Advanced Search
MyIDEAS: Login

A Generalized Missing-Indicator Approach to Regression with Imputed Covariates

Contents:

Author Info

  • Valentino Dardanoni

    (University of Palermo)

  • Giuseppe De Luca

    (ISFOL)

  • Salvatore Modica

    (University of Palermo)

  • Franco Peracchi

    (Tor Vergata University and EIEF)

Abstract

This paper considers estimation of a linear regression model using data where some covariate values are missing but imputations are available to fill-in the missing values. The availability of imputations generates a trade-off between bias and precision in the estimators of the regression parameters. The complete cases are often too few, so precision is lost, but filling-in the missing values with imputations may lead to bias. We provide the new Stata command gmi which allows handling such bias-precision trade-off using either model reduction or model averaging techniques in the context of the generalized missing-indicator approach recently proposed by Dardanoni et al.(2011). If multiple imputations are available, our gmi command can be also combined with the built-in Stata prefix mi estimate to account for the extra variability due to the imputation process. The gmi command is illustrated with an empirical application which investigates the relationship between an objective health indicator and a set of socio-demographic and economic covariates affected by substantial item nonresponse.

Download Info

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
File URL: http://www.eief.it/files/2012/09/wp-11-a-generalized-missing-indicator-approach-to-regression-with-imputed-covariates.pdf
Download Restriction: no

Bibliographic Info

Paper provided by Einaudi Institute for Economics and Finance (EIEF) in its series EIEF Working Papers Series with number 1111.

as in new window
Length: 28 pages
Date of creation: 2011
Date of revision: May 2011
Handle: RePEc:eie:wpaper:1111

Contact details of provider:
Postal: Via Sallustiana, 62 - 00187 Roma
Phone: +39 066790013
Fax: +39 0647924872
Email:
Web page: http://www.eief.it/repec
More information through EDIRC

Related research

Keywords:

Other versions of this item:

This paper has been announced in the following NEP Reports:

References

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
as in new window
  1. Horton, Nicholas J. & Kleinman, Ken P., 2007. "Much Ado About Nothing: A Comparison of Missing Data Methods and Software to Fit Incomplete Data Regression Models," The American Statistician, American Statistical Association, vol. 61, pages 79-90, February.
  2. Dimitrios Christelis, 2011. "Imputation of Missing Data in Waves 1 and 2 of SHARE," CSEF Working Papers 278, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
  3. Dardanoni, Valentino & Modica, Salvatore & Peracchi, Franco, 2011. "Regression with imputed covariates: A generalized missing-indicator approach," Journal of Econometrics, Elsevier, vol. 162(2), pages 362-368, June.
  4. Magnus, Jan R. & Powell, Owen & Prüfer, Patricia, 2010. "A comparison of two model averaging techniques with an application to growth empirics," Journal of Econometrics, Elsevier, vol. 154(2), pages 139-153, February.
  5. Agar Brugiavini & Tullio Jappelli & Guglielmo Weber, 2002. "The Survey on Health, Aging and Wealth," CSEF Working Papers 86, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
  6. Jan R. Magnus, 2002. "Estimation of the mean of a univariate normal distribution with known variance," Econometrics Journal, Royal Economic Society, vol. 5(1), pages 225-236, June.
Full references (including those not matched with items on IDEAS)

Citations

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

Cited by:
  1. Valentino Dardanoni & Giuseppe De Luca & Salvatore Modica & Franco Peracchi, 2013. "Bayesian Model Averaging for Generalized Linear Models with Missing Covariates," EIEF Working Papers Series 1311, Einaudi Institute for Economics and Finance (EIEF), revised May 2013.
  2. Giuseppe De Luca & Jan R. Magnus, 2011. "Bayesian model averaging and weighted-average least squares: Equivariance, stability, and numerical issues," Stata Journal, StataCorp LP, vol. 11(4), pages 518-544, December.

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:eie:wpaper:1111. 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: (Facundo Piguillem).

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 references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link 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 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.