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AStA Advances in Statistical Analysis

, Volume 97, Issue 1, pp 49–76 | Cite as

Illuminate the unknown: evaluation of imputation procedures based on the SAVE survey

  • Michael Ziegelmeyer
Original Paper

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-case analysis. Many imputation techniques have been developed and implemented in several surveys. Using the German SAVE data, a new estimation technique is necessary to overcome the upward bias of monetary variables caused by the initially implemented imputation procedure. The upward bias is the result of adding random draws to the implausible negative values predicted by OLS regressions until all values are positive. To overcome this problem the logarithm of the dependent variable is taken and the predicted values are retransformed to the original scale by Duan’s smearing estimate. This paper evaluates the two 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. 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.

Keywords

Imputation methods Monte-Carlo simulation Imputation evaluation Item-nonresponse Missing data Imputation Retransformation Sample surveys SAVE 

JEL Classification

C01 C81 C49 

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Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.Economics and Research DepartmentBanque centrale du Luxembourg (BCL), Munich Center for the Economics of Aging (MEA)LuxembourgLuxembourg

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