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

  • Michael Ziegelmeyer


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. Copyright Springer-Verlag 2013

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Article provided by Springer in its journal AStA Advances in Statistical Analysis.

Volume (Year): 97 (2013)
Issue (Month): 1 (January)
Pages: 49-76

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Handle: RePEc:spr:alstar:v:97:y:2013:i:1:p:49-76
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  1. Manning, Willard G. & Mullahy, John, 2001. "Estimating log models: to transform or not to transform?," Journal of Health Economics, Elsevier, vol. 20(4), pages 461-494, July.
  2. Essig, Lothar & Winter, Joachim, 2009. "Item non-response to financial questions in household surveys: An experimental study of interviewer and mode effects," Munich Reprints in Economics 20547, University of Munich, Department of Economics.
  3. Joachim R. Frick & Markus M. Grabka, 2007. "Item Non-response and Imputation of Annual Labor Income in Panel Surveys from a Cross-National Perspective," SOEPpapers on Multidisciplinary Panel Data Research 49, DIW Berlin, The German Socio-Economic Panel (SOEP).
  4. John Mullahy, 1998. "Much Ado About Two: Reconsidering Retransformation and the Two-Part Model in Health Economics," NBER Technical Working Papers 0228, National Bureau of Economic Research, Inc.
  5. Daniel Schunk, 2007. "A Markov Chain Monte Carlo Multiple Imputation Procedure for Dealing with Item Nonresponse in the German SAVE Survey," MEA discussion paper series 07121, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
  6. Wasito, Ito & Mirkin, Boris, 2006. "Nearest neighbours in least-squares data imputation algorithms with different missing patterns," Computational Statistics & Data Analysis, Elsevier, vol. 50(4), pages 926-949, February.
  7. Michael Ziegelmeyer, 2009. "Documentation of the logical imputation using the panel structure of the 2003-2008 German SAVE Survey," MEA discussion paper series 09173, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
  8. Bello, A. L., 1995. "Imputation techniques in regression analysis: Looking closely at their implementation," Computational Statistics & Data Analysis, Elsevier, vol. 20(1), pages 45-57, July.
  9. Manning, Willard G., 1998. "The logged dependent variable, heteroscedasticity, and the retransformation problem," Journal of Health Economics, Elsevier, vol. 17(3), pages 283-295, June.
  10. Mullahy, John, 1998. "Much ado about two: reconsidering retransformation and the two-part model in health econometrics," Journal of Health Economics, Elsevier, vol. 17(3), pages 247-281, June.
  11. Patrick Royston, 2004. "Multiple imputation of missing values," Stata Journal, StataCorp LP, vol. 4(3), pages 227-241, September.
  12. repec:ese:iserwp:2004-19 is not listed on IDEAS
  13. Daniel Schunk, 2008. "A Markov chain Monte Carlo algorithm for multiple imputation in large surveys," AStA Advances in Statistical Analysis, Springer, vol. 92(1), pages 101-114, February.
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