1. Editing and Imputing VAT Data for the Purpose of Producing Mixed- Source Turnover Estimates Hannah Finselbach and Daniel Lewis Office for National Statistics, UK

2. Overview Key principles Types of error in VAT Turnover data Methods for detecting suspicious VAT Turnover Methods for correcting suspicious VAT Turnover Conclusions

3. Key principles Understand how data are returned and processed Maintain original data supplied by tax office Keep audit trail for changes to data Make good use of historical and register data Automate process of detecting and correcting errors, and allow future improvements to this process

4. Types of error in VAT Turnover data Suspicious individual turnover values Unusually large or small turnover Evaluate detection and correction methods using past data from survey to be used in mixed-source estimates Unit errors Systematic errors, e.g. Thousand or Million Pound (GBP) errors Automatic correction Suspicious quarterly reporting patterns Described in Hoogland (2011) Mark as suspicious before correcting

5. Methods for detecting suspicious VAT Turnover values Extreme values in current period distribution Extreme change in contribution to industry compared with previous period Hidiroglou-Berthelot method Transformed period on period ratio with influence measure Combine methods 1 or 2 with influence measure

6. Evaluating methods for detecting suspicious VAT Turnover Set parameters so that each method fails the same number of businesses Check mean size of total turnover and employment for failing businesses Estimate “false hits” based on comparison to short term (Monthly Business Survey) survey data Businesses that fail but whose values are similar to those collected by survey

7. Results of evaluation for the MBS Methods based on extremes in current distribution fail largest businesses Lower estimated false hits for methods using previous period data Best method: extreme change in contribution to industry compared to previous period

8. Options for dealing with suspicious VAT data Remove from data set Mark as suspicious in data set Change values (impute) manually Change values (impute) automatically Evaluate by randomly creating suspicious values in “clean” data and comparing imputed values to original values (repeated simulation) Best method is ratio imputation – ratio of means using data from the previous period

9. Implementation in ONS Fine tune thresholds for detection method examining distribution of period on period ratios for each VAT Turnover stagger and reporting pattern Specify methods for IT system developers Add variables and markers to Business Register hold raw and cleaned VAT data Flag suspicious values of VAT Turnover Further research on: Impact of using cleaned VAT Turnover data in Business Register processes Required changes to selective editing for surveys producing mixed-source estimates

10. Conclusions Important to clean VAT turnover data before use in short term mixed-source estimates Need to understand how data are returned and processed Methods based on previous period returns work best for detection and correction of suspicious values Unit errors and suspicious patterns should be identified and corrected Further work required to successfully implement mixed-source estimates in ONS