A generalized missing-indicator approach to regression with imputed covariates
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.
Volume (Year): 12 (2012)
Issue (Month): 4 (December)
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- Valentino Dardanoni & Salvatore Modica & Franco Peracchi, 2009.
"Regression with Imputed Covariates:a Generalized Missing Indicator Approach,"
CEIS Research Paper
150, Tor Vergata University, CEIS, revised 08 Oct 2009.
- 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.
- Valentino Dardanoni & Salvatore Modica & Franco Peracchi, 2011. "Regression with imputed covariates: A generalized missing-indicator approach," Post-Print hal-00815561, HAL.
- Valentino Dardanoni & Salvatore Modica & Franco Peracchi, 2011. "Regression with imputed covariates: A generalized missing-indicator approach," Post-Print peer-00815561, HAL.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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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