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

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  • 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.

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  • 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
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    References listed on IDEAS

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    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. 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.
    4. 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.
    5. 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.
    6. Cristina Barceló, 2006. "Imputation of the 2002 wave of the Spanish survey of household finances (EFF)," Occasional Papers 0603, Banco de España.
    7. 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.
    8. 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.
    9. Nicolas Albacete, 2012. "Multiple Imputation in the Austrian Household Survey on Housing Wealth," Working Papers 176, Oesterreichische Nationalbank (Austrian Central Bank).
    10. 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.
    11. 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.
    12. repec:iab:iabfme:201202(en is not listed on IDEAS
    13. 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.
    14. 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].
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    Citations

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    Cited by:

    1. Corneo, Giacomo G. & Bönke, Timm & Westermeier, Christian, 2016. "Erbschaft und Eigenleistung im Vermögen der Deutschen: eine Verteilungsanalyse," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 17(1), pages 35-53.
    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.

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    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

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