IDEAS home Printed from https://ideas.repec.org/p/red/sed006/123.html
   My bibliography  Save this paper

Bench Mark Revisions and the U.S. Personal Saving Rate

Author

Listed:
  • Leonard I. Nakamura

    (Federal Reserve Bank of Phildelphia)

  • Tom Stark

Abstract

Initially published estimates of the personal saving rate from 1965 Q3 to 1999 Q2, which averaged 5.3 percent, have been revised up 2.8 percentage points to 8.1 percent, as we document. We show that much of the initial variations in personal saving rate across time was pure noise. Nominal disposable personal income has been revised upward an average of 8.3 percent: one dollar in twelve was originally missing. We use both conventional and real-time estimates of the personal saving rate to forecast real disposable income, gross domestic product, and personal consumption and show that using the personal saving rate in real-time would have almost invariably made forecasts worse. Thus while the personal saving rate may contain information about later consumption once we know the true saving rate, as Campbell (1987) and Ireland(1995) have shown, as a practical matter, noise in the U.S. personal saving rate makes it uninformative for forecasting purposes

Suggested Citation

  • Leonard I. Nakamura & Tom Stark, 2006. "Bench Mark Revisions and the U.S. Personal Saving Rate," 2006 Meeting Papers 123, Society for Economic Dynamics.
  • Handle: RePEc:red:sed006:123
    as

    Download full text from publisher

    File URL: https://www.red-files-public.s3.amazonaws.com/meetpapers/2006/paper_123.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Hall, Robert E, 1978. "Stochastic Implications of the Life Cycle-Permanent Income Hypothesis: Theory and Evidence," Journal of Political Economy, University of Chicago Press, vol. 86(6), pages 971-987, December.
    2. S. Borağan Aruoba, 2008. "Data Revisions Are Not Well Behaved," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(2‐3), pages 319-340, March.
    3. Croushore, Dean & Stark, Tom, 2001. "A real-time data set for macroeconomists," Journal of Econometrics, Elsevier, vol. 105(1), pages 111-130, November.
    4. Jonathan A. Parker, 2000. "Spendthrift in America? On Two Decades of Decline in the US Saving Rate," NBER Chapters, in: NBER Macroeconomics Annual 1999, Volume 14, pages 317-387, National Bureau of Economic Research, Inc.
    5. Michael J. Boskin, 2000. "Economic Measurement: Progress and Challenges," American Economic Review, American Economic Association, vol. 90(2), pages 247-252, May.
    6. Campbell, John Y, 1987. "Does Saving Anticipate Declining Labor Income? An Alternative Test of the Permanent Income Hypothesis," Econometrica, Econometric Society, vol. 55(6), pages 1249-1273, November.
    7. Peter N. Ireland, 1995. "Using the permanent income hypothesis for forecasting," Economic Quarterly, Federal Reserve Bank of Richmond, issue Win, pages 49-63.
    8. 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.
    9. Dean Croushore & Tom Stark, 2003. "A Real-Time Data Set for Macroeconomists: Does the Data Vintage Matter?," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 605-617, August.
    10. William G. Gale & John Sabelhaus, 1999. "Perspectives on the Household Saving Rate," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 30(1), pages 181-224.
    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. Andrew Kish, 2006. "Perspectives on recent trends in consumer debt," Consumer Finance Institute discussion papers 06-05, Federal Reserve Bank of Philadelphia.
    2. Croushore, Dean & Del Monaco Santos, Pedro, 2022. "The personal saving rate: Data revisions and forecasts," Economics Letters, Elsevier, vol. 219(C).

    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. Clements, Michael P. & Beatriz Galvão, Ana, 2010. "First announcements and real economic activity," European Economic Review, Elsevier, vol. 54(6), pages 803-817, August.
    2. Leonard I. Nakamura & Tom Stark, 2007. "Mismeasured personal saving and the permanent income hypothesis," Working Papers 07-8, Federal Reserve Bank of Philadelphia.
    3. Croushore, Dean & Evans, Charles L., 2006. "Data revisions and the identification of monetary policy shocks," Journal of Monetary Economics, Elsevier, vol. 53(6), pages 1135-1160, September.
    4. Clements, Michael P. & Beatriz Galvao, Ana, 2010. "Real-time Forecasting of Inflation and Output Growth in the Presence of Data Revisions," Economic Research Papers 270771, University of Warwick - Department of Economics.
    5. Clements Michael P., 2012. "Forecasting U.S. Output Growth with Non-Linear Models in the Presence of Data Uncertainty," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(1), pages 1-27, January.
    6. Carlo Altavilla & Matteo Ciccarelli, 2011. "Monetary Policy Analysis in Real-Time. Vintage Combination from a Real-Time Dataset," CESifo Working Paper Series 3372, CESifo.
    7. Emilia Tomczyk, 2013. "End of sample vs. real time data: perspectives for analysis of expectations," Working Papers 68, Department of Applied Econometrics, Warsaw School of Economics.
    8. 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.
    9. Dean Croushore, 2011. "Frontiers of Real-Time Data Analysis," Journal of Economic Literature, American Economic Association, vol. 49(1), pages 72-100, March.
    10. 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.
    11. David Hendry & Michael P. Clements, 2010. "Forecasting from Mis-specified Models in the Presence of Unanticipated Location Shifts," Economics Series Working Papers 484, University of Oxford, Department of Economics.
    12. Emilio Fernandez-Corugedo, 2004. "Consumption Theory," Handbooks, Centre for Central Banking Studies, Bank of England, number 23, April.
    13. Corradi, Valentina & Fernandez, Andres & Swanson, Norman R., 2009. "Information in the Revision Process of Real-Time Datasets," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 455-467.
    14. André Kallåk Anundsen & Ragnar Nymoen, 2015. "Did US Consumers 'Save for a Rainy Day' Before the Great Recession?," CESifo Working Paper Series 5347, CESifo.
    15. 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.
    16. Kishor, N. Kundan, 2011. "Data revisions in India: Implications for monetary policy," Journal of Asian Economics, Elsevier, vol. 22(2), pages 164-173, April.
    17. Denis Shibitov & Mariam Mamedli, 2021. "Forecasting Russian Cpi With Data Vintages And Machine Learning Techniques," Bank of Russia Working Paper Series wps70, Bank of Russia.
    18. Gregory E. Givens, 2017. "Do Data Revisions Matter for DSGE Estimation?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(6), pages 1385-1407, September.
    19. S. Boragan Aruoba, 2004. "Data Uncertainty in General Equilibrium," Computing in Economics and Finance 2004 131, Society for Computational Economics.
    20. Francisco Castro & Javier J. P√Ârez & Marta Rodr√Çguez-Vives, 2013. "Fiscal Data Revisions in Europe," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 45(6), pages 1187-1209, September.

    More about this item

    Keywords

    Permanent Income; Saving; Real-time data;
    All these keywords.

    JEL classification:

    • 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
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access

    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:red:sed006:123. 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: Christian Zimmermann (email available below). General contact details of provider: https://edirc.repec.org/data/sedddea.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.