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PRODID:https://www.diw.de/de/diw_01.c.806339.de/veranstaltungen.html
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UID:diw_01.c.482335.en
LOCATION:Eleanor-Dulles-Raum,DIW Berlin im Quartier 110,5.2.010,Anton-Wilhelm-Amo-Straße 58,10117 Berlin
SUMMARY:Estimating the Impact of Alternative Multiple Imputation Methods on Longitudinal Wealth Data
DESCRIPTION:12:30 - 13:30 // (together with Markus M. Grabka) Statistical Analysis in surveys is generally facing missing data. In a longitudinal study, when for some missing values past or future data points might be available, the question arises how to successfully transform this advantage into improved imputation strategies. In a simulation study the authors compare six combinations of cross-sectional and longitudinal imputation strategies for German wealth panel data. The authors create simulation data sets by blanking out observed data points: they induce item non response by both missing at random (MAR) and two missing not at random (MNAR) mechanisms. They test the performance of multiple imputation using chained equations (MICE), an imputation procedure for panel data known as the row-and-column method and a regression prediction with correction for sample selection. The regression and MICE approaches serve as fallback methods when only cross-sectional data is available. The row-and-column method, a univariate method, performs well considering the cross-sectional evaluation criteria. As for wealth mobility, two additional criteria show that a model based approach such as MICE might be the preferable choice. Overall the results show that if the variables, which ought to be imputed, are highly skewed, an univariate imputation technique such as the row-and-column imputation should not be dismissed beforehand.
DTSTART;VALUE=DATE:20141022
DTEND;VALUE=DATE:20141022
DTSTAMP:20141019T220000Z
URL:https://www.diw.de/en/diw_01.c.482335.en/events/estimating_the_impact_of_alternative_multiple_imputation_methods_on_longitudinal_wealth_data.html
ORGANIZER;CN=Sarah Dahmann:mailto:sdahmann@diw.de
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