Object-Oriented Bayesian Network to Deal with Measurement Error in Household Surveys

In this paper we propose to use the object-oriented Bayesian networks (OOBNs) architecture to model measurement errors in the Italian survey on household income and wealth (SHIW) 2008 when the variable of interest is categorical. The network is used to stochastically impute microdata for households. Imputation is performed both assuming a misreport probability constant over all the population and learning a Bayesian network for estimating such a probability. Finally, potentialities and possible extensions of this approach are discussed.