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Errors inevitably affect data. Statistical institutes deal with them with rules designing data relationships and imputation for missing information. In this paper, we report the use of classification trees to improve this process when using longitudinal time-lagged administrative data in the estimation process.

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Classification Trees Applied to Time-Lagged Data to Improve Quality in Official Statistics

  • 会议论文

  • 发表日期：

    2025年

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  • Marco Di Zio,
  • Romina Filippini,
  • Gaia Rocchetti,
  • Simona Toti

摘要

Errors inevitably affect data. Statistical institutes deal with them with rules designing data relationships and imputation for missing information. In this paper, we report the use of classification trees to improve this process when using longitudinal time-lagged administrative data in the estimation process.