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Measuring and Communicating the Uncertainty in Official Economic Statistics

Author

Listed:
  • Mazzi Gian Luigi

    (Freelance consultant.)

  • Mitchell James

    (Federal Reserve Bank of Cleveland, 1455 E 6th St, Cleveland, Ohio, 44114, USA.)

  • Carausu Florabela

    (GOPA Luxembourg, Rue Luxembourg, Bereldange, 7240, Luxembourg.)

Abstract

Official economic statistics are uncertain even if not always interpreted or treated as such. From a historical perspective, this article reviews different categorisations of data uncertainty, specifically the traditional typology that distinguishes sampling from nonsampling errors and a newer typology of Manski (2015). Throughout, the importance of measuring and communicating these uncertainties is emphasised, as hard as it can prove to measure some sources of data uncertainty, especially those relevant to administrative and big data sets. Accordingly, this article both seeks to encourage further work into the measurement and communication of data uncertainty in general and to introduce the Comunikos (COMmunicating UNcertainty In Key Official Statistics) project at Eurostat. Comunikos is designed to evaluate alternative ways of measuring and communicating data uncertainty specifically in contexts relevant to official economic statistics.

Suggested Citation

  • Mazzi Gian Luigi & Mitchell James & Carausu Florabela, 2021. "Measuring and Communicating the Uncertainty in Official Economic Statistics," Journal of Official Statistics, Sciendo, vol. 37(2), pages 289-316, June.
    • Handle: RePEc:vrs:offsta:v:37:y:2021:i:2:p:289-316:n:6
    DOI: 10.2478/jos-2020-0013
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    References listed on IDEAS

    as
    1. Silvia Lui & James Mitchell & Martin Weale, 2011. "Qualitative business surveys: signal or noise?," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(2), pages 327-348, April.
    2. Severin Bernhard, 2016. "A real-time GDP data set for Switzerland," Economic Studies 2016-09, Swiss National Bank.
    3. John Geweke, 1978. "The Temporal and Sectoral Aggregation of Seasonally Adjusted Time Series," NBER Chapters, in: Seasonal Analysis of Economic Time Series, pages 411-432, National Bureau of Economic Research, Inc.
    4. Croushore, Dean & Stark, Tom, 2001. "A real-time data set for macroeconomists," Journal of Econometrics, Elsevier, vol. 105(1), pages 111-130, November.
    5. Domenico Giannone & Jérôme Henry & Magdalena Lalik & Michele Modugno, 2012. "An Area-Wide Real-Time Database for the Euro Area," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1000-1013, November.
    6. Reza Modarres & Joseph L. Gastwirth, 2006. "A Cautionary Note on Estimating the Standard Error of the Gini Index of Inequality," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 68(3), pages 385-390, June.
    7. Philipp Bagus, 2011. "Morgenstern's Forgotten Contribution: A Stab to the Heart of Modern Economics," American Journal of Economics and Sociology, Wiley Blackwell, vol. 70(2), pages 540-562, April.
    8. Galvao, Ana Beatriz & Mitchell, James & Runge, Johnny, 2019. "Communicating Data Uncertainty: Experimental Evidence for U.K. GDP," EMF Research Papers 30, Economic Modelling and Forecasting Group.
    9. Marcel Boumans, 2012. "Observations in a Hostile Environment: Morgenstern on the Accuracy of Economic Observations," History of Political Economy, Duke University Press, vol. 44(5), pages 114-136, Supplemen.
    10. Giovanni Maria Giorgi & Chiara Gigliarano, 2017. "The Gini Concentration Index: A Review Of The Inference Literature," Journal of Economic Surveys, Wiley Blackwell, vol. 31(4), pages 1130-1148, September.
    11. Domenico Piccolo & Rosaria Simone, 2019. "The class of cub models: statistical foundations, inferential issues and empirical evidence," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 389-435, September.
    12. Tomson Ogwang, 2000. "A Convenient Method of Computing the Gini Index and its Standard Error," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 62(1), pages 123-129, February.
    13. Charles F. Manski, 2015. "Communicating Uncertainty in Official Economic Statistics: An Appraisal Fifty Years after Morgenstern," Journal of Economic Literature, American Economic Association, vol. 53(3), pages 631-653, September.
    14. repec:taf:jnlbes:v:30:y:2012:i:2:p:181-190 is not listed on IDEAS
    15. Elias Karagiannis & Milorad Kovacevic', 2000. "A Method to Calculate the Jackknife Variance Estimator For the Gini Coefficient," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 62(1), pages 119-122, February.
    16. Richard J. Smith & Martin R. Weale & Steven E. Satchell, 1998. "Measurement Error with Accounting Constraints: Point and Interval Estimation for Latent Data with an Application to U.K. Gross Domestic Product," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(1), pages 109-134.
    17. Karagiannis, Elias & Kovacevic', Milorad, 2000. "A Method to Calculate the Jackknife Variance Estimator for the Gini Coefficient," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 62(1), pages 119-122, February.
    18. Dean Croushore & Tom Stark, 2003. "A Real-Time Data Set for Macroeconomists: Does the Data Vintage Matter?," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 605-617, August.
    19. repec:taf:jnlbes:v:30:y:2012:i:2:p:173-180 is not listed on IDEAS
    20. Yitzhaki, Shlomo, 1991. "Calculating Jackknife Variance Estimators for Parameters of the Gini Method," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(2), pages 235-239, April.
    21. Qin, Yongsong & Rao, J.N.K. & Wu, Changbao, 2010. "Empirical likelihood confidence intervals for the Gini measure of income inequality," Economic Modelling, Elsevier, vol. 27(6), pages 1429-1435, November.
    22. Chow, Gregory C & Lin, An-loh, 1971. "Best Linear Unbiased Interpolation, Distribution, and Extrapolation of Time Series by Related Series," The Review of Economics and Statistics, MIT Press, vol. 53(4), pages 372-375, November.
    23. Manski, Charles F., 2016. "Credible interval estimates for official statistics with survey nonresponse," Journal of Econometrics, Elsevier, vol. 191(2), pages 293-301.
    24. James Mitchell & Richard J. Smith & Martin R. Weale & Stephen Wright & Eduardo L. Salazar, 2005. "An Indicator of Monthly GDP and an Early Estimate of Quarterly GDP Growth," Economic Journal, Royal Economic Society, vol. 115(501), pages 108-129, February.
    25. Richard McKenzie, 2006. "Undertaking Revisions and Real-Time Data Analysis using the OECD Main Economic Indicators Original Release Data and Revisions Database," OECD Statistics Working Papers 2006/2, OECD Publishing.
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