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Integrating Expenditure and Income Data: What to do with the Statistical Discrepancy?

Author

Listed:
  • J. Joseph Beaulieu

    () (Division of Research and Statistics, Board of Governors of the Federal Reserve System, Washington)

  • Eric J. Bartelsman

    () (Faculty of Economics and Business Administration, Vrije Universiteit Amsterdam)

Abstract

This discussion paper led to a publication in (D.W. Jorgenson, J.S. Landefeld, W.D. Nordhaus, eds.) 'A New Architecture for the U.S. National Accounts', NBER Studies in Income and Wealth , vol. 66, 309-54, University of Chicago Press, 2006. The purpose of this paper is to build consistent, integrated datasets to investigate whether various disaggregated data can shed light on the possible sources of the statistical discrepancy. Our strategy is first to use disaggregated data to estimate consistent sets of input-output models that sum to either GDP or GDI and compare the two in order to see where the discrepancy resides. We find a few “problem” industries that appear to explain most of the statistical discrepancy. Second, we explore what combination of the expenditure data and the income data seem to produce the most sensible data according to a few economic criteria. A mixture of data that do not aggregate either to GDP or to GDI appears optimal.

Suggested Citation

  • J. Joseph Beaulieu & Eric J. Bartelsman, 2004. "Integrating Expenditure and Income Data: What to do with the Statistical Discrepancy?," Tinbergen Institute Discussion Papers 04-078/3, Tinbergen Institute.
  • Handle: RePEc:tin:wpaper:20040078
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    References listed on IDEAS

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    1. Louis De Mesnard, 2002. "Forecast output coincidence and biproportion: two criteria to determine the orientation of an economy. Comparison for France (1980-1997)," Applied Economics, Taylor & Francis Journals, vol. 34(16), pages 2085-2091.
    2. Eric J. Bartelsman & J. Joseph Beaulieu, 2007. "A Consistent Accounting of U.S. Productivity Growth," NBER Chapters, in: Hard-to-Measure Goods and Services: Essays in Honor of Zvi Griliches, pages 449-482, National Bureau of Economic Research, Inc.
    3. Richard Stone & D. G. Champernowne & J. E. Meade, 1942. "The Precision of National Income Estimates," Review of Economic Studies, Oxford University Press, vol. 9(2), pages 111-125.
    4. Mario Forni & Lucrezia Reichlin, 1998. "Let's Get Real: A Factor Analytical Approach to Disaggregated Business Cycle Dynamics," Review of Economic Studies, Oxford University Press, vol. 65(3), pages 453-473.
    5. Dale W. Jorgenson & Kevin J. Stiroh, 2000. "Raising the Speed Limit: U.S. Economic Growth in the Information Age," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 31(1), pages 125-236.
    6. Dennis J Fixler & Marshall B Reinsdorf & Shaunda Villones, 2010. "Measuring the services of commercial banks in the NIPA," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The IFC's contribution to the 57th ISI Session, Durban, August 2009, volume 33, pages 346-349, Bank for International Settlements.
    7. William D. Nordhaus, 2000. "New Data and Output Concepts for Understanding Productivity Trends," Cowles Foundation Discussion Papers 1286, Cowles Foundation for Research in Economics, Yale University.
    8. Horvath, Michael, 2000. "Sectoral shocks and aggregate fluctuations," Journal of Monetary Economics, Elsevier, vol. 45(1), pages 69-106, February.
    9. David E. Lebow, 1990. "The covariability of productivity shocks across industries," Working Paper Series / Economic Activity Section 102, Board of Governors of the Federal Reserve System (U.S.), revised 1990.
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    Cited by:

    1. Baoline Chen, 2012. "A Balanced System of U.S. Industry Accounts and Distribution of the Aggregate Statistical Discrepancy by Industry," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(2), pages 202-211, February.

    More about this item

    Keywords

    industry data; input-output; national accounts; statistical discrepancy;

    JEL classification:

    • C67 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Input-Output Models
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access

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