IDEAS home Printed from https://ideas.repec.org/p/bdi/opques/qef_605_21.html
   My bibliography  Save this paper

Covid-19 and official statistics: a wakeup call?

Author

Listed:
  • Claudia Biancotti

    (Bank of Italy)

  • Alfonso Rosolia

    (Bank of Italy)

  • Giovanni Veronese

    (Bank of Italy)

  • Robert Kirchner

    (Deutsche Bundesbank)

  • Francois Mouriaux

    (Banque de France)

Abstract

As COVID-19 spread globally, fast political decisions and the implementation of drastic measures were necessary to slow down proliferation and counter the economic disruption. The demand for broad, timely, high-frequency statistics about economic and health developments surged. At the same time, the pandemic outpaced the frequency at which most conventional statistics become available. Unconventional data helped to bridge these time lags, and to supply information on aspects of society not suitably covered by traditional official statistics, but that the need of the day suddenly made prominent for decision makers. The lesson from the COVID-19 crisis is that greater preparedness and flexibility in facing �future unknowns� is essential. Enabling users of statistics to quickly tap on data dimensions and relationships needed for their decisions when confronted with exceptional circumstances, is essential for guaranteeing salience and, ultimately, trustworthiness of official statistics.

Suggested Citation

  • Claudia Biancotti & Alfonso Rosolia & Giovanni Veronese & Robert Kirchner & Francois Mouriaux, 2021. "Covid-19 and official statistics: a wakeup call?," Questioni di Economia e Finanza (Occasional Papers) 605, Bank of Italy, Economic Research and International Relations Area.
  • Handle: RePEc:bdi:opques:qef_605_21
    as

    Download full text from publisher

    File URL: https://www.bancaditalia.it/pubblicazioni/qef/2021-0605/QEF_605_21.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Valentina Aprigliano & Guerino Ardizzi & Libero Monteforte, 2019. "Using Payment System Data to Forecast Economic Activity," International Journal of Central Banking, International Journal of Central Banking, vol. 15(4), pages 55-80, October.
    2. Bodo, Giorgio & Signorini, Luigi Federico, 1987. "Short-term forecasting of the industrial production index," International Journal of Forecasting, Elsevier, vol. 3(2), pages 245-259.
    3. Andrew Gelman & Christian Hennig, 2017. "Beyond subjective and objective in statistics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(4), pages 967-1033, October.
    4. Tobias Cagala, 2017. "Improving data quality and closing data gaps with machine learning," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Data needs and Statistics compilation for macroprudential analysis, volume 46, Bank for International Settlements.
    5. Claudia Biancotti & Paolo Ciocca, 2019. "Opening Internet Monopolies to Competition with Data Sharing Mandates," Policy Briefs PB19-3, Peterson Institute for International Economics.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Irving Fisher Committee, 2021. "Issues in Data Governance," IFC Bulletins, Bank for International Settlements, number 54, July.
    2. Webel, Karsten, 2022. "A review of some recent developments in the modelling and seasonal adjustment of infra-monthly time series," Discussion Papers 31/2022, Deutsche Bundesbank.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Valentina Aprigliano & Guerino Ardizzi & Alessia Cassetta & Alessandro Cavallero & Simone Emiliozzi & Alessandro Gambini & Nazzareno Renzi & Roberta Zizza, 2021. "Exploiting payments to track Italian economic activity: the experience at Banca d’Italia," Questioni di Economia e Finanza (Occasional Papers) 609, Bank of Italy, Economic Research and International Relations Area.
    2. Delbianco Fernando & Tohmé Fernando, 2023. "What is a relevant control?: An algorithmic proposal," Asociación Argentina de Economía Política: Working Papers 4643, Asociación Argentina de Economía Política.
    3. Costantini, Mauro & Pappalardo, Carmine, 2010. "A hierarchical procedure for the combination of forecasts," International Journal of Forecasting, Elsevier, vol. 26(4), pages 725-743, October.
    4. Riccardo De Bonis & Matteo Piazza, 2021. "A silent revolution. How central bank statistics have changed in the last 25 years," PSL Quarterly Review, Economia civile, vol. 74(299), pages 347-371.
    5. James Chapman & Ajit Desai, 2021. "Using Payments Data to Nowcast Macroeconomic Variables During the Onset of COVID-19," Staff Working Papers 21-2, Bank of Canada.
    6. Piero Demetrio Falorsi & Giorgio Alleva & Fabio Bacchini & Roberto Iannaccone, 2005. "Estimates based on preliminary data from a specific subsample and from respondents not included in the subsample," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 14(1), pages 83-99, February.
    7. Roma, Antonio & Pirino, Davide, 2009. "The extraction of natural resources: The role of thermodynamic efficiency," Ecological Economics, Elsevier, vol. 68(10), pages 2594-2606, August.
    8. Francesco De Pretis & Barbara Osimani, 2019. "New Insights in Computational Methods for Pharmacovigilance: E-Synthesis , a Bayesian Framework for Causal Assessment," IJERPH, MDPI, vol. 16(12), pages 1-19, June.
    9. Valentina Aprigliano & Alessandro Borin & Francesco Paolo Conteduca & Simone Emiliozzi & Marco Flaccadoro & Sabina Marchetti & Stefania Villa, 2021. "Forecasting Italian GDP growth with epidemiological data," Questioni di Economia e Finanza (Occasional Papers) 664, Bank of Italy, Economic Research and International Relations Area.
    10. Prol, Javier López & O, Sungmin, 2020. "Impact of COVID-19 Measures on Short-Term Electricity Consumption in the Most Affected EU Countries and USA States," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 23(10).
    11. Francesco Cusano & Giuseppe Marinelli & Stefano Piermattei, 2022. "Learning from revisions: an algorithm to detect errors in banks’ balance sheet statistical reporting," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(6), pages 4025-4059, December.
    12. Kohei Matsumura & Yusuke Oh & Tomohiro Sugo & Koji Takahashi, "undated". "Nowcasting Economic Activity with Mobility Data," Bank of Japan Working Paper Series 21-E-2, Bank of Japan.
    13. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2022. "Machine Learning Time Series Regressions With an Application to Nowcasting," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1094-1106, June.
    14. Tatjana Dahlhaus & Angelika Welte, 2021. "Payment Habits During COVID-19: Evidence from High-Frequency Transaction Data," Staff Working Papers 21-43, Bank of Canada.
    15. Thury, Gerhard & Witt, Stephen F., 1998. "Forecasting industrial production using structural time series models," Omega, Elsevier, vol. 26(6), pages 751-767, December.
    16. Gerhard Fenz & Helmut Stix, 2021. "Monitoring the economy in real time with the weekly OeNB GDP indicator: background, experience and outlook," Monetary Policy & the Economy, Oesterreichische Nationalbank (Austrian Central Bank), issue Q4/20-Q1/, pages 17-40.
    17. Mihnea Constantinescu, 2023. "Sparse Warcasting," Working Papers 01/2023, National Bank of Ukraine.
    18. Guerino Ardizzi & Andrea Nobili & Giorgia Rocco, 2020. "A game changer in payment habits: evidence from daily data during a pandemic," Questioni di Economia e Finanza (Occasional Papers) 591, Bank of Italy, Economic Research and International Relations Area.
    19. James Chapman & Ajit Desai, 2022. "Macroeconomic Predictions Using Payments Data and Machine Learning," Staff Working Papers 22-10, Bank of Canada.
    20. Riccardo Corradini, 2019. "A Set of State–Space Models at a High Disaggregation Level to Forecast Italian Industrial Production," J, MDPI, vol. 2(4), pages 1-53, November.

    More about this item

    Keywords

    high frequency statistics; data access; official statistics;
    All these keywords.

    JEL classification:

    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • F60 - International Economics - - Economic Impacts of Globalization - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bdi:opques:qef_605_21. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/bdigvit.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.