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A Generalized Missing-Indicator Approach to Regression with Imputed Covariates

  • Valentino Dardanoni

    (University of Palermo)

  • Giuseppe De Luca


  • Salvatore Modica

    (University of Palermo)

  • Franco Peracchi

    (Tor Vergata University and EIEF)

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.

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

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Length: 28 pages
Date of creation: 2011
Date of revision: May 2011
Handle: RePEc:eie:wpaper:1111
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  1. Charles Lindsey & Simon Sheather, 2010. "Variable selection in linear regression," Stata Journal, StataCorp LP, vol. 10(4), pages 650-669, December.
  2. Einmahl, J.H.J. & Magnus, J.R. & Kumar, K., 2011. "On the Choice of Prior in Bayesian Model Averaging," Discussion Paper 2011-003, Tilburg University, Center for Economic Research.
  3. Valentino Dardanoni & Salvatore Modica & Franco Peracchi, 2011. "Regression with imputed covariates: A generalized missing-indicator approach," Post-Print hal-00815561, HAL.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
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