Model averaging estimation of generalized linear models with imputed covariates

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10 Citazioni (Scopus)


We address the problem of estimating generalized linear models when some covariate values are missingbut imputations are available to fill-in the missing values. This situation generates a bias-precision tradeoffin the estimation of the model parameters. Extending the generalized missing-indicator methodproposed by Dardanoni et al. (2011) for linear regression, we handle this trade-off as a problem ofmodel uncertainty using Bayesian averaging of classical maximum likelihood estimators (BAML). We alsopropose a block model averaging strategy that incorporates information on the missing-data patterns andis computationally simple. An empirical application illustrates our approach.
Lingua originaleEnglish
pagine (da-a)452-463
Numero di pagine12
RivistaJournal of Econometrics
Stato di pubblicazionePublished - 2015

All Science Journal Classification (ASJC) codes

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