A generalized missing-indicator approach to regression with imputed covariates
Valentino Dardanoni
University of Palermo
Palermo, Italy
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Giuseppe De Luca
University of Palermo
Palermo, Italy
[email protected]
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Salvatore Modica
University of Palermo
Palermo, Italy
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Franco Peracchi
Tor Vergata University and EIEF
Rome, Italy
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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 missing
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.
View all articles by these authors:
Valentino Dardanoni, Giuseppe De Luca, Salvatore Modica, Franco Peracchi
View all articles with these keywords:
gmi, missing covariates, imputation, bias–precision tradeoff, model reduction, model averaging
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