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

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  • Michael Ziegelmeyer

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. Copyright Springer-Verlag 2013

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  • Michael Ziegelmeyer, 2013. "Illuminate the unknown: evaluation of imputation procedures based on the SAVE survey," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(1), pages 49-76, January.
    • Handle: RePEc:spr:alstar:v:97:y:2013:i:1:p:49-76
    DOI: 10.1007/s10182-012-0197-2
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    1. Schunk, Daniel, 2007. "A Markov Chain Monte Carlo multiple imputation procedure for dealing with item nonresponse in the German SAVE survey," Papers 07-06, Sonderforschungsbreich 504.
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    5. Patrick Royston, 2004. "Multiple imputation of missing values," Stata Journal, StataCorp LP, vol. 4(3), pages 227-241, September.
    6. Frick, Joachim R. & Grabka, Markus M., 2007. "Item Non-Response and Imputation of Annual Labor Income in Panel Surveys from a Cross-National Perspective," IZA Discussion Papers 3043, Institute of Labor Economics (IZA).
    7. Nicoletti, Cheti & Peracchi, Franco, 2004. "The effects of income imputation on micro analyses: evidence from the ECHP," ISER Working Paper Series 2004-19, Institute for Social and Economic Research.
    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. Susanne Rässler & Regina Riphahn, 2006. "Survey item nonresponse and its treatment," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(1), pages 217-232, March.
    10. Ziegelmeyer, Michael, 2009. "Documentation of the logical imputation using the panel structure of the 2003-2008 German SAVE Survey," Papers 08-41, Sonderforschungsbreich 504.
    11. Daniel Schunk, 2008. "A Markov chain Monte Carlo algorithm for multiple imputation in large surveys," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 92(1), pages 101-114, February.
    12. Cristina Barceló, 2006. "Imputation of the 2002 wave of the Spanish survey of household finances (EFF)," Occasional Papers 0603, Banco de España.
    13. 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.
    14. 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.
    15. 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.
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    Cited by:

    1. Bucher-Koenen, Tabea & Lamla, Bettina, 2014. "The long Shadow of Socialism: On East-West German Differences in Financial Literacy," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100585, Verein für Socialpolitik / German Economic Association.
    2. Necker, Sarah & Ziegelmeyer, Michael, 2016. "Household risk taking after the financial crisis," The Quarterly Review of Economics and Finance, Elsevier, vol. 59(C), pages 141-160.
    3. Lamla, Bettina & Coppola, Michela, 2013. "Is it all about access? Perceived access to occupational pensions in Germany," MEA discussion paper series 201312, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    4. Bannier, Christina E. & Schwarz, Milena, 2018. "Gender- and education-related effects of financial literacy and confidence on financial wealth," Journal of Economic Psychology, Elsevier, vol. 67(C), pages 66-86.
    5. Bannier, Christina E. & Neubert, Milena, 2016. "Actual and perceived financial sophistication and wealth accumulation: The role of education and gender," CFS Working Paper Series 528, Center for Financial Studies (CFS).
    6. Kluth, Sebastian, 2014. "Should I Stay or Should I Go? The Role of Actuarial Reduction Rates in Individual Retirement Planning in Germany," MEA discussion paper series 201409, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    7. Bucher-Koenen, Tabea, 2011. "Financial Literacy, Riester Pensions, and Other Private Old Age Provision in Germany," MEA discussion paper series 11250, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    8. Bucher-Koenen, Tabea & Koenen, Johannes, 2015. "Do Seemingly Smarter Consumers Get Better Advice?," MEA discussion paper series 201501, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    9. Thomas Y. Mathä & Alessandro Porpiglia & Michael Ziegelmeyer, 2012. "The Luxembourg Household Finance and Consumption Survey (LU-HFCS): Introduction and Results," BCL working papers 73, Central Bank of Luxembourg.
    10. Michael Ziegelmeyer & Julius Nick, 2013. "Backing out of private pension provision: lessons from Germany," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 40(3), pages 505-539, August.
    11. Bannier, Christina E. & Schwarz, Milena, 2017. "Skilled but unaware of it: Occurrence and potential long-term effects of females' financial underconfidence," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168188, Verein für Socialpolitik / German Economic Association.
    12. Kluth, Sebastian, 2014. "Should I Stay or Should I Go? The Role of Actuarial Reduction Rates in Individual Retirement Planning in Germany," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100413, Verein für Socialpolitik / German Economic Association.
    13. repec:mea:meawpa:14282 is not listed on IDEAS
    14. Tabea Bucher†Koenen & Bettina Lamla†Dietrich, 2018. "The Long Shadow of Socialism: Puzzling Evidence on East†West German Differences in Financial Literacy," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 47(2-3), pages 413-438, July.
    15. repec:mea:meawpa:14279 is not listed on IDEAS
    16. Coppola, Michela & Börsch-Supan, Axel, 2011. "The German SAVE Study: Design, selected results and future developments," VfS Annual Conference 2011 (Frankfurt, Main): The Order of the World Economy - Lessons from the Crisis 48733, Verein für Socialpolitik / German Economic Association.
    17. repec:mea:meawpa:12262 is not listed on IDEAS
    18. Coppola, Michela & Gasche, Martin, 2011. "Die Riester-Förderung – das unbekannte Wesen," MEA discussion paper series 11244, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.

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    More about this item

    Keywords

    Imputation methods; Monte-Carlo simulation; Imputation evaluation; Item-nonresponse; Missing data; Imputation; Retransformation; Sample surveys; SAVE; C01; C81; C49;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other

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