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A Balanced System of U.S. Industry Accounts and Distribution of the Aggregate Statistical Discrepancy by Industry

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  • Baoline Chen

Abstract

This article describes and illustrates a generalized least squares (GLS) method that systematically incorporates all available information on the reliability of initial data in the reconciliation of a large disaggregated system of national accounts. The GLS method is applied to reconciling the 1997 U.S. Input-Output and Gross Domestic Product (GDP)-by-industry accounts with benchmarked GDP estimated from expenditures. The GLS procedure produced a balanced system of industry accounts and distributed the aggregate statistical discrepancy by industry according to the estimated relative reliabilities of initial estimates. The study demonstrates the empirical feasibility and computational efficiency of the GLS method for large accounts reconciliation.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:jnlbes:v:30:y:2012:i:2:p:202-211
    DOI: 10.1080/07350015.2012.669667
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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 U.S. National Accounts, pages 309-354, National Bureau of Economic Research, Inc.
    2. 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.
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    Cited by:

    1. Baoline Chen & Tommaso Di Fonzo & Thomas Howells & Marco Marini, 2018. "The statistical reconciliation of time series of accounts between two benchmark revisions," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 533-552, November.
    2. Baoline Chen & Tommaso Di Fonzo & Thomas Howells & Marco Marini, 2014. "The Statistical Reconciliation of Time Series of Accounts after a Benchmark Revision," BEA Working Papers 0117, Bureau of Economic Analysis.
    3. Víctor M. Guerrero & Francisco Corona, 2018. "Retropolating some relevant series of Mexico's System of National Accounts at constant prices: The case of Mexico City's GDP," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 495-519, November.
    4. Tucker McElroy, 2018. "Seasonal adjustment subject to accounting constraints," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 574-589, November.
    5. Enrique M. Quilis, 2018. "Temporal disaggregation of economic time series: The view from the trenches," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 447-470, November.

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    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.

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