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Wealth dynamics: reducing noise in panel data

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  • Daniel H. Hill

    (The Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI 48104, USA)

Abstract

Although the asset data from the Health and Retirement Study (HRS) is of very high quality, there is sufficient noise to frustrate attempts to study saving behaviour by examining wave-to-wave change in wealth. In this research, we attempt to reduce noise by means of reactive-dependent interviewing in which respondents with large inexplicable changes in assets between 1998 and 2000 are called back by HRS interviewers, presented with their prior reports and asked to reconcile the data. We achieved reconciliation for 1255 households (2479 net-worth components) and, as a result, the variance in measured change for the entire sample of 11,583 households with the same financial respondents in both waves was cut in half. The empirical validity of the data also appears to have been improved. The correlation of gross change in net worth and income, for instance, increased from an insignificant negative to a highly significant positive value. Although reconciliation of large asset changes marginally improves the goodness of fit of multivariate models, there remains sufficient noise in the asset-change data to require analysts to employ additional methods to reduce the influence of outliers. Copyright © 2006 John Wiley & Sons, Ltd.

Suggested Citation

  • Daniel H. Hill, 2006. "Wealth dynamics: reducing noise in panel data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(6), pages 845-860.
    • Handle: RePEc:jae:japmet:v:21:y:2006:i:6:p:845-860
    DOI: 10.1002/jae.878
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    References listed on IDEAS

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    1. Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843, Elsevier.
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    1. James M. Poterba & Steven F. Venti & David A. Wise, 2011. "Family Status Transitions, Latent Health, and the Post-Retirement Evolution of Assets," NBER Chapters, in: Explorations in the Economics of Aging, pages 23-69, National Bureau of Economic Research, Inc.
    2. Michelle Sovinsky & Steven Stern, 2016. "Dynamic modelling of long-term care decisions," Review of Economics of the Household, Springer, vol. 14(2), pages 463-488, June.
    3. Poterba, James & Venti, Steven & Wise, David A., 2018. "Longitudinal determinants of end-of-life wealth inequality," Journal of Public Economics, Elsevier, vol. 162(C), pages 78-88.
    4. Fabrizio Mazzonna & Franco Peracchi, 2024. "Are Older People Aware of Their Cognitive Decline? Misperception and Financial Decision-Making," Journal of Political Economy, University of Chicago Press, vol. 132(6), pages 1793-1830.
    5. Bucks, Brian & Pence, Karen, 2015. "Wealth, pensions, debt, and savings: Considerations for a panel survey," Journal of Economic and Social Measurement, IOS Press, issue 1-4, pages 151-175.
    6. Bridget Hiedemann & Michelle Sovinsky & Steven Stern, 2018. "Will You Still Want Me Tomorrow?: The Dynamics of Families’ Long-Term Care Arrangements," Journal of Human Resources, University of Wisconsin Press, vol. 53(3), pages 663-716.
    7. Venti Steven, 2011. "Economic Measurement in the Health and Retirement Study," Forum for Health Economics & Policy, De Gruyter, vol. 14(2), pages 1-20, April.
    8. Arora, Kanika, 2016. "How does dementia onset in parents influence unmarried adult children's wealth," Social Science & Medicine, Elsevier, vol. 152(C), pages 156-165.

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