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Book review: Forecasting

The model approach to missing value imputation begins with changing the role of the input variable with missing values to now be a target variable. The inputs to the new model are other input variables that may predict this new target variable well. The training data should be large enough, and all of the inputs must be populated; listwise deletion is an appropriate way to remove records with any missing values. Keep in mind that even a moderately accurate model can still produce good imputations; the alternatives are either random or constant imputation methods. Modelers have two problems with using models to impute missing values. First, if the software doesn’t have methods to impute values from models automatically, this kind of imputation can take considerable time and effort; one must build as many models are as there are variables in the data.