IDEAS home Printed from https://ideas.repec.org/p/bea/wpaper/0076.html
   My bibliography  Save this paper

Is GDP or GDI a better measure of output? A statistical approach

Author

Listed:
  • Ryan Greenaway-McGrevy

    (Bureau of Economic Analysis)

Abstract

Gross domestic product (GDP) and gross domestic income (GDI) are in theory estimates of the same concept, namely economic production over a defined span of time and space. Yet the two measures are compiled using different source data, and the two measures often give different indications of the direction of the economy. This raises the issue of which of the two measures is a more accurate estimate of economic production. In this paper we present a time-series statistical framework for addressing this issue. Our findings indicate that the latest vintage of GDP has been a better measure of true output over the 1983-2009 period than the latest vintage of GDI. Our model also implies an optimal weighting of GDP and GDI can yield a more accurate estimate of economic output than either GDP or GDI alone. Our empirical findings indicate that a weighting of approximately 60% to GDP yields the best estimate for the 1983-2009 period. When we consider vintages of estimated output, we find that GDI often contains additional information to GDP regarding true output.

Suggested Citation

  • Ryan Greenaway-McGrevy, 2011. "Is GDP or GDI a better measure of output? A statistical approach," BEA Working Papers 0076, Bureau of Economic Analysis.
    • Handle: RePEc:bea:wpaper:0076
    as

    Download full text from publisher

    File URL: https://www.bea.gov/system/files/papers/WP2011-8.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gabriel Perez-Quiros & Margaret M. McConnell, 2000. "Output Fluctuations in the United States: What Has Changed since the Early 1980's?," American Economic Review, American Economic Association, vol. 90(5), pages 1464-1476, December.
    2. Bruce T. Grimm, 2005. "Alternative Measures of U.S. Economic Activity in Business Cycles and Business Cycle Dating," BEA Papers 0052, Bureau of Economic Analysis.
    3. N. Gregory Mankiw & Matthew D. Shapiro, 1986. "News or Noise? An Analysis of GNP Revisions," NBER Working Papers 1939, National Bureau of Economic Research, Inc.
    4. Patterson, K. D., 1994. "A state space model for reducing the uncertainty associated with preliminary vintages of data with an application to aggregate consumption," Economics Letters, Elsevier, vol. 46(3), pages 215-222, November.
    5. 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.
    6. Dennis Fixler & Bruce Grimm, 2006. "GDP Estimates: Rationality Tests and Turning Point Performance," Journal of Productivity Analysis, Springer, vol. 25(3), pages 213-229, June.
    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. 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.
    2. Martín Almuzara & Dante Amengual & Gabriele Fiorentini & Enrique Sentana, 2022. "GDP Solera. The Ideal Vintage Mix," Working Papers wp2022_2204, CEMFI.
    3. Tincho Almuzara & Dante Amengual & Enrique Sentana, 2017. "Normality Tests for Latent Variables," Working Papers wp2018_1708, CEMFI.
    4. Mary C. Daly & John G. Fernald & Òscar Jordà & Fernanda Nechio, 2013. "Shocks and Adjustments," Working Paper Series 2013-32, Federal Reserve Bank of San Francisco.
    5. 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.
    6. Martín Almuzara & Dante Amengual & Enrique Sentana, 2019. "Normality tests for latent variables," Quantitative Economics, Econometric Society, vol. 10(3), pages 981-1017, July.

    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. 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.).
    2. Jeremy J. Nalewaik, 2010. "The Income- and Expenditure-Side Estimates of U.S. Output Growth," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 41(1 (Spring), pages 71-127.
    3. D’Elia Enrico, 2014. "Predictions vs. Preliminary Sample Estimates: The Case of Eurozone Quarterly GDP," Journal of Official Statistics, Sciendo, vol. 30(3), pages 1-22, September.
    4. Hännikäinen Jari, 2017. "Selection of an Estimation Window in the Presence of Data Revisions and Recent Structural Breaks," Journal of Econometric Methods, De Gruyter, vol. 6(1), pages 1-22, January.
    5. Alastair Cunningham & Chris Jeffery & George Kapetanios & Vincent Labhard, 2007. "A State Space Approach To The Policymaker's Data Uncertainty Problem," Money Macro and Finance (MMF) Research Group Conference 2006 168, Money Macro and Finance Research Group.
    6. Michael P. Clements, 2014. "Anticipating Early Data Revisions to US GDP and the Effects of Releases on Equity Markets," ICMA Centre Discussion Papers in Finance icma-dp2014-06, Henley Business School, University of Reading.
    7. Alastair Cunningham & Jana Eklund & Chris Jeffery & George Kapetanios & Vincent Labhard, 2009. "A State Space Approach to Extracting the Signal From Uncertain Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(2), pages 173-180, March.
    8. Clements, Michael P. & Galvao, Ana Beatriz, 2020. "Density Forecasting with BVAR Models under Macroeconomic Data Uncertainty," EMF Research Papers 36, Economic Modelling and Forecasting Group.
    9. M. Mogliani & T. Ferrière, 2016. "Rationality of announcements, business cycle asymmetry, and predictability of revisions. The case of French GDP," Working papers 600, Banque de France.
    10. Hännikäinen, Jari, 2014. "Multi-step forecasting in the presence of breaks," MPRA Paper 55816, University Library of Munich, Germany.
    11. Boragan Aruoba & Francis X. Diebold & Jeremy Nalewaik & Frank Schorfheide & Dongho Song, 2011. "Improving GDP Measurement: A Forecast Combination Perspective," PIER Working Paper Archive 11-028, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    12. Jacobs, Jan P.A.M. & van Norden, Simon, 2011. "Modeling data revisions: Measurement error and dynamics of "true" values," Journal of Econometrics, Elsevier, vol. 161(2), pages 101-109, April.
    13. Peter A. Zadrozny, 2016. "Real-Time State Space Method for Computing Smoothed Estimates of Future Revisions of U.S. Monthly Chained CPI," CESifo Working Paper Series 5897, CESifo.
    14. Verónica Cañal-Fernández, 2012. "Accuracy and reliability of Spanish regional accounts (CRE-95)," Empirical Economics, Springer, vol. 43(3), pages 1299-1320, December.
    15. Boysen-Hogrefe, Jens & Neuwirth, Stefan, 2012. "The impact of seasonal and price adjustments on the predictability of German GDP revisions," Kiel Working Papers 1753, Kiel Institute for the World Economy (IfW Kiel).
    16. Cecilia Frale & Valentina Raponi, 2011. "Revisions in ocial data and forecasting," Working Papers LuissLab 1194, Dipartimento di Economia e Finanza, LUISS Guido Carli.
    17. Valentina Raponi & Cecilia Frale, 2014. "Revisions in official data and forecasting," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(3), pages 451-472, August.
    18. Dean Croushore, 2019. "Revisions to PCE Inflation Measures: Implications for Monetary Policy," International Journal of Central Banking, International Journal of Central Banking, vol. 15(4), pages 241-265, October.
    19. Jeremy J. Nalewaik, 2008. "Lack of signal error (LoSE) and implications for OLS regression: measurement error for macro data," Finance and Economics Discussion Series 2008-15, Board of Governors of the Federal Reserve System (U.S.).
    20. Michael P Clements & Ana Beatriz Galvao, 2017. "Data Revisions and Real-time Probabilistic Forecasting of Macroeconomic Variables," ICMA Centre Discussion Papers in Finance icma-dp2017-01, Henley Business School, University of Reading.

    More about this item

    JEL classification:

    • E6 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access

    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:0076. 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.