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Wealth dynamics: reducing noise in panel data

dc.contributor.authorHill, Daniel H.en_US
dc.date.accessioned2007-09-18T19:24:40Z
dc.date.available2007-09-18T19:24:40Z
dc.date.issued2006-09en_US
dc.identifier.citationHill, Daniel H. (2006). "Wealth dynamics: reducing noise in panel data." Journal of Applied Econometrics 21(6): 845-860. <http://hdl.handle.net/2027.42/55813>en_US
dc.identifier.issn0883-7252en_US
dc.identifier.issn1099-1255en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/55813
dc.description.abstractAlthough 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.en_US
dc.format.extent132316 bytes
dc.format.extent3118 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.publisherJohn Wiley & Sons, Ltd.en_US
dc.subject.otherBusiness, Finance & Managementen_US
dc.titleWealth dynamics: reducing noise in panel dataen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelEconomicsen_US
dc.subject.hlbsecondlevelMathematicsen_US
dc.subject.hlbsecondlevelStatistics and Numeric Dataen_US
dc.subject.hlbtoplevelBusinessen_US
dc.subject.hlbtoplevelScienceen_US
dc.subject.hlbtoplevelSocial Sciencesen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumThe Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI 48104, USA ; The Survey Research Center, Institute for Social Research, University of Michigan, 426 Thompson Street, Room 3136, Ann Arbor, MI 48104, USA.en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/55813/1/878_ftp.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1002/jae.878en_US
dc.identifier.sourceJournal of Applied Econometricsen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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