IDEAS home Printed from https://ideas.repec.org/p/bkr/wpaper/wps76.html
   My bibliography  Save this paper

A Real-Time Historical Database of Macroeconomic Indicators for Russia

Author

Listed:
  • Dmitry Gornostaev

    (Bank of Russia, Russian Federation)

  • Alexey Ponomarenko

    (Bank of Russia, Russian Federation)

  • Sergei Seleznev

    (Bank of Russia, Russian Federation)

  • Alexandra Sterkhova

    (Bank of Russia, Russian Federation)

Abstract

We compile a database that contains data vintages of a large collection of short-term economic indicators. The main result of the work is a database which is available as an electronic annex to this working paper. The Research and Forecasting Department of the Bank of Russia plans to update this database in the future. We also perform an illustrative analysis of the properties of the revisions for a number of indicators. The preliminary results indicate that the magnitude of the revisions is in many cases substantial.

Suggested Citation

  • Dmitry Gornostaev & Alexey Ponomarenko & Sergei Seleznev & Alexandra Sterkhova, 2021. "A Real-Time Historical Database of Macroeconomic Indicators for Russia," Bank of Russia Working Paper Series wps76, Bank of Russia.
    • Handle: RePEc:bkr:wpaper:wps76
    as

    Download full text from publisher

    File URL: https://www.cbr.ru/StaticHtml/File/124609/WP76.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. Kevin Lee & Nilss Olekalns & Kalvinder Shields & Zheng Wang, 2012. "Australian Real-Time Database: An Overview and an Illustration of its Use in Business Cycle Analysis," The Economic Record, The Economic Society of Australia, vol. 88(283), pages 495-516, December.
    3. Croushore, Dean & Stark, Tom, 2001. "A real-time data set for macroeconomists," Journal of Econometrics, Elsevier, vol. 105(1), pages 111-130, November.
    4. Ekaterina Astafieva & Marina Turuntseva, 2021. "Revisions of GDP: Data and Assessment of Statistical Properties," HSE Economic Journal, National Research University Higher School of Economics, vol. 25(1), pages 65-101.
    5. Adriana Fernandez & Evan F. Koenig & Alex Nikolsko-Rzhevskyy, 2011. "A real-time historical database for the OECD," Globalization Institute Working Papers 96, Federal Reserve Bank of Dallas.
    6. Athanasios Orphanides, 2001. "Monetary Policy Rules Based on Real-Time Data," American Economic Review, American Economic Association, vol. 91(4), pages 964-985, September.
    7. Denis Shibitov & Mariam Mamedli, 2021. "Forecasting Russian Cpi With Data Vintages And Machine Learning Techniques," Bank of Russia Working Paper Series wps70, Bank of Russia.
    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. Denis Shibitov & Mariam Mamedli, 2021. "Forecasting Russian Cpi With Data Vintages And Machine Learning Techniques," Bank of Russia Working Paper Series wps70, Bank of Russia.
    2. Michael Zhemkov, 2022. "Assessment of Monthly GDP Growth Using Temporal Disaggregation Methods," Russian Journal of Money and Finance, Bank of Russia, vol. 81(2), pages 79-104, June.
    3. Urmat Dzhunkeev, 2022. "Forecasting Unemployment in Russia Using Machine Learning Methods," Russian Journal of Money and Finance, Bank of Russia, vol. 81(1), pages 73-87, March.

    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. Denis Shibitov & Mariam Mamedli, 2021. "Forecasting Russian Cpi With Data Vintages And Machine Learning Techniques," Bank of Russia Working Paper Series wps70, Bank of Russia.
    2. Dibiasi, Andreas & Sarferaz, Samad, 2023. "Measuring macroeconomic uncertainty: A cross-country analysis," European Economic Review, Elsevier, vol. 153(C).
    3. Nicolas Pinkwart, 2011. "Zur Stabilität von Saisonbereinigungsverfahren: Eine Echtzeitdaten-Analyse am Beispiel BV4.1 und X-12-ARIMA," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 5(2), pages 125-144, August.
    4. Ronald Indergand & Stefan Leist, 2014. "A Real-Time Data Set for Switzerland," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 150(IV), pages 331-352, December.
    5. Andreas Dibiasi & Samad Sarferaz, 2020. "Measuring Macroeconomic Uncertainty: The Labor Channel of Uncertainty from a Cross-Country Perspective," Papers 2006.09007, arXiv.org, revised Dec 2020.
    6. Emilia Tomczyk, 2013. "End of sample vs. real time data: perspectives for analysis of expectations," Working Papers 68, Department of Applied Econometrics, Warsaw School of Economics.
    7. Paredes, Joan & Pedregal, Diego J. & Pérez, Javier J., 2009. "A quarterly fiscal database for the euro area based on intra-annual fiscal information," Working Paper Series 1132, European Central Bank.
    8. Jacopo Cimadomo, 2016. "Real-Time Data And Fiscal Policy Analysis: A Survey Of The Literature," Journal of Economic Surveys, Wiley Blackwell, vol. 30(2), pages 302-326, April.
    9. Marek RUSNAK, 2013. "Revisions to the Czech National Accounts: Properties and Predictability," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 63(3), pages 244-261, July.
    10. Ana Beatriz Galvão & James Mitchell, 2023. "Real‐Time Perceptions of Historical GDP Data Uncertainty," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 457-481, June.
    11. Julien Champagne & Guillaume Poulin-Bellisle & Rodrigo Sekkel, 2018. "Evaluating the Bank of Canada Staff Economic Projections Using a New Database of Real-Time Data and Forecasts," Staff Working Papers 18-52, Bank of Canada.
    12. Aguirre, Idoia & Vázquez, Jesús, 2018. "Inflation monitoring in real time: A comparative analysis of the Federal Reserve and the Bank of England," International Review of Economics & Finance, Elsevier, vol. 58(C), pages 200-209.
    13. Bańkowski, Krzysztof & Faria, Thomas & Schall, Robert, 2022. "How well-behaved are revisions to quarterly fiscal data in the euro area?," Working Paper Series 2676, European Central Bank.
    14. Steven P. Cassou & C. Patrick Scott & Jesús Vázquez, 2018. "Optimal monetary policy revisited: does considering US real-time data change things?," Applied Economics, Taylor & Francis Journals, vol. 50(57), pages 6203-6219, December.
    15. Frederick H. Wallace & Gary L. Shelley & Luis F. Cabrera Castellanos, 2004. "Pruebas de la neutralidad monetaria a largo plazo: el caso de Nicaragua," Monetaria, CEMLA, vol. 0(4), pages 407-418, octubre-d.
    16. Croushore, Dean & Evans, Charles L., 2006. "Data revisions and the identification of monetary policy shocks," Journal of Monetary Economics, Elsevier, vol. 53(6), pages 1135-1160, September.
    17. Clements, Michael P. & Beatriz Galvao, Ana, 2010. "Real-time Forecasting of Inflation and Output Growth in the Presence of Data Revisions," Economic Research Papers 270771, University of Warwick - Department of Economics.
    18. Aastveit, Knut Are & Trovik, Tørres, 2014. "Estimating the output gap in real time: A factor model approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(2), pages 180-193.
    19. Clements, Michael P. & Beatriz Galvão, Ana, 2010. "First announcements and real economic activity," European Economic Review, Elsevier, vol. 54(6), pages 803-817, August.
    20. Michael P. Clements & Ana Beatriz Galvão, 2007. "Macroeconomic Forecasting with Mixed Frequency Data: Forecasting US Output Growth," Working Papers 616, Queen Mary University of London, School of Economics and Finance.

    More about this item

    Keywords

    data revisions; data vintages; database; Russia;
    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
    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
    • E2 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment

    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:bkr:wpaper:wps76. 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: BoR Research (email available below). General contact details of provider: https://edirc.repec.org/data/cbrgvru.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.