1. bookVolume 37 (2021): Issue 2 (June 2021)
    Special Issue on New Techniques and Technologies for Statistics
Journal Details
First Published
01 Oct 2013
Publication timeframe
4 times per year
access type Open Access

Measuring the Accuracy of Aggregates Computed from a Statistical Register

Published Online: 22 Jun 2021
Page range: 481 - 503
Received: 01 May 2019
Accepted: 01 Jan 2021
Journal Details
First Published
01 Oct 2013
Publication timeframe
4 times per year

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.


Alleva, G., and F. Petrarca. 2013. “New indicators for investigating the Integration of Sapienza graduates into the labor market.” Working papers n. 120/2013 del Dipartimento Memotef, ISSN 2239-608X.Search in Google Scholar

Alleva, G. 2017. “The new role of sample surveys in official statistics.” ITACOSM 2017, The 5th Italian Conference on Survey Methodology, June 14, 2017, Bologna, Italy. Available at: https://www.istat.it/it/files//2015/10/Alleva_ITACOSM_14062017.pdf (accesssed May 2021).Search in Google Scholar

Biemer, P.P. 2010. “Total Survey Error Design, implementation, and evaluation.” Public Opinion Quarterly 4(5) : 817–848. DOI: https://doi.org/10.1093/poq/nfq058.Search in Google Scholar

Binder, D.A., and Z. Patak. 1994. “Use of estimating functions for estimation from complex surveys.” Journal of the American Statistical Association 89: 1035–1043. DOI: https://doi.org/10.1080/01621459.1994.10476839.Search in Google Scholar

Breidt, F.J., and J.D. Opsomer. 2017. “Model-Assisted Survey Estimation with Modern Prediction Techniques.” Statistical Science 32(2) : 190–205. DOI: https://doi.org/10.1214/16-STS589.Search in Google Scholar

Chambers, R.L., and R.G. Clark. 2015. “An Introduction to Model-Based Sampling with Applications.” Oxford Statistical Science. 37. DOI: https://doi.org/10.1093/acprof:oso/9780198566625.001.0001.Search in Google Scholar

Chen, S., and D. Haziza. 2017. “Multiply robust imputation procedures for the treatment of item nonresponse in surveys.” Biometrika 102: 439–453. DOI: https://doi.org/10.1007/s40300-017-0128-9.Search in Google Scholar

Citro, C.F. 2014. “From multiple modes for surveys to multiple data sources for estimates.” Survey Methodology. Statistics Canada. Available at: https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2014002/article/14128-eng.pdf?st=emZzAE9_ (accessed May 2021).Search in Google Scholar

Cochran, W.G. 1977. Sampling techniques, (Third edition). New York: Wiley. Available at: https://glad.geog.umd.edu/Potapov/_Library/Cochran_1977_Sampling_Techniques_Third_Edition.pdf (accessed May 2021).Search in Google Scholar

Deville, J.-C. 1999. “Variance estimation for complex statistics and estimators: Linearization and residual techniques.” Survey Methodology 25(2) : 193–203. Statistics Canada. Available at: https://www150.statcan.gc.ca/n1/en/pub/12-001-x/1999002/article/4882-eng.pdf?st=qNANeGtP (accessed May 2021).Search in Google Scholar

Deville, J.-C., and Y. Tillè. 2005. “Variance approximation under balanced sampling.” Journal of Statistical Planning and Inference 128(2) : 569–591. Available at: https://core.ac.uk/download/pdf/43673958.pdf (accessed May 2021).Search in Google Scholar

Eurostat. 2019. Available at: https://ec.europa.eu/eurostat/cros/content/essnet-quality-multisource-statistics-komuso_en (accessed May 2021).Search in Google Scholar

Falorsi, P.D., P. Lavallée, and P. Righi. 2019. “Cost Optimal Sampling for the Integrated Observation of Different Populations.” Survey Methodology. Available at: https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2019003/article/00004-eng.pdf?st=qZIAruhQ (accessed May 2021).Search in Google Scholar

FAO. 2014. Technical Report on the Integrated Survey Framework, Technical Report Series GO-02-2014. Available at: http://gsars.org/wp-content/uploads/2014/07/Technical_report_on-ISF-Final.pdf (accessed May 2021).Search in Google Scholar

Graf, M. 2015. A Simplified Approach to Linearization Variance for Surveys. Technical Report, Institut De Statistique, Université de Neuchâtel. Available at: https://doc.rero.ch/record/324723/files/00002754.pdf (accessed May 20121).Search in Google Scholar

Gruppo UNI.CO. 2015. La Domanda di Lavoro per i laureati. I risultati dell’integrazione tra gli archivi amministrativi dell’Universita‘ Sapienza di Roma e del Ministero del Lavoro e delle Politiche Sociali, Edizioni Nuova Cultura-Roma. ISBN 9788868124816. DOI: https://doi.org/10.4458/4816.Search in Google Scholar

Isaki, C., and W.A. Fuller. 1982. “Survey design under the regression superpopulation model.” Journal of the American Statistical Association 77: 89–96.Search in Google Scholar

ISCO 1-ISCO 2. Available at: https://www.ilo.org/wcmsp5/groups/public/@dgre-ports/@dcomm/@publ/documents/publication/wcms_172572.pdfSearch in Google Scholar

Istat. 2016. Istat’s Modernisation Programme. Available at: https://www.istat.it/en/files/2011/04/IstatsModernistionProgramme_EN.pdf (accessed May 2021).Search in Google Scholar

Kendall, M.G, and A. Stuart. 1976. The Advanced Theory of Statistics: Design and analysis, and time-series. Hafner.Search in Google Scholar

Kim, J.K., and J.N.K. Rao. 2012. “Combining data from two independent surveys: a model-assisted approach.” Biometrika 99(1) : 85–100. DOI: https://doi.org/10.1093/-biomet/asr063.Search in Google Scholar

Nedyalkova, D., and Y. Tillé. 2008. “Optimal sampling and estimation strategies under the linear model.” Biometrika 95: 521–537. DOI: https://doi.org/10.1093/biomet/asn027.Search in Google Scholar

Nirel, R., and H. Glickman. 2009. “Chapter 21 – Sample Surveys and Censuses.” In Handbook of Statistics, edited by C.R. Rao.: Elsevier.Search in Google Scholar

Petrarca, F. 2014a. “Non-metric PLS path modeling: Integration into the labour market of Sapienza graduates.” In Advances in latent variables. Studies in theoretical and applied statistics: 159–170. Berlin: Springer. DOI: https://doi.org/10.1007/10104_2014_16Search in Google Scholar

Petrarca, F. 2014b. Assessing Sapienza University alumni job careers: Enhanced partial least squares latent variable path models for the analysis of the UNI.CO administrative archive. PhD diss., Dipartimento di Economia dell’Universita‘ degli studi Roma Tre. Available at: http://hdl.handle.net/2307/4167 (accessed May 2021).Search in Google Scholar

Pfeffermann, D. 2015. “Methodological Issues and Challenges in the Production of Official Statistics: 24th Annual Morris Hansen Lecture.” Journal of Survey Statistics and Methodology 3(4) : 425–483. DOI: https://doi.org/10.1093/jssam/smv035.Search in Google Scholar

Righi, P., P.D. Falorsi, S. Daddi, E. Fiorello, P. Massoli, and M.D. Terribili. 2021. “Optimal sampling for the Population Coverage Survey of the new Italian Register Based Census.” Journal of Official Statistics (September 2021)Search in Google Scholar

Särndal, C.E., B. Swensson, and J. Wretman. 1992. Model Assisted Survey Sampling. Springer-Verlag.Search in Google Scholar

Scholtus, S. 2019. “A bootstrap method for estimators based on combined administrative and survey data.” In NTTS Conference 2019. Brussels, Belgium. Available at: https://slidetodoc.com/download.php?id=4386.Search in Google Scholar

Statistics Canada. 2009. Statistics Canada Quality Guidelines, (6th edition). Available at: https://www150.statcan.gc.ca/n1/en/pub/12-539-x/12-539-x2019001-eng.pdf?st=y2AqFuiY.Search in Google Scholar

Wallgren, A., and B. Wallgren. 2014. Register-based Statistics: statistical methods for administrative data, (2nd Edition). Chichester: Wiley.Search in Google Scholar

Wolter, K.M. 1985. Introduction to Variance Estimation. New York: Springer.Search in Google Scholar

Wolter, K.M. 1986. “Some Coverage Error Models for Census Data.” Journal of the American Statistical Association 81: 338–346. DOI: https://doi.org/10.2307/2289222.Search in Google Scholar

Vaillant, R. 2009. “Model based predictions of finite population totals.” In Chapter 23 in Handbook of statistics 29: Design, Methods and Applications, edited by P. Pfefferman and C.R. Rao. Amsterdam: North Holland.Search in Google Scholar

Vallée, A.A., and Y. Tillé. 2019. “Linearisation for Variance Estimation by Means of Sampling Indicators: Application to Non-response.” International Statistical Review 0(0) : 1–21. DOI: https://doi.org/10.1111/insr.12313.Search in Google Scholar

Ziegler, A. 2015. Generalized Estimating Expressions, Springer and Verlag. Lecture Notes in statistics.Search in Google Scholar

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