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A Balanced System of Industry Accounts for the U.S. and Structural Distribution of Statistical Discrepancy

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  • Baoline Chen

    (Bureau of Economic Analysis)

Abstract

This paper describes and illustrates a generalized least squares (GLS) reconciliation method that can efficiently incorporate all available information on initial data in reconciling a large system of disaggregated accounts and can accurately estimate industry distribution of statistical discrepancy. The GLS reconciliation method is applied to reconciling the 1997 GDP-by-industry accounts and the Input-output accounts. The GDP-by-industry accounts measure GDP by industry using industry gross income, and the input-output accounts measure GDP by industry as the residual between gross output and intermediate inputs. The GLS method produced balanced estimates and estimated the industry distribution of the statisical discrepancy. The results show that using reliability to reconcile different accounts produces statistically meaningful balanced estimates. The study demonstrates that reconciling a large system of disaggregated accounts is empirically feasible and computationally efficient.

Suggested Citation

  • Baoline Chen, 2006. "A Balanced System of Industry Accounts for the U.S. and Structural Distribution of Statistical Discrepancy," BEA Papers 0070, Bureau of Economic Analysis.
    • Handle: RePEc:bea:papers:0070
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    References listed on IDEAS

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    1. J. Joseph Beaulieu & Eric J. Bartelsman, 2006. "Integrating Expenditure and Income Data: What to Do with the Statistical Discrepancy?," NBER Chapters, in: A New Architecture for the US National Accounts, pages 309-354, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Dylan G. Rassier & Thomas F. Howell III & Edward T. Morgan & Nicholas R. Empey & Conrad E. Roesch, 2007. "Implementing a Reconciliation and Balancing Model in the U.s. Industry Accounts," BEA Papers 0078, Bureau of Economic Analysis.
    2. 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.
    3. Reinier Bikker & Jacco Daalmans & Nino Mushkudiani, 2013. "Benchmarking Large Accounting Frameworks: A Generalized Multivariate Model," Economic Systems Research, Taylor & Francis Journals, vol. 25(4), pages 390-408, December.

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    More about this item

    JEL classification:

    • E60 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - General

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