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Accuracy of Mixed-Source Statistics as Affected by Classification Errors

Author

Listed:
  • van Delden Arnout
  • Scholtus Sander

    (Statistics Netherlands, Department of Process Development and Methodology, Henri Faasdreef 312, P.O. Box 24500, 2490 HA The Hague, The Netherlands.)

  • Burger Joep

    (Statistics Netherlands, Department of Process Development and Methodology, CBS-weg 11, P.O. Box 4481, 6401 CZ Heerlen, The Netherlands.)

Abstract

Publications in official statistics are increasingly based on a combination of sources. Although combining data sources may result in nearly complete coverage of the target population, the outcomes are not error free. Estimating the effect of nonsampling errors on the accuracy of mixed-source statistics is crucial for decision making, but it is not straightforward. Here we simulate the effect of classification errors on the accuracy of turnover-level estimates in car-trade industries. We combine an audit sample, the dynamics in the business register, and expert knowledge to estimate a transition matrix of classification-error probabilities. Bias and variance of the turnover estimates caused by classification errors are estimated by a bootstrap resampling approach. In addition, we study the extent to which manual selective editing at micro level can improve the accuracy. Our analyses reveal which industries do not meet preset quality criteria. Surprisingly, more selective editing can result in less accurate estimates for specific industries, and a fixed allocation of editing effort over industries is more effective than an allocation in proportion with the accuracy and population size of each industry. We discuss how to develop a practical method that can be implemented in production to estimate the accuracy of register-based estimates.

Suggested Citation

  • van Delden Arnout & Scholtus Sander & Burger Joep, 2016. "Accuracy of Mixed-Source Statistics as Affected by Classification Errors," Journal of Official Statistics, Sciendo, vol. 32(3), pages 619-642, September.
    • Handle: RePEc:vrs:offsta:v:32:y:2016:i:3:p:619-642:n:4
    DOI: 10.1515/jos-2016-0032
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    References listed on IDEAS

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    1. Peter Hall & Tapabrata Maiti, 2006. "On parametric bootstrap methods for small area prediction," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(2), pages 221-238, April.
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