IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/57666.html
   My bibliography  Save this paper

Multiple imputation in a complex household survey - the German Panel on Household Finances (PHF): challenges and solutions

Author

Listed:
  • Martin, Eisele
  • Zhu, Junyi

Abstract

In this paper, we present a case study of the imputation in a complex household survey - the first wave of the German Panel on Household Finances (PHF). A household wealth survey has to be built on a questionnaire with rather complex logical structure mainly because the probes of many wealth items have to be proceeded on both intensive and extensive margins. Hence the number of potential predictors for each imputation model grows and more non-compliance can confront standard modelling due to, e.g., irregular missing patterns, interdependent logical constraints, data anomalies etc. Our model selection procedure borrows the techniques for the out-of-sample prediction to handle the overfitting often associated with the introduction of a large number of predictors. We also take the measures to produce ex ante evaluation for modelling which can be more efficient than the common diagnosis done after imputation in practice. Solutions for the difficulties in the real data and questionnaire structures are also presented. On the other hand, we incorporate the rich flagging information in developing various measures of item-nonresponse to access this complication from logical structure. We find that information loss due to the contagion of item-nonresponse between variables is not serious in our imputed data.

Suggested Citation

  • 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.
  • Handle: RePEc:pra:mprapa:57666
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/57666/1/MPRA_paper_57666.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Yucel, Recai M., 2011. "State of the Multiple Imputation Software," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i01).
    2. White, Ian R. & Daniel, Rhian & Royston, Patrick, 2010. "Avoiding bias due to perfect prediction in multiple imputation of incomplete categorical variables," Computational Statistics & Data Analysis, Elsevier, vol. 54(10), pages 2267-2275, October.
    3. Cragg, John G, 1971. "Some Statistical Models for Limited Dependent Variables with Application to the Demand for Durable Goods," Econometrica, Econometric Society, vol. 39(5), pages 829-844, September.
    4. 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.
    5. repec:iab:iabfme:201202(en is not listed on IDEAS
    6. Schenker, Nathaniel & Raghunathan, Trivellore E. & Chiu, Pei-Lu & Makuc, Diane M. & Zhang, Guangyu & Cohen, Alan J., 2006. "Multiple Imputation of Missing Income Data in the National Health Interview Survey," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 924-933, September.
    7. Jörg Drechsler, 2011. "Multiple imputation in practice—a case study using a complex German establishment survey," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(1), pages 1-26, March.
    8. Kobi Abayomi & Andrew Gelman & Marc Levy, 2008. "Diagnostics for multivariate imputations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 57(3), pages 273-291, June.
    9. 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.
    10. Cristina Barceló, 2006. "Imputation of the 2002 wave of the Spanish survey of household finances (EFF)," Occasional Papers 0603, Banco de España.
    11. Rubin, Donald B., 2004. "The Design of a General and Flexible System for Handling Nonresponse in Sample Surveys," The American Statistician, American Statistical Association, vol. 58, pages 298-302, November.
    12. 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.
    13. Jaenichen, Ursula & Sakshaug, Joseph, 2012. "Multiple imputation of household income in the first wave of PASS," FDZ Methodenreport 201202_en, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    14. Nicolas Albacete, 2012. "Multiple Imputation in the Austrian Household Survey on Housing Wealth," Working Papers 176, Oesterreichische Nationalbank (Austrian Central Bank).
    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. Corneo Giacomo & Bönke Timm & Westermeier Christian, 2016. "Erbschaft und Eigenleistung im Vermögen der Deutschen: Eine Verteilungsanalyse," Perspektiven der Wirtschaftspolitik, De Gruyter, vol. 17(1), pages 35-53, April.
    2. Metzger, Christoph, 2018. "Intra-household allocation of non-mandatory retirement savings," The Journal of the Economics of Ageing, Elsevier, vol. 12(C), pages 77-87.
    3. Christoph Metzger, 2017. "Who is saving privately for retirement and how much? New evidence for Germany," International Review of Applied Economics, Taylor & Francis Journals, vol. 31(6), pages 811-831, November.
    4. Pasteau, Etienne & Zhu, Junyi, 2018. "Love and money with inheritance: Marital sorting by labor income and inherited wealth in the modern partnership," Discussion Papers 23/2018, Deutsche Bundesbank.
    5. Altmann Kristina & Bernard René & Le Blanc Julia & Gabor-Toth Enikö & Hebbat Malik & Kothmayr Lisa & Schmidt Tobias & Tzamourani Panagiota & Werner Daniel & Zhu Junyi, 2020. "The Panel on Household Finances (PHF) – Microdata on household wealth in Germany," German Economic Review, De Gruyter, vol. 21(3), pages 373-400, September.
    6. Giacomo Corneo & Johannes König & Carsten Schröder, 2018. "Distributional Effects of Subsidizing Retirement Savings Accounts: Evidence from Germany," FinanzArchiv: Public Finance Analysis, Mohr Siebeck, Tübingen, vol. 74(4), pages 415-445, December.
    7. Humera Razzak & Christian Heumann, 2019. "Hybrid Multiple Imputation In A Large Scale Complex Survey," Statistics in Transition New Series, Polish Statistical Association, vol. 20(4), pages 33-58, December.
    8. Razzak Humera & Heumann Christian, 2019. "Hybrid Multiple Imputation In A Large Scale Complex Survey," Statistics in Transition New Series, Polish Statistical Association, vol. 20(4), pages 33-58, December.
    9. Kreutzmann, Ann-Kristin & Marek, Philipp & Salvati, Nicola & Schmid, Timo, 2019. "Estimating regional wealth in Germany: How different are East and West really?," Discussion Papers 35/2019, Deutsche Bundesbank.

    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. Christian Aßmann & Ariane Würbach & Solange Goßmann & Ferdinand Geissler & Anika Bela, 2017. "Nonparametric Multiple Imputation for Questionnaires with Individual Skip Patterns and Constraints: The Case of Income Imputation in the National Educational Panel Study," Sociological Methods & Research, , vol. 46(4), pages 864-897, November.
    2. 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.
    3. Juana Sanchez & Sydney Noelle Kahmann, 2017. "R&D, Attrition and Multiple Imputation in BRDIS," Working Papers 17-13, Center for Economic Studies, U.S. Census Bureau.
    4. 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.
    5. 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.
    6. Williams, Randi M. & Zhang, Jing & Woodard, Nathaniel & Slade, Jimmie & Santos, Sherie Lou Zara & Knott, Cheryl L., 2020. "Development and validation of an instrument to assess institutionalization of health promotion in faith-based organizations," Evaluation and Program Planning, Elsevier, vol. 79(C).
    7. Stephen D. Oliner & Morris A. Davis & Will Larson, 2019. "Mortgage risk since 1990," AEI Economics Working Papers 1001502, American Enterprise Institute.
    8. Mingyang Cai & Gerko Vink, 2022. "A note on imputing squares via polynomial combination approach," Computational Statistics, Springer, vol. 37(5), pages 2185-2201, November.
    9. repec:iab:iabfme:201202(en is not listed on IDEAS
    10. Morehart, Mitch & Milkove, Dan & Xu, Yang, 2014. "Multivariate Farm Debt Imputation in the Agricultural Resource Management Survey (ARMS)," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 169401, Agricultural and Applied Economics Association.
    11. Rabea Aschenbruck & Gero Szepannek & Adalbert F. X. Wilhelm, 2023. "Imputation Strategies for Clustering Mixed-Type Data with Missing Values," Journal of Classification, Springer;The Classification Society, vol. 40(1), pages 2-24, April.
    12. Zhong, Hua & Hu, Wuyang & Penn, Jerrod M., 2018. "Application of Multiple Imputation in Dealing with Missing Data in Agricultural Surveys: The Case of BMP Adoption," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 43(1), January.
    13. Jaenichen, Ursula & Sakshaug, Joseph, 2012. "Multiple imputation of household income in the first wave of PASS," FDZ Methodenreport 201202_en, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    14. 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.
    15. 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.
    16. 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.
    17. Dang, Hai-Anh H & Carletto, Calogero, 2022. "Recall Bias Revisited: Measure Farm Labor Using Mixed-Mode Surveys and Multiple Imputation," IZA Discussion Papers 14997, Institute of Labor Economics (IZA).
    18. Zachary H. Seeskin, 2016. "Evaluating the Use of Commercial Data to Improve Survey Estimates of Property Taxes," CARRA Working Papers 2016-06, Center for Economic Studies, U.S. Census Bureau.
    19. F. Di Lascio & Simone Giannerini & Alessandra Reale, 2015. "Exploring copulas for the imputation of complex dependent data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(1), pages 159-175, March.
    20. Ankita Patnaik & Jeffrey Hemmeter & Arif Mamun, "undated". "Promoting Readiness of Minors with Autism Spectrum Disorder: Evidence from a Randomized Controlled Trial," Mathematica Policy Research Reports a74c93d9bdce40709ad81cdbc, Mathematica Policy Research.
    21. Westermeier, Christian & Grabka, Markus M., 2016. "Longitudinal Wealth Data and Multiple Imputation: An Evaluation Study," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 10(3), pages 237-252.

    More about this item

    Keywords

    Multiple imputation; Model selection; Panel on household finance; item-nonresponse evaluation;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey Methods
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

    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:pra:mprapa:57666. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.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.