Abstract
Questions about monetary variables (such as income, wealth or savings) are key components of questionnaires on household finances. However, missing information on such sensitive topics is a well-known phenomenon which can seriously bias any inference based only on complete cases analysis. Many imputation techniques have been developed and implemented in several surveys. Using the German SAVE data, this paper evaluates different techniques for the imputation of monetary variables implementing a simulation study, where a random pattern of missingness is imposed on the observed values of the variables of interest. New estimation techniques are necessary to overcome the upward bias of monetary variables caused by the initially implemented imputation procedure. A Monte-Carlo simulation based on the observed data shows the superiority of the newly implemented smearing estimate to construct the missing data structure. All waves are consistently imputed using the new method.
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