IDEAS home Printed from https://ideas.repec.org/a/cuf/journl/y2005v6i2p289-301.html
   My bibliography  Save this article

Estimation of Parameters in Multiple Regression with Missing Covariates Using a Modified First Order Regression Procedure

Author

Listed:
  • H. Toutenburg

    (Institute of Statistics University of Munich)

  • V.K. Srivastava

    (Department of Statistics, Lucknow University)

  • Shalabh

    (Department of Mathematics and Statistics, Indian Institute of Technology)

  • C. Heumann

    (Institute of Statistics, University of Munich)

Abstract

This paper considers the estimation of coefficients in a linear regression model with missing observations in the independent variables and introduces a modification of the standard first order regression method for imputation of missing values. The modification provides stochastic values for imputation. Asymptotic properties of the estimators for the regression coefficients arising from the proposed modification are derived when either both the number of complete observations and the number of missing values grow large or only the number of complete observations grows large and the number of missing observations stays fixed. Using these results, the proposed procedure is compared with two popular procedures¡ªone which utilizes only the complete observations and the other which employs the standard first order regression imputation method for missing values. It is suggested that an elaborate simulation experiment will be helpful to evaluate the gain in efficiency especially in case of discrete regressor variables and to examine some other interesting issues like the impact of varying degree of multicollinearity in explanatory variables. Applications to some concrete data sets may also shed some light on these aspects. Some work on these lines is in progress and will be reported in a future article to follow.

Suggested Citation

  • H. Toutenburg & V.K. Srivastava & Shalabh & C. Heumann, 2005. "Estimation of Parameters in Multiple Regression with Missing Covariates Using a Modified First Order Regression Procedure," Annals of Economics and Finance, Society for AEF, vol. 6(2), pages 289-301, November.
  • Handle: RePEc:cuf:journl:y:2005:v:6:i:2:p:289-301
    as

    Download full text from publisher

    File URL: http://www.aeconf.net/Articles/Nov2005/aef060205.pdf
    Download Restriction: no

    File URL: http://down.aefweb.net/AefArticles/aef060205.pdf
    Download Restriction: no

    References listed on IDEAS

    as
    1. Dagenais, Marcel G., 1973. "The use of incomplete observations in multiple regression analysis : A generalized least squares approach," Journal of Econometrics, Elsevier, vol. 1(4), pages 317-328, December.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Missing data; Regression model; Least squares estimator;

    JEL classification:

    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General

    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:cuf:journl:y:2005:v:6:i:2:p:289-301. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Qiang Gao). General contact details of provider: http://edirc.repec.org/data/emcufcn.html .

    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.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.