1. A computer-implemented method for multiple imputation for retail data sets with missing data values, the method comprising: receiving an original data set including values including a plurality of products, a plurality of stores or chains in which each said product is sold, and a plurality of time-periods indicating when said products were sold; identifying and encoding the missing data values in the original data set with dummy indicator variables corresponding to specific product, store and time-period combinations; obtaining a joint probability distribution for the magnitudes of the missing data values in the original data set; generating a plurality of complete data sets corresponding to the original data set, wherein each complete data set in said plurality of complete data sets corresponds to the original data set with its non-missing values intact, and replacing, in each of the complete data sets, missing values indicated by said dummy variables with a sampled set of values from the joint probability distribution for the magnitudes of the missing elements as obtained, wherein a programmed processor device performs one or more of one or more the receiving, identifying and encoding, obtaining, generating and replacing.