IDEAS home Printed from https://ideas.repec.org/a/fip/fedprr/00014.html
   My bibliography  Save this article

Real-time performance of GDPplus and alternative model-based measures of GDP: 2005—2014

Author

Listed:
  • Tom Stark

Abstract

Like most macroeconomic variables, real gross domestic product is subject to measurement error. Because the U.S. Bureau of Economic Analysis lacks complete information at the time it publishes its initial GDP estimates, revisions are often substantial. Analysts concerned about the accuracy of these early estimates for expenditure GDP could focus instead on gross domestic income, the BEA?s measure of U.S. output on the income side of the national accounts. Conceptually, GDP on the expenditure side should equal GDP on the income side, and there should be no choice to make between the two series. As a practical matter, however, the two measures can differ by a significant amount because each measure is constructed using ?largely independent? source data, which themselves are ?less than perfect? [BEA (2014)].

Suggested Citation

  • Tom Stark, 2014. "Real-time performance of GDPplus and alternative model-based measures of GDP: 2005—2014," Research Rap Special Report, Federal Reserve Bank of Philadelphia, issue Nov.
    • Handle: RePEc:fip:fedprr:00014
    as

    Download full text from publisher

    File URL: https://www.philadelphiafed.org/-/media/frbp/assets/economy/reports/research-rap/2014/real-time-performance-of-gdpplus.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Athanasios Orphanides & Simon van Norden, 2002. "The Unreliability of Output-Gap Estimates in Real Time," The Review of Economics and Statistics, MIT Press, vol. 84(4), pages 569-583, November.
    3. Athanasios Orphanides, 2001. "Monetary Policy Rules Based on Real-Time Data," American Economic Review, American Economic Association, vol. 91(4), pages 964-985, September.
    4. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    5. S. Boragan Aruoba & Francis X. Diebold & Jeremy J. Nalewaik & Frank Schorfheide & Dongho Song, 2011. "Improving GDP measurement: a forecast combination perspective," Working Papers 11-41, Federal Reserve Bank of Philadelphia.
    6. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    7. Orphanides, Athanasios, 2003. "Historical monetary policy analysis and the Taylor rule," Journal of Monetary Economics, Elsevier, vol. 50(5), pages 983-1022, July.
    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. Madalina-Gabriela Anghel & Alexandru Manole & Alina-Georgiana Solomon, 2017. "Using the System of National Accounts in the Forecasting Activity," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 7(2), pages 91-96, April.

    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. 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.
    2. Costas Milas & Ruthira Naraidoo, 2009. "Financial Market Conditions, Real Time, Nonlinearity and European Central Bank Monetary Policy: In-Sample and Out-of-Sample Assessment," Working Papers 200923, University of Pretoria, Department of Economics.
    3. de Carvalho, Miguel & Rua, António, 2017. "Real-time nowcasting the US output gap: Singular spectrum analysis at work," International Journal of Forecasting, Elsevier, vol. 33(1), pages 185-198.
    4. Molodtsova, Tanya & Nikolsko-Rzhevskyy, Alex & Papell, David H., 2008. "Taylor rules with real-time data: A tale of two countries and one exchange rate," Journal of Monetary Economics, Elsevier, vol. 55(Supplemen), pages 63-79, October.
    5. Tanya Molodtsova & Alex Nikolsko-Rzhevskyy & David H. Papell, 2011. "Taylor Rules and the Euro," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43, pages 535-552, March.
    6. Kamada, Koichiro, 2005. "Real-time estimation of the output gap in Japan and its usefulness for inflation forecasting and policymaking," The North American Journal of Economics and Finance, Elsevier, vol. 16(3), pages 309-332, December.
    7. Matthieu LEMOINE & Odile CHAGNY, 2005. "Estimating the potential output of the euro area with a semi-structural multivariate Hodrick-Prescott filter," Computing in Economics and Finance 2005 344, Society for Computational Economics.
    8. McDonald, Christopher & Thamotheram, Craig & Vahey, Shaun P. & Wakerly, Elizabeth C., 2015. "Assessing the Economic Value of Probabilistic Forecasts in the Presence of an Inflation Target," EMF Research Papers 09, Economic Modelling and Forecasting Group.
    9. Croushore, D., 2002. "Comments on 'The state of macroeconomic forecasting'," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 483-489, December.
    10. Olivier Coibion & Yuriy Gorodnichenko, 2011. "Monetary Policy, Trend Inflation, and the Great Moderation: An Alternative Interpretation," American Economic Review, American Economic Association, vol. 101(1), pages 341-370, February.
    11. Hinterlang, Natascha, 2019. "Predicting Monetary Policy Using Artificial Neural Networks," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203503, Verein für Socialpolitik / German Economic Association.
    12. Robert Lehmann, 2023. "READ-GER: Introducing German Real-Time Regional Accounts Data for Revision Analysis and Nowcasting," CESifo Working Paper Series 10315, CESifo.
    13. Kevin Lee & James Morley & Kalvinder Shields, 2015. "The Meta Taylor Rule," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(1), pages 73-98, February.
    14. Ahsan ul Haq Satti & Wasim Shahid Malik, 2017. "The Unreliability of Output-Gap Estimates in Real Time," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 56(3), pages 193-219.
    15. Garciga, Christian & Knotek II, Edward S., 2019. "Forecasting GDP growth with NIPA aggregates: In search of core GDP," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1814-1828.
    16. George Kapetanios, 2004. "Estimating Time-Variation in Measurement Error from Data Revisions: An Application to Forecasting in Dynamic Models," Working Papers 520, Queen Mary University of London, School of Economics and Finance.
    17. Helge Berger & Pär Österholm, 2011. "Does Money matter for U.S. Inflation? Evidence from Bayesian VARs," CESifo Economic Studies, CESifo Group, vol. 57(3), pages 531-550, September.
    18. Dan Armeanu & Georgiana Camelia Crețan & Leonard Lache & Mihaela Mitroi, 2015. "Estimating Potential GDP for the Romanian Economy and Assessing the Sustainability of Economic Growth: A Multivariate Filter Approach," Sustainability, MDPI, vol. 7(3), pages 1-21, March.
    19. Carlos Fonseca Marinheiro, 2010. "Fiscal sustainability and the accuracy of macroeconomic forecasts: do supranational forecasts rather than government forecasts make a difference?," GEMF Working Papers 2010-07, GEMF, Faculty of Economics, University of Coimbra.
    20. Athanasios Orphanides & Simon van Norden, 2002. "The Unreliability of Output-Gap Estimates in Real Time," The Review of Economics and Statistics, MIT Press, vol. 84(4), pages 569-583, November.

    More about this item

    Keywords

    GDPplus; Real-time data; GDP;
    All these keywords.

    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:fip:fedprr:00014. 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: Beth Paul (email available below). General contact details of provider: https://edirc.repec.org/data/frbphus.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.