IDEAS home Printed from https://ideas.repec.org/a/wly/japmet/v38y2023i7p1007-1017.html
   My bibliography  Save this article

Employment reconciliation and nowcasting

Author

Listed:
  • Eiji Goto
  • Jan P.A.M. Jacobs
  • Tara M. Sinclair
  • Simon van Norden

Abstract

We construct a latent employment estimate for the United States, which both reconciles the information from separate payroll and household surveys and incorporates the preliminary data revision process of the payroll data. We find that our reconciled latent employment series looks somewhat different than the initial release of payroll employment and is closer to the fully revised data that is benchmarked to a near census of employment. A real‐time exercise, however, suggests that the reconciled employment estimate is remarkably similar to the initial release of payroll employment with near zero weight on the household survey information.

Suggested Citation

  • Eiji Goto & Jan P.A.M. Jacobs & Tara M. Sinclair & Simon van Norden, 2023. "Employment reconciliation and nowcasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(7), pages 1007-1017, November.
    • Handle: RePEc:wly:japmet:v:38:y:2023:i:7:p:1007-1017
    DOI: 10.1002/jae.2995
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/jae.2995
    Download Restriction: no
    File URL: https://libkey.io/10.1002/jae.2995?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Nikoleta Anesti & Ana Beatriz Galvao & Silvia Miranda-Agrippino, 2018. "Uncertain Kingdom: Nowcasting GDP and its Revisions," Discussion Papers 1824, Centre for Macroeconomics (CFM).
    2. Weale, Martin, 1992. "Estimation of Data Measured with Error and Subject to Linear Restrictions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(2), pages 167-174, April-Jun.
    3. Richard Stone & D. G. Champernowne & J. E. Meade, 1942. "The Precision of National Income Estimates," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 9(2), pages 111-125.
    4. Tomaz Cajner & Leland D. Crane & Ryan A. Decker & Adrian Hamins-Puertolas & Christopher Kurz, 2019. "Improving the Accuracy of Economic Measurement with Multiple Data Sources: The Case of Payroll Employment Data," NBER Chapters, in: Big Data for Twenty-First-Century Economic Statistics, pages 147-170, National Bureau of Economic Research, Inc.
    5. Barend Abeln & Jan P. A. M. Jacobs, 2023. "Seasonal Adjustment Without Revisions," SpringerBriefs in Economics, Springer, number 978-3-031-22845-2, October.
    6. Martín Almuzara & Gabriele Fiorentini & Enrique Sentana, 2023. "Aggregate Output Measurements: A Common Trend Approach," Advances in Econometrics, in: Essays in Honor of Joon Y. Park: Econometric Methodology in Empirical Applications, volume 45, pages 3-33, Emerald Group Publishing Limited.
    7. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
    8. Jan P. A. M. Jacobs & Samad Sarferaz & Jan-Egbert Sturm & Simon van Norden, 2022. "Can GDP Measurement Be Further Improved? Data Revision and Reconciliation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 423-431, January.
    9. Serena Ng, 2021. "Modeling Macroeconomic Variations after Covid-19," NBER Working Papers 29060, National Bureau of Economic Research, Inc.
    10. Kevin Dowd & Margaret Woods & John Cotter & Chris Humphrey, 2010. "How Unlucky is 25-Sigma?," Working Papers 200838, Geary Institute, University College Dublin.
    11. Aruoba, S. Borağan & Diebold, Francis X. & Nalewaik, Jeremy & Schorfheide, Frank & Song, Dongho, 2016. "Improving GDP measurement: A measurement-error perspective," Journal of Econometrics, Elsevier, vol. 191(2), pages 384-397.
    12. Marcelle Chauvet & Jeremy Piger, 2013. "Employment And The Business Cycle," Manchester School, University of Manchester, vol. 81, pages 16-42, October.
    13. Phillips, Keith R. & Nordlund, James, 2012. "The efficiency of the benchmark revisions to the current employment statistics (CES) data," Economics Letters, Elsevier, vol. 115(3), pages 431-434.
    14. Xiaohong Chen & Norman R. Swanson (ed.), 2013. "Recent Advances and Future Directions in Causality, Prediction, and Specification Analysis," Springer Books, Springer, edition 127, number 978-1-4614-1653-1, January.
    15. Michael T. Owyang & E. Katarina Vermann, 2014. "Employment Revision Asymmetries," Economic Synopses, Federal Reserve Bank of St. Louis, issue 11.
    16. Neumark, David & Wascher, William L, 1991. "Can We Improve upon Preliminary Estimates of Payroll Employment Growth?," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(2), pages 197-205, April.
    17. Barend Abeln & Jan P. A. M. Jacobs, 2023. "CAMPLET: Seasonal Adjustment Without Revisions," SpringerBriefs in Economics, in: Seasonal Adjustment Without Revisions, chapter 0, pages 7-29, Springer.
    18. Nicholas Haltom & Vanessa D. Mitchell & Ellis W. Tallman, 2005. "Payroll employment data: measuring the effects of annual benchmark revisions," Economic Review, Federal Reserve Bank of Atlanta, vol. 90(Q 2), pages 1-23.
    19. repec:bla:manchs:v:81:y:2013:i:s2:p:16-42 is not listed on IDEAS
    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. Martín Almuzara & Dante Amengual & Gabriele Fiorentini & Enrique Sentana, 2024. "GDP Solera: The Ideal Vintage Mix," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(3), pages 984-997, July.
    2. 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).
    3. repec:wrk:wrkemf:37 is not listed on IDEAS
    4. Jan P. A. M. Jacobs & Samad Sarferaz & Jan-Egbert Sturm & Simon van Norden, 2022. "Can GDP Measurement Be Further Improved? Data Revision and Reconciliation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 423-431, January.
    5. Sekine, Toshitaka, 2022. "Looking from Gross Domestic Income: Alternative view of Japan’s economy," Japan and the World Economy, Elsevier, vol. 64(C).
    6. John G. Fernald & J. Christina Wang, 2016. "Why Has the Cyclicality of Productivity Changed? What Does It Mean?," Annual Review of Economics, Annual Reviews, vol. 8(1), pages 465-496, October.
    7. Tomaz Cajner & Leland D. Crane & Ryan A. Decker & Adrian Hamins-Puertolas & Christopher Kurz, 2019. "Improving the Accuracy of Economic Measurement with Multiple Data Sources: The Case of Payroll Employment Data," NBER Chapters, in: Big Data for Twenty-First-Century Economic Statistics, pages 147-170, National Bureau of Economic Research, Inc.
    8. Chauvet, Marcelle & Potter, Simon, 2013. "Forecasting Output," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 141-194, Elsevier.
    9. 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.
    10. Athanasopoulos, George & Hyndman, Rob J. & Kourentzes, Nikolaos & Panagiotelis, Anastasios, 2024. "Forecast reconciliation: A review," International Journal of Forecasting, Elsevier, vol. 40(2), pages 430-456.
    11. Martín Almuzara & Dante Amengual & Enrique Sentana, 2019. "Normality tests for latent variables," Quantitative Economics, Econometric Society, vol. 10(3), pages 981-1017, July.
    12. Jan P.A.M. Jacobs & Samad Sarferaz & Simon van Norden & Jan-Egbert Sturm, 2013. "Modeling Multivariate Data Revisions," CIRANO Working Papers 2013s-44, CIRANO.
    13. Cai, Michael & Del Negro, Marco & Giannoni, Marc P. & Gupta, Abhi & Li, Pearl & Moszkowski, Erica, 2019. "DSGE forecasts of the lost recovery," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1770-1789.
    14. Tincho Almuzara & Dante Amengual & Enrique Sentana, 2017. "Normality Tests for Latent Variables," Working Papers wp2018_1708, CEMFI.
    15. Dennis J. Fixler & Jeremy J. Nalewaik, 2007. "News, noise, and estimates of the \"true\" unobserved state of the economy," Finance and Economics Discussion Series 2007-34, Board of Governors of the Federal Reserve System (U.S.).
    16. Fleury, Afonso & Fleury, Maria Tereza Leme & Oliveira, Luis & Leao, Pablo, 2024. "Going digital EMNEs: The role of digital maturity capability," International Business Review, Elsevier, vol. 33(4).
    17. Alessandro Giovannelli & Marco Lippi & Tommaso Proietti, 2023. "Band-Pass Filtering with High-Dimensional Time Series," Papers 2305.06618, arXiv.org.
    18. Kurt Graden Lunsford, 2023. "The Discrepancy Between Expenditure- and Income-Side Estimates of US Output," Economic Commentary, Federal Reserve Bank of Cleveland, vol. 2023(01), pages 1-7, January.
    19. Phillips, Keith R. & Nordlund, James, 2012. "The efficiency of the benchmark revisions to the current employment statistics (CES) data," Economics Letters, Elsevier, vol. 115(3), pages 431-434.
    20. Kohns, David & Potjagailo, Galina, 2023. "Flexible Bayesian MIDAS: time‑variation, group‑shrinkage and sparsity," Bank of England working papers 1025, Bank of England.
    21. Mumtaz, Haroon & Theodoridis, Konstantinos, 2017. "Common and country specific economic uncertainty," Journal of International Economics, Elsevier, vol. 105(C), pages 205-216.

    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity

    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:wly:japmet:v:38:y:2023:i:7:p:1007-1017. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/0883-7252/ .

    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.