IDEAS home Printed from https://ideas.repec.org/p/cen/wpaper/23-15.html
   My bibliography  Save this paper

Building the Census Bureau Index of Economic Activity (IDEA)

Author

Listed:
  • Jose Asturias
  • William R. Bell
  • Rebecca Hutchinson
  • Tucker McElroy
  • Katherine J. Thompson

Abstract

The Census Bureau Index of Economic Activity (IDEA) is constructed from 15 of the Census Bureau’s primary monthly economic time series. The index is intended to provide a single time series reflecting, to the extent possible, the variation over time in the whole set of component series. The component series provide monthly measures of activity in retail and wholesale trade, manufacturing, construction, international trade, and business formations. Most of the input series are Principal Federal Economic Indicators. The index is constructed by applying the method of principal components analysis (PCA) to the time series of monthly growth rates of the seasonally adjusted component series, after standardizing the growth rates to series with mean zero and variance 1. Similar PCA approaches have been used for the construction of other economic indices, including the Chicago Fed National Activity Index issued by the Federal Reserve Bank of Chicago, and the Weekly Economic Index issued by the Federal Reserve Bank of New York. While the IDEA is constructed from time series of monthly data, it is calculated and published every business day, and so is updated whenever a new monthly value is released for any of its component series. Since release dates of data values for a given month vary across the component series, with slight variations in the monthly release date for any one component series, updates to the index are frequent. It is unavoidably the case that, at almost all updates, some of the component series lack observations for the current (most recent) data month. To address this situation, component series that are one month behind are predicted (nowcast) for the current index month, using a multivariate autoregressive time series model. This report discusses the input series to the index, the construction of the index by PCA, and the nowcasting procedure used. The report then examines some properties of the index and its relation to quarterly U.S. Gross Domestic Product and to some monthly non-Census Bureau economic indicators.

Suggested Citation

  • Jose Asturias & William R. Bell & Rebecca Hutchinson & Tucker McElroy & Katherine J. Thompson, 2023. "Building the Census Bureau Index of Economic Activity (IDEA)," Working Papers 23-15, Center for Economic Studies, U.S. Census Bureau.
  • Handle: RePEc:cen:wpaper:23-15
    as

    Download full text from publisher

    File URL: https://www2.census.gov/library/working-papers/2023/adrm/ces/CES-WP-23-15.pdf
    File Function: First version, 2023
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Scott Brave, 2008. "Economic trends and the Chicago Fed National Activity Index," Chicago Fed Letter, Federal Reserve Bank of Chicago, issue May.
    2. Matthew J. Kotchen & James H. Stock & Catherine D. Wolfram, 2019. "Introduction to "Environmental and Energy Policy and the Economy"," NBER Chapters, in: Environmental and Energy Policy and the Economy, volume 1, pages 3-7, National Bureau of Economic Research, Inc.
    3. 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.
    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. 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.
    3. Dinh, Tami & Schultze, Wolfgang, 2022. "Accounting for R&D on the income statement? Evidence on non-discretionary vs. discretionary R&D capitalization under IFRS in Germany," Journal of International Accounting, Auditing and Taxation, Elsevier, vol. 46(C).
    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. 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.
    6. 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).
    7. Valenti, Daniele & Bastianin, Andrea & Manera, Matteo, 2023. "A weekly structural VAR model of the US crude oil market," Energy Economics, Elsevier, vol. 121(C).
    8. De Santis, Roberto A. & Van der Veken, Wouter, 2020. "Forecasting macroeconomic risk in real time: Great and Covid-19 Recessions," Working Paper Series 2436, European Central Bank.
    9. Eraslan, Sercan & Götz, Thomas, 2021. "An unconventional weekly economic activity index for Germany," Economics Letters, Elsevier, vol. 204(C).
    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. Aprigliano, Valentina & Emiliozzi, Simone & Guaitoli, Gabriele & Luciani, Andrea & Marcucci, Juri & Monteforte, Libero, 2023. "The power of text-based indicators in forecasting Italian economic activity," International Journal of Forecasting, Elsevier, vol. 39(2), pages 791-808.
    12. Ilan Noy & Nguyen Doan & Benno Ferrarini & Donghyun Park, 2020. "Measuring the Economic Risk of COVID‐19," Global Policy, London School of Economics and Political Science, vol. 11(4), pages 413-423, September.
    13. 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).
    14. Fabrizio Ferretti & Michele Mariani & Elena Sarti, 2022. "Does the Prevalence of Obesity Affect the Demand for Soft Drinks? Evidence from Cross-Country Panel Data," IJERPH, MDPI, vol. 19(2), pages 1-12, January.
    15. 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.
    16. Fabian Kruger & Hendrik Plett, 2022. "Prediction intervals for economic fixed-event forecasts," Papers 2210.13562, arXiv.org, revised Mar 2024.
    17. Miranda Gualdrón, Karen Alejandra & Poncela, Pilar & Ruiz Ortega, Esther, 2021. "Dynamic factor models: does the specification matter?," DES - Working Papers. Statistics and Econometrics. WS 32210, Universidad Carlos III de Madrid. Departamento de Estadística.
    18. Ali B. Barlas & Seda Guler Mert & Berk Orkun Isa & Alvaro Ortiz & Tomasa Rodrigo & Baris Soybilgen & Ege Yazgan, 2021. "Big Data Information and Nowcasting: Consumption and Investment from Bank Transactions in Turkey," Papers 2107.03299, arXiv.org.
    19. Le, Thai-Ha & Boubaker, Sabri & Bui, Manh Tien & Park, Donghyun, 2023. "On the volatility of WTI crude oil prices: A time-varying approach with stochastic volatility," Energy Economics, Elsevier, vol. 117(C).
    20. Beck, Elliot & De Nard, Gianluca & Wolf, Michael, 2023. "Improved inference in financial factor models," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 364-379.

    More about this item

    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:cen:wpaper:23-15. 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: Dawn Anderson (email available below). General contact details of provider: https://edirc.repec.org/data/cesgvus.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.