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A generalized missing-indicator approach to regression with imputed covariates

Author

Listed:
  • Valentino Dardanoni

    (University of Palermo)

  • Giuseppe De Luca

    () (University of Palermo)

  • Salvatore Modica

    (University of Palermo)

  • Franco Peracchi

    (Tor Vergata University)

Abstract

We consider estimation of a linear regression model using data where some covariate values are missing but imputations are available to fill in the miss- ing values. This situation generates a tradeoff between bias and precision when estimating the regression parameters of interest. Using only the subsample of complete observations does not cause bias but may imply a substantial loss of precision because the complete cases may be too few. On the other hand, filling in the missing values with imputations may cause bias. We provide the new Stata command gmi, which handles such tradeoff by using either model reduction or Bayesian model averaging techniques in the context of the generalized missing- indicator approach recently proposed by Dardanoni, Modica, and Peracchi (2011, Journal of Econometrics 162: 362–368). If multiple imputations are available, gmi can also be combined with the built-in Stata prefix mi estimate to account for extra variability due to imputation. We illustrate the use of gmi with an empirical application in the health domain, where item nonresponse is substantial. Copyright 2012 by StataCorp LP.

Suggested Citation

  • Valentino Dardanoni & Giuseppe De Luca & Salvatore Modica & Franco Peracchi, 2012. "A generalized missing-indicator approach to regression with imputed covariates," Stata Journal, StataCorp LP, vol. 12(4), pages 575-604, December.
  • Handle: RePEc:tsj:stataj:v:12:y:2012:i:4:p:575-604
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    1. Einmahl, J.H.J. & Magnus, J.R. & Kumar, K., 2011. "On the Choice of Prior in Bayesian Model Averaging," Other publications TiSEM 3ca603c9-5336-4ecb-9521-6, Tilburg University, School of Economics and Management.
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    8. 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.
    9. 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.
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    11. 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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    1. 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.
    2. Aedın Doris & Donal O’Neill & Olive Sweetman, 2011. "GMM estimation of the covariance structure of longitudinal data on earnings," Stata Journal, StataCorp LP, vol. 11(3), pages 439-459, September.
    3. 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.
    4. 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.
    5. Hartwell, Christopher A., 2016. "The institutional basis of efficiency in resource-rich countries," Economic Systems, Elsevier, vol. 40(4), pages 519-538.
    6. 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.
    7. 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.
    8. 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.

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