The incomes of top earners are typically top‐coded in survey data. I show that the accuracy of imputed income values for top earners in longitudinal surveys can be improved significantly by incorporating information from multiple time periods into the imputation process in a simple way. Moreover, I introduce an innovative, nonparametric empirical Bayes imputation method that further improves imputation quality. I show that the empirical Bayes imputation method reduces the RMSE of imputed income values by 19–51% relative to standard approaches in the literature. I also illustrate the benefits of the empirical Bayes method for investigating multi‐year income inequality.

中文翻译：

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在纵向调查中估算顶级收入数据*

收入最高的人的收入通常在调查数据中排名最高。我表明，通过以简单的方式将多个时间段的信息纳入估算过程，可以显着提高纵向调查中最高收入者的估算收入值的准确性。此外，我介绍了一种创新的非参数经验贝叶斯插补方法，可以进一步提高插补质量。我表明，相对于文献中的标准方法，经验贝叶斯估算方法使估算收入值的RMSE降低了19–51％。我还说明了经验贝叶斯方法在调查多年收入不平等方面的好处。