Model averaging estimation of generalized linear models with imputed covariates

Valentino Dardanoni (), Giuseppe De Luca (), Salvatore Modica and Franco Peracchi

Journal of Econometrics, 2015, vol. 184, issue 2, 452-463

Abstract: We address the problem of estimating generalized linear models when some covariate values are missing but imputations are available to fill-in the missing values. This situation generates a bias-precision trade-off in the estimation of the model parameters. Extending the generalized missing-indicator method proposed by Dardanoni et al. (2011) for linear regression, we handle this trade-off as a problem of model uncertainty using Bayesian averaging of classical maximum likelihood estimators (BAML). We also propose a block model averaging strategy that incorporates information on the missing-data patterns and is computationally simple. An empirical application illustrates our approach.

Keywords: Model averaging; Bayesian averaging of maximum likelihood estimators; Generalized linear models; Missing covariates; Generalized missing-indicator method; SHARE (search for similar items in EconPapers)
JEL-codes: C11 C25 C35 C81 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:184:y:2015:i:2:p:452-463

DOI: 10.1016/j.jeconom.2014.06.002

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