Measurement error correction by non parametric Bayesian networks: application and evaluation

In this paper a procedure for measurement error correction based on nonparametric Bayesian networks is proposed. The performance of the proposed method is evaluated using a validation sample collected by Banca d’Italia and a major Italian bank group to investigate the measurement error mechanism in the main financial variables amounts observed in the Banca d’Italia survey on Household Income andWealth. Specifically, in this paper attention is focused on the bond amounts. By means of Uninet’s programmatic engine working directly from R, data can be corrected unit by unit by sampling from the nonparametric Bayesian network. Thanks to the validation sample, the distances between the true and the imputed values are computed and the procedure is evaluated.