Modelling measurement errors by object-oriented Bayesian Networks: an application to 2008 SHIW

In this paper we propose to use the object-oriented Bayesian network architecture to model measurement errors. We then apply our model to the Italian Survey on Household Income and Wealth (SHIW) 2008. Attention is focused on errors caused by the respondents. The parameters of the error model are estimated using a validation sample. The network is used to stochastically impute micro data for households. In particular imputation is performed also using an auxiliary variable. Indices are calculated to evaluate the performance of the correction procedure and show that accounting for auxiliary information improves the results. Finally, potentialities and possible extensions of the Bayesian network approach both to the measurement error context and to official statistics problems in general are discussed.