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Measuring the Accuracy of Aggregates Computed from a Statistical Register

Author

Listed:
  • Alleva Giorgio
  • Petrarca Francesca

    (Sapienza University of Rome, Via del Castro Laurenziano 9, 00161Rome, Italy.)

  • Falorsi Piero Demetrio
  • Righi Paolo

    (Italian National Institute of Statistics (Istat), Via Cesare Balbo, 16 – 00184Rome, Italy.)

Abstract

The Italian National Statistical Institute (Istat) is currently engaged in a modernization programme that foresees a significant revision of the methods traditionally used for the production of official statistics. The main concept behind this transformation is the use of the Integrated System Statistical Registers, created by a massive integration of administrative archives and survey data. In this article, we focus on how to measure the accuracy of register estimates of a population total from measurements calculated at the unit level. We propose the global mean squared error (GMSE) as a statistical quantity suitable for measuring accuracy in the context of the production of official statistics. It can be defined to explicitly consider the main sources of uncertainty that may affect registers. The article suggests a feasible calculation strategy for the GMSE that allows National Statistical Institutes to build algorithms that can promptly be applied for each user request, thus improving the relevance, transparency and confidence of official statistics. Through a simulation study, we verified the efficacy of the proposed strategy.

Suggested Citation

  • Alleva Giorgio & Petrarca Francesca & Falorsi Piero Demetrio & Righi Paolo, 2021. "Measuring the Accuracy of Aggregates Computed from a Statistical Register," Journal of Official Statistics, Sciendo, vol. 37(2), pages 481-503, June.
    • Handle: RePEc:vrs:offsta:v:37:y:2021:i:2:p:481-503:n:3
    DOI: 10.2478/jos-2021-0021
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    References listed on IDEAS

    as
    1. Jae Kwang Kim & J. N. K. Rao, 2012. "Combining data from two independent surveys: a model-assisted approach," Biometrika, Biometrika Trust, vol. 99(1), pages 85-100.
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    3. Sixia Chen & David Haziza, 2017. "Multiply robust imputation procedures for the treatment of item nonresponse in surveys," Biometrika, Biometrika Trust, vol. 104(2), pages 439-453.
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