Calibrated hot deck imputation for numerical data under edit restrictions

We develop a non-parametric imputation method for item non-response based on the well-known hot-deck approach. The proposed imputation method is developed for imputing numerical data that ensure that all record-level edit rules are satisfied and previously estimated or known totals are exactly preserved. We propose a sequential hot-deck imputation approach that takes into account survey weights. Original survey weights are not changed, rather the imputations themselves are calibrated so that weighted estimates will equal known or estimated population totals. Edit rules are preserved by integrating the sequential hot-deck imputation with Fourier-Motzkin elimination which defines the range of feasible values that can be used for imputation such that all record-level edits will be satisfied. We apply the proposed imputation method under different scenarios of random and nearest-neighbour hot-deck on two data sets: an annual structural business survey and a synthetically generated data set with a large proportion of missing data. We compare the proposed imputation methods to standard imputation methods based on a set of evaluation measures.