Optimal adjustments for inconsistency in imputed data

Conflicting information may arise in statistical micro data due to partial imputation, where one part of the imputed record consists of the observed values of the original record and the other of the imputed values. Edit rules that involve variables from both parts of the record will often be violated. One strategy to remedy this problem is to make adjustments to the imputations such that all constraints are simultaneously satisfied and the adjustments are, in some sense, as small as possible. The minimal adjustments are obtained by minimizing a chosen distance metric subject to the constraints and we show how different choices of the distance metric result in different adjustments to the imputed data. As an extension we also consider an approach that does not aim to minimize the adjustments but to make the adjustments as uniform as possible between variables. Under this approach, even the values that are not explicitly involved in any constraints can be adjusted. The properties and interpretations of the proposed methods are illustrated using empirical business-economic data.

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