IDEAS home Printed from https://ideas.repec.org/p/mnh/spaper/2561.html
   My bibliography  Save this paper

A Markov Chain Monte Carlo multiple imputation procedure for dealing with item nonresponse in the German SAVE survey

Author

Listed:
  • Schunk, Daniel

Abstract

Important empirical information on household behavior is obtained from surveys. However, various interdependent factors that can only be controlled to a limited extent lead to unit and item nonresponse, and missing data on certain items is a frequent source of difficulties in statistical practice. This paper presents the theoretical underpinnings of a Markov Chain Monte Carlo multiple imputation procedure and applies this procedure to a socio-economic survey of German households, the SAVE survey. I discuss convergence properties and results of the iterative multiple imputation method and I compare them briefly with other imputation approaches. Concerning missing data in the SAVE survey, the results suggest that item nonresponse is not occurring randomly but is related to the included covariates. The analysis further indicates that there might be differences in the character of nonresponse across asset types. Concerning the methodology of imputation, the paper underlines that it would be of particular interest to apply different imputation methods to the same dataset and to compare the findings.

Suggested Citation

  • 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.
    • Handle: RePEc:mnh:spaper:2561
    as

    Download full text from publisher

    File URL: https://madoc.bib.uni-mannheim.de/2561/1/dp07_06.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Scheuren, Fritz, 1988. "Missing-Data Adjustments in Large Surveys: Comment," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 298-299, July.
    2. Lothar Essig & Joachim K. Winter, 2009. "Item Non-Response to Financial Questions in Household Surveys: An Experimental Study of Interviewer and Mode Effects," Fiscal Studies, Institute for Fiscal Studies, vol. 30(Special I), pages 367-390, December.
    3. Daniel Schunk, 2006. "The German SAVE Survey: Documentation and Methodology," MEA discussion paper series 06109, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    4. Hersch, Joni, 2000. "Gender, Income Levels, and the Demand for Cigarettes," Journal of Risk and Uncertainty, Springer, vol. 21(2-3), pages 263-282, November.
    5. Joni Hersch & W. Kip Viscusi, 1990. "Cigarette Smoking, Seatbelt Use, and Differences in Wage-Risk Tradeoffs," Journal of Human Resources, University of Wisconsin Press, vol. 25(2), pages 202-227.
    6. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 287-296, July.
    7. Hilary W. Hoynes & Michael D. Hurd & Harish Chand, 1998. "Household Wealth of the Elderly under Alternative Imputation Procedures," NBER Chapters, in: Inquiries in the Economics of Aging, pages 229-257, National Bureau of Economic Research, Inc.
    8. 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.
    9. David A. Wise, 2005. "Analyses in the Economics of Aging," NBER Books, National Bureau of Economic Research, Inc, number wise05-1.
    10. Axel H. Boersch-Supan & Lothar Essig, 2005. "Household Saving in Germany: Results of the First SAVE Study," NBER Chapters, in: Analyses in the Economics of Aging, pages 317-356, National Bureau of Economic Research, Inc.
    11. Phillip B. Levine & Tara A. Gustafson & Ann D. Velenchik, 1995. "More Bad News for Smokers? The Effects of Cigarette Smoking on Labor Market Outcomes," NBER Working Papers 5270, National Bureau of Economic Research, Inc.
    12. 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.
    13. Cristina Barceló, 2006. "Imputation of the 2002 wave of the Spanish survey of household finances (EFF)," Occasional Papers 0603, Banco de España.
    14. Sande, I G, 1988. "Missing-Data Adjustments in Large Surveys: Comment," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 296-297, July.
    15. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 300-301, July.
    16. Phillip B. Levine & Tara A. Gustafson & Ann D. Velenchik, 1997. "More Bad News for Smokers? The Effects of Cigarette Smoking on Wages," ILR Review, Cornell University, ILR School, vol. 50(3), pages 493-509, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Daniel Schunk, 2006. "The German SAVE Survey: Documentation and Methodology," MEA discussion paper series 06109, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    2. Börsch-Supan, Axel & Reil-Held, Anette & Schunk, Daniel, 2007. "The savings behaviour of German households: First Experiences with state promoted private pensions," MEA discussion paper series 07136, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    3. Eiji Yamamura, 2016. "Smokers’ Preference for Divorce and Extramarital Sex," Journal of Economics and Econometrics, Economics and Econometrics Society, vol. 59(2), pages 44-76.
    4. Martin, Eisele & Zhu, Junyi, 2013. "Multiple imputation in a complex household survey - the German Panel on Household Finances (PHF): challenges and solutions," MPRA Paper 57666, University Library of Munich, Germany.
    5. Börsch-Supan, Axel & Coppola, Michela & Reil-Held, Anette, 1970. "Riester Pensions in Germany: Design, Dynamics, Targetting Success and Crowding-In," MEA discussion paper series 201220, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    6. 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.
    7. Munasinghe, Lalith & Sicherman, Nachum, 2004. "Wage Dynamics and Unobserved Heterogeneity: Time Preference or Learning Ability?," IZA Discussion Papers 1436, Institute of Labor Economics (IZA).
    8. Borghans, Lex & Golsteyn, Bart H.H., 2006. "Time discounting and the body mass index: Evidence from the Netherlands," Economics & Human Biology, Elsevier, vol. 4(1), pages 39-61, January.
    9. Ziegelmeyer, Michael, 2009. "Documentation of the logical imputation using the panel structure of the 2003-2008 German SAVE Survey," Sonderforschungsbereich 504 Publications 08-41, Sonderforschungsbereich 504, Universität Mannheim;Sonderforschungsbereich 504, University of Mannheim.
    10. 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.
    11. Eiji Yamamura, 2014. "Smokers’ Sexual Behavior and Their Satisfaction with Family Life," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 118(3), pages 1229-1247, September.
    12. Borghans, Lex & Golsteyn, Bart H.H., 2005. "Time Discounting and the Body Mass Index," IZA Discussion Papers 1597, Institute of Labor Economics (IZA).
    13. Picone Gabriel & Sloan Frank, 2001. "How Costly Are Smokers to Other People? Longitudinal Evidence on the Near Elderly," Forum for Health Economics & Policy, De Gruyter, vol. 4(1), pages 1-31, January.
    14. Borghans, Lex & Golsteyn, Bart H.H., 2005. "Time Discounting and the Body Mass Index," IZA Discussion Papers 1597, Institute of Labor Economics (IZA).
    15. 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.
    16. Joost Ginkel & Pieter Kroonenberg, 2014. "Using Generalized Procrustes Analysis for Multiple Imputation in Principal Component Analysis," Journal of Classification, Springer;The Classification Society, vol. 31(2), pages 242-269, July.
    17. Verbeek, M.J.C.M. & Nijman, T.E., 1992. "Incomplete panels and selection bias : A survey," Discussion Paper 1992-7, Tilburg University, Center for Economic Research.
    18. Pierre Pestieau & Gregory Ponthiere, 2012. "Myopia, regrets, and risky behaviors," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 19(2), pages 288-317, April.
    19. Gerko Vink & Laurence E. Frank & Jeroen Pannekoek & Stef Buuren, 2014. "Predictive mean matching imputation of semicontinuous variables," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 68(1), pages 61-90, February.
    20. Xiong, Ruoxuan & Pelger, Markus, 2023. "Large dimensional latent factor modeling with missing observations and applications to causal inference," Journal of Econometrics, Elsevier, vol. 233(1), pages 271-301.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:mnh:spaper:2561. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Katharina Rautenberg (email available below). General contact details of provider: https://edirc.repec.org/data/sfmande.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.