance may depend on the realization of the underlying random variable. We use an asymptotic embedding where the noise shrinks with the sample size to calculate the leading bias arising from the presence of noise. Conditions are obtained under which this bias is asymptotically non-negligible. Analytical and jackknife corrections for the empirical distribution are derived that recenter the limit distribution and yield con fidence intervals with correct coverage in large samples. Similar adjustments are presented for nonparametric estimators of the density and quantile function. Our approach can be connected to corrections for selection bias and shrinkage estimation. Simulation results confi rm the much improved sampling behavior of the corrected estimators. An empirical application to the estimation of a stochastic-frontier model is also provided.

Suggested Citation

  • Koen Jochmans & Martin Weidner, 2018. "Inference on a distribution from noisy draws," CeMMAP working papers CWP14/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:cemmap:14/18
    as

    Download full text from publisher

    File URL: https://www.ifs.org.uk/uploads/CWP141818.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Okui, Ryo & Yanagi, Takahide, 2019. "Panel data analysis with heterogeneous dynamics," Journal of Econometrics, Elsevier, vol. 212(2), pages 451-475.
    2. Ryo Okui & Takahide Yanagi, 2020. "Kernel estimation for panel data with heterogeneous dynamics," The Econometrics Journal, Royal Economic Society, vol. 23(1), pages 156-175.
    3. Gobillon, Laurent & Magnac, Thierry & Roux, Sébastien, 2022. "Lifecycle Wages and Human Capital Investments: Selection and Missing Data," TSE Working Papers 22-1299, Toulouse School of Economics (TSE).
    4. Magnac, Thierry & Roux, Sébastien, 2021. "Heterogeneity and wage inequalities over the life cycle," European Economic Review, Elsevier, vol. 134(C).
    5. Kutta, Tim & Dette, Holger, 2024. "Validating approximate slope homogeneity in large panels," Journal of Econometrics, Elsevier, vol. 246(1).
    6. Laurent Barras & Patrick Gagliardini & Olivier Scaillet, 2022. "Skill, Scale, and Value Creation in the Mutual Fund Industry," Journal of Finance, American Finance Association, vol. 77(1), pages 601-638, February.
    7. St'ephane Bonhomme & Martin Weidner, 2019. "Posterior Average Effects," Papers 1906.06360, arXiv.org, revised Sep 2021.

    More about this item

    Keywords

    ;
    ;
    ;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ifs:cemmap:14/18. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Emma Hyman (email available below). General contact details of provider: https://edirc.repec.org/data/cmifsuk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.