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A Generalized Missing-Indicator Approach to Regression with Imputed Covariates

Author

Listed:
  • 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.

Suggested Citation

  • Valentino Dardanoni & Giuseppe De Luca & Salvatore Modica & Franco Peracchi, 2011. "A Generalized Missing-Indicator Approach to Regression with Imputed Covariates," EIEF Working Papers Series 1111, Einaudi Institute for Economics and Finance (EIEF), revised May 2011.
  • Handle: RePEc:eie:wpaper:1111
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. repec:ags:stataj:163399 is not listed on IDEAS
    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. 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. 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.
    7. 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.
    8. Charles Lindsey & Simon Sheather, 2010. "Variable selection in linear regression," Stata Journal, StataCorp LP, vol. 10(4), pages 650-669, December.
    9. De Luca, Giuseppe & Magnus, Jan R., 2011. "Bayesian model averaging and weighted-average least squares: Equivariance, stability, and numerical issues," Stata Journal, StataCorp LP, vol. 0(Number 4), pages 1-29.
    10. Magnus, Jan R. & Wan, Alan T.K. & Zhang, Xinyu, 2011. "Weighted average least squares estimation with nonspherical disturbances and an application to the Hong Kong housing market," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1331-1341, March.
    11. Jan R. Magnus & Giuseppe De Luca, 2016. "Weighted-Average Least Squares (Wals): A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 30(1), pages 117-148, February.
    12. repec:hal:journl:peer-00815561 is not listed on IDEAS
    13. 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.
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    Cited by:

    1. Laszlo Goerke & Sabrina Jeworrek & Markus Pannenberg, 2015. "Trade union membership and paid vacation in Germany," IZA Journal of Labor Economics, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 4(1), pages 1-26, December.
    2. Djavad Salehi-Isfahani & Nadia Hassine & Ragui Assaad, 2014. "Equality of opportunity in educational achievement in the Middle East and North Africa," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 12(4), pages 489-515, December.
    3. De Luca, Giuseppe & Magnus, Jan R., 2011. "Bayesian model averaging and weighted-average least squares: Equivariance, stability, and numerical issues," Stata Journal, StataCorp LP, vol. 0(Number 4), pages 1-29.
    4. Doris, Aedín & O'Neill, Donal & Sweetman, Olive, 2011. "GMM estimation of the covariance structure of longitudinal data on earnings," Stata Journal, StataCorp LP, vol. 0(Number 3), pages 1-23.
    5. Christopher Hartwell, 2015. "Après le déluge: Institutions, the Global Financial Crisis, and Bank Profitability in Transition," Open Economies Review, Springer, vol. 26(3), pages 497-524, July.
    6. Hartwell, Christopher A., 2016. "The institutional basis of efficiency in resource-rich countries," Economic Systems, Elsevier, vol. 40(4), pages 519-538.
    7. Dardanoni, Valentino & De Luca, Giuseppe & Modica, Salvatore & Peracchi, Franco, 2015. "Model averaging estimation of generalized linear models with imputed covariates," Journal of Econometrics, Elsevier, vol. 184(2), pages 452-463.
    8. 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.

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