Statistical analysis in surveys is generally facing missing data. In longitudinal studies for some missing values there might be past or future data points 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 a missing at random (MAR) and two differential non-response (DNR) mechanisms. We 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 performs surprisingly well considering the cross-sectional evaluation criteria. For trend estimates and the measurement of inequality, combining MICE with the row-and-column technique regularly improves the results based on a catalogue of six evaluation criteria including three separate inequality indices. 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, the row-and-column technique should not be dismissed beforehand.