Efficient Detection of Errors in VAT Data - Investigative Methods by Katie Davies

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Explore efficient methods for detecting errors in VAT data through selective editing, outlier detection, and combining conclusions. Reduce respondent burden and introduce administrative data for more accurate reporting.

  • VAT Data
  • Error Detection
  • Investigative Methods
  • Katie Davies
  • Administrative Data

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Presentation Transcript


  1. Investigating methods of efficient detection of errors in VAT data Katie Davies, Office for National Statistics

  2. Overview Background Approach 1: Selective Editing Approach 2: Outlier Detection Comparisons Approach 3: Combine Conclusions & Recommendations

  3. Background Reduce respondent burden introduction of administrative data Distributive Trade Transformation Magnitude of data Terminology: o RU: Reporting Unit o Cell level: RUs aggregated based on industry and employment size band

  4. Background Initial Automatic Editing Thousand Pound rule Quarterly pattern o [x, x, x, x] except x = 0 o [x, x, x, y] o [0, 0, 0, y]

  5. Approach 1: Selective Editing Micro level Targeted Score function Ratio of means imputation

  6. Approach 1: Selective Editing Score Function ? ? ? ? ? ? ? ? ? ? ? ??,? 1 ? ??,? 1 ????? = ? ??? ? = ???????? ??? ??????? ?????? ? = ???? ???????? ???? ? ? ???? 3 ????? ? =???????? ???????? ??? ?????????? ?? ??????? ?????? ???????? ???????? ??? ?????? ?? ??????? ?????? ??,? 1= ????? ???????? ??? ?????? ? ?? ???????? ?????? ? 1 ? = ???????? ????????? ?? ???????? ???? ? ? ???? 3 ?????

  7. Approach 1: Selective Editing good performance

  8. Approach 1: Selective Editing further consideration

  9. Approach 1: Selective Editing further consideration

  10. Approach 1: Selective Editing further consideration

  11. Approach 1: Selective Editing further consideration

  12. Approach 1: Selective Editing further consideration

  13. Approach 2: Outlier Detection Macro level Seas function to identify Automatic treatment = use factors from seas RU treatment = Ratio of means imputation on RU with highest score

  14. Approach 2: Outlier Detection good performance

  15. Comparisons Selective Editing Outlier Detection

  16. Comparisons Selective Editing Outlier Detection

  17. Approach 3: Combine Raw RU data Selective editing Original vs Treated plots Edited RU data Aggregation Edited RU data based on decisions Cell level data RU Outliers detected treatment Treated RU data

  18. Approach 3: Combine good performance

  19. Approach 3: Combine further investigation

  20. Conclusions & Recommendations Raw RU data Selective editing Edited RU data Original vs Treated plots Aggregation Edited RU data based on decisions Cell level data RU Outliers detected treatment Aggregation Treated RU data Treated cell level data Further investigation & revisions Final cell level data

  21. Thanks for listening!

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