IDEAS home Printed from https://ideas.repec.org/p/bca/bocadp/24-17.html
   My bibliography  Save this paper

Seasonal Adjustment of Weekly Data

Author

Listed:
  • Jeffrey Mollins
  • Rachit Lumb

Abstract

This paper summarizes and assesses several of the most popular methods to seasonally adjust weekly data. The industry standard approach, known as X-13ARIMA-SEATS, is suitable only for monthly or quarterly data. Given the increased availability and promise of non-traditional data at higher frequencies, alternative approaches are required to extract relevant signals for monitoring and analysis. This paper reviews four such methods for high-frequency seasonal adjustment. We find that tuning the parameters of each method helps deliver a properly adjusted series. We optimize using a grid search and test for residual seasonality in each series. While no method works perfectly for every series, some methods are generally effective at removing seasonality in weekly data, despite the increased difficulty of accounting for the shock of the COVID-19 pandemic. Because seasonally adjusting high-frequency data is typically a difficult task, we recommend closely inspecting each series and comparing results from multiple methods whenever possible.

Suggested Citation

  • Jeffrey Mollins & Rachit Lumb, 2024. "Seasonal Adjustment of Weekly Data," Discussion Papers 2024-17, Bank of Canada.
  • Handle: RePEc:bca:bocadp:24-17
    as

    Download full text from publisher

    File URL: https://www.bankofcanada.ca/2024/11/staff-discussion-paper-2024-17/
    File Function: Abstract
    Download Restriction: no

    File URL: https://www.bankofcanada.ca/wp-content/uploads/2024/11/sdp2024-17.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Daniel J. Lewis & Karel Mertens & James H. Stock & Mihir Trivedi, 2022. "Measuring real activity using a weekly economic index," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 667-687, June.
    2. Ollech, Daniel, 2021. "Economic analysis using higher frequency time series: Challenges for seasonal adjustment," Discussion Papers 53/2021, Deutsche Bundesbank.
    Full references (including those not matched with items on IDEAS)

    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. Miescu, Mirela & Rossi, Raffaele, 2021. "COVID-19-induced shocks and uncertainty," European Economic Review, Elsevier, vol. 139(C).
    2. Diane Alexander & Ezra Karger, 2023. "Do Stay-at-Home Orders Cause People to Stay at Home? Effects of Stay-at-Home Orders on Consumer Behavior," The Review of Economics and Statistics, MIT Press, vol. 105(4), pages 1017-1027, July.
    3. Tyler Atkinson & Jim Dolmas & Christoffer Koch & Evan F. Koenig & Karel Mertens & Anthony Murphy & Kei-Mu Yi, 2020. "Mobility and Engagement Following the SARS-Cov-2 Outbreak," Working Papers 2014, Federal Reserve Bank of Dallas.
    4. Christiane Baumeister & Danilo Leiva-León & Eric Sims, 2024. "Tracking Weekly State-Level Economic Conditions," The Review of Economics and Statistics, MIT Press, vol. 106(2), pages 483-504, March.
    5. Frank Schorfheide & Dongho Song, 2024. "Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic," International Journal of Central Banking, International Journal of Central Banking, vol. 20(4), pages 275-320, October.
    6. James Mitchell & Gary Koop & Stuart McIntyre & Aubrey Poon, 2020. "Reconciled Estimates of Monthly GDP in the US," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2020-16, Economic Statistics Centre of Excellence (ESCoE).
    7. Francesco Casalena, 2024. "Back to normal? Assessing the Effects of the Federal Reserve's Quantitative Tightening," IHEID Working Papers 14-2024, Economics Section, The Graduate Institute of International Studies.
    8. Toru Kitagawa & Weining Wang & Mengshan Xu, 2024. "Policy choice in time series by empirical welfare maximization," CeMMAP working papers 27/24, Institute for Fiscal Studies.
    9. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2022. "Nowcasting tail risk to economic activity at a weekly frequency," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 843-866, August.
    10. Bricongne, Jean-Charles & Meunier, Baptiste & Pouget, Sylvain, 2023. "Web-scraping housing prices in real-time: The Covid-19 crisis in the UK," Journal of Housing Economics, Elsevier, vol. 59(PB).
    11. Zheng, Chen & Zhang, Junru, 2021. "The impact of COVID-19 on the efficiency of microfinance institutions," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 407-423.
    12. Laura Felber & Simon Beyeler, 2023. "Nowcasting economic activity using transaction payments data," Working Papers 2023-01, Swiss National Bank.
    13. 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.
    14. Baek, ChaeWon & McCrory, Peter B & Messer, Todd & Mui, Preston, 2020. "Unemployment Effects of Stay-at-Home Orders: Evidence from High Frequency Claims Data," Institute for Research on Labor and Employment, Working Paper Series qt042177j7, Institute of Industrial Relations, UC Berkeley.
    15. Tomaz Cajner & Leland D. Crane & Ryan A. Decker & John Grigsby & Adrian Hamins-Puertolas & Erik Hurst & Christopher Kurz & Ahu Yildirmaz, 2020. "The US Labor Market during the Beginning of the Pandemic Recession," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 51(2 (Summer), pages 3-33.
    16. 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.
    17. Eraslan, Sercan & Reif, Magnus, 2023. "A latent weekly GDP indicator for Germany," Technical Papers 08/2023, Deutsche Bundesbank.
    18. Barend Abeln & Jan P. A. M. Jacobs, 2023. "COVID-19 and Seasonal Adjustment," SpringerBriefs in Economics, in: Seasonal Adjustment Without Revisions, chapter 0, pages 53-61, Springer.
    19. Kong, Edward & Prinz, Daniel, 2020. "Disentangling policy effects using proxy data: Which shutdown policies affected unemployment during the COVID-19 pandemic?," Journal of Public Economics, Elsevier, vol. 189(C).
    20. Woloszko, Nicolas, 2024. "Nowcasting with panels and alternative data: The OECD weekly tracker," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1302-1335.

    More about this item

    Keywords

    Econometric and statistical methods;

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth

    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:bca:bocadp:24-17. 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/bocgvca.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.