IDEAS home Printed from https://ideas.repec.org/p/bea/wpaper/0117.html
   My bibliography  Save this paper

The Statistical Reconciliation of Time Series of Accounts after a Benchmark Revision

Author

Listed:
  • Baoline Chen
  • Tommaso Di Fonzo
  • Thomas Howells
  • Marco Marini

    (Bureau of Economic Analysis)

Abstract

No abstract is available for this item.

Suggested Citation

  • Baoline Chen & Tommaso Di Fonzo & Thomas Howells & Marco Marini, 2014. "The Statistical Reconciliation of Time Series of Accounts after a Benchmark Revision," BEA Working Papers 0117, Bureau of Economic Analysis.
    • Handle: RePEc:bea:wpaper:0117
    as

    Download full text from publisher

    File URL: https://www.bea.gov/system/files/papers/WP2015-1.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tommaso Di Fonzo & Marco Marini, 2011. "Simultaneous and two‐step reconciliation of systems of time series: methodological and practical issues," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 60(2), pages 143-164, March.
    2. Baoline Chen, 2012. "A Balanced System of U.S. Industry Accounts and Distribution of the Aggregate Statistical Discrepancy by Industry," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(2), pages 202-211, February.
    3. Reinier Bikker & Jacco Daalmans & Nino Mushkudiani, 2013. "Benchmarking Large Accounting Frameworks: A Generalized Multivariate Model," Economic Systems Research, Taylor & Francis Journals, vol. 25(4), pages 390-408, December.
    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. Baoline Chen & Tommaso Di Fonzo & Thomas Howells & Marco Marini, 2018. "The statistical reconciliation of time series of accounts between two benchmark revisions," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 533-552, November.
    2. Geoffrey Brent, 2018. "Maximum likelihood estimation framework for table‐balancing adjustments," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 520-532, November.

    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. Baoline Chen & Tommaso Di Fonzo & Thomas Howells & Marco Marini, 2018. "The statistical reconciliation of time series of accounts between two benchmark revisions," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 533-552, November.
    2. Enrique M. Quilis, 2018. "Temporal disaggregation of economic time series: The view from the trenches," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 447-470, November.
    3. Umed Temursho, 2018. "Entropy‐based benchmarking methods," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 421-446, November.
    4. Víctor M. Guerrero & Francisco Corona, 2018. "Retropolating some relevant series of Mexico's System of National Accounts at constant prices: The case of Mexico City's GDP," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 495-519, November.
    5. Tucker McElroy, 2018. "Seasonal adjustment subject to accounting constraints," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 574-589, November.
    6. Geoffrey Brent, 2018. "Maximum likelihood estimation framework for table‐balancing adjustments," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 520-532, November.
    7. George Athanasopoulos & Rob J Hyndman & Nikolaos Kourentzes & Anastasios Panagiotelis, 2023. "Forecast Reconciliation: A Review," Monash Econometrics and Business Statistics Working Papers 8/23, Monash University, Department of Econometrics and Business Statistics.
    8. Luboš Marek & Stanislava Hronová & Richard Hindls, 2017. "Option For Predicting The Czech Republic'S Foreign Trade Time Series As Components In Gross Domestic Product," Statistics in Transition New Series, Polish Statistical Association, vol. 18(3), pages 481-500, September.
    9. Marta Crispino & Vincenzo Mariani, 2023. "A tool to nowcast tourist overnight stays with payment data and complementary indicators," Questioni di Economia e Finanza (Occasional Papers) 746, Bank of Italy, Economic Research and International Relations Area.
    10. Suad Elezović & Yingfu Xie, 2018. "Reconciliation of seasonally adjusted data with applications to the Swedish quarterly national accounts," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 590-602, November.
    11. Bikker Reinier & van den Brakel Jan & Krieg Sabine & Ouwehand Pim & van der Stegen Ronald, 2019. "Consistent Multivariate Seasonal Adjustment for Gross Domestic Product and its Breakdown in Expenditures," Journal of Official Statistics, Sciendo, vol. 35(1), pages 9-30, March.
    12. Kazemier Brugt & van Veen Michel & IJmker Sander, 2019. "The unobserved economy and the Dutch national accounts after the benchmark revision 2015," Central European Economic Journal, Sciendo, vol. 6(53), pages 1-24, January.
    13. Ton de Waal & Arnout van Delden & Sander Scholtus, 2020. "Multi‐source Statistics: Basic Situations and Methods," International Statistical Review, International Statistical Institute, vol. 88(1), pages 203-228, April.
    14. Nino Mushkudiani & Jacco Daalmans & Reinier Bikker, 2018. "Solving large‐data consistency problems at Statistics Netherlands using macro‐integration techniques," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 553-573, November.
    15. Hyndman, Rob J. & Lee, Alan J. & Wang, Earo, 2016. "Fast computation of reconciled forecasts for hierarchical and grouped time series," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 16-32.
    16. Tommaso Fonzo & Marco Marini, 2015. "Reconciliation of systems of time series according to a growth rates preservation principle," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(4), pages 651-669, November.
    17. Danny Pfeffermann & Anna Sikov & Richard Tiller, 2014. "Single- and two-stage cross-sectional and time series benchmarking procedures for small area estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(4), pages 631-666, December.
    18. Marek Luboš & Hronová Stanislava & Hindis Richard, 2017. "Option for Predicting the Czech Republic’S Foreign Trade Time Series as Components in Gross Domestic Product," Statistics in Transition New Series, Polish Statistical Association, vol. 18(3), pages 481-500, September.

    More about this item

    JEL classification:

    • E60 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - General

    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:bea:wpaper:0117. 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: Andrea Batch (email available below). General contact details of provider: https://edirc.repec.org/data/beagvus.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.