<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Measuring the Accuracy of Aggregates Computed from a Statistical Register

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Measuring the Accuracy of Aggregates Computed from a Statistical Register"}

The Italian National Statistical Institute (Istat) is currently engaged in a modernization programme that foresees a significant revision of the methods...

Measuring the Accuracy of Aggregates Computed from a Statistical Register

Published Online: Jun 22, 2021

Page range: 481 - 503

Received: May 01, 2019

Accepted: Jan 01, 2021

DOI: https://doi.org/10.2478/jos-2021-0021

Keywords
Integration, anticipated variance, linearization, mean square error, total survey error

© 2021 Giorgio Alleva et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Measuring the Accuracy of Aggregates Computed from a Statistical Register

Published Online: Jun 22, 2021

Page range: 481 - 503

Received: May 01, 2019

Accepted: Jan 01, 2021

DOI: https://doi.org/10.2478/jos-2021-0021

KeywordsIntegration, anticipated variance, linearization, mean square error, total survey error

© 2021 Giorgio Alleva et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
Integration, anticipated variance, linearization, mean square error, total survey error