IDEAS home Printed from https://ideas.repec.org/p/cdl/uctcwp/qt5rv0265r.html
   My bibliography  Save this paper

Multiple Imputations for Linear Regression Models

Author

Listed:
  • Brownstone, David

Abstract

Rubin (1987) has proposed multiple imputations as a general method for estimation ion the presence of missing data. Rubin's results only strictly apply to Bayesian models, but Schenker and Welsh (1988) directly prove the consistency of multiple imputations inferences when there are missing values of the dependent variable in linear regression models. This paper extends and modifies Schenker and Welsh's theorems to give conditions where multiple imputations yield consistent inferences for both ignorable and nonignorable missing data in exogenous variables. One key condition is that the imputed values must have the same conditional first and second moments as the true values. Monte Carlo studies show that the multiple imputation covariance estimates are accurate for realistic sample sizes. They also support the applications of multiple imputations in Brownstone and Valletta (1991), where the multiple imputations estimates substantially changed the qualitative conclusions implied by the model.

Suggested Citation

  • Brownstone, David, 1991. "Multiple Imputations for Linear Regression Models," University of California Transportation Center, Working Papers qt5rv0265r, University of California Transportation Center.
    • Handle: RePEc:cdl:uctcwp:qt5rv0265r
    as

    Download full text from publisher

    File URL: https://www.escholarship.org/uc/item/5rv0265r.pdf;origin=repeccitec
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Kaestner, Robert & Joyce, Theodore & Wehbeh, Hassan, 1996. "The Effect of Maternal Drug Use on Birth Weight: Measurement Error in Binary Variables," Economic Inquiry, Western Economic Association International, vol. 34(4), pages 617-629, October.
    2. Guevara, C. Angelo & Tang, Yue & Gao, Song, 2018. "The initial condition problem with complete history dependency in learning models for travel choices," Transportation Research Part B: Methodological, Elsevier, vol. 117(PB), pages 850-861.
    3. Brownstone, David & Velletta, Robert G., 1996. "Modeling Earnings Measurement Error: A Multiple Imputation Approach," University of California Transportation Center, Working Papers qt2t08s22q, University of California Transportation Center.
    4. Brownstone, David, 1997. "Multiple Imputation Methodology for Missing Data, Non-Random Response, and Panel Attrition," University of California Transportation Center, Working Papers qt2zd6w6hh, University of California Transportation Center.
    5. Artés, Joaquín, 2009. "Long-run versus short-run decisions: R&D and market structure in Spanish firms," Research Policy, Elsevier, vol. 38(1), pages 120-132, February.
    6. Brownstone, David & Valletta, Robert G, 1996. "Modeling Earnings Measurement Error: A Multiple Imputation Approach," University of California Transportation Center, Working Papers qt3gb0k9b5, University of California Transportation Center.
    7. Brownstone, David & Valletta, Robert G, 1996. "Modeling Earnings Measurement Error: A Multiple Imputation Approach," The Review of Economics and Statistics, MIT Press, vol. 78(4), pages 705-717, November.

    More about this item

    Keywords

    Social and Behavioral Sciences;

    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:cdl:uctcwp:qt5rv0265r. 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: Lisa Schiff (email available below). General contact details of provider: https://edirc.repec.org/data/itucbus.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.