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National Accounts Estimation Using Indicator Ratios

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  • Jan R. Magnus
  • Jan W. van Tongeren
  • Aart F. de Vos

Abstract

We propose a new approach to national accounts compilation, which also serves as a formalization of current compilation practices. When formalizing the procedure, a distinction is made between (basic) data, national accounts identities and so‐called indicator ratios. The latter are ratios of or percentage relations between national accounts variables, such as the relation between output and value added. Indicator ratios are currently used in national accounts compilation practices in order to make adjustments to the basic data or to fill in missing data. The latter use is particularly relevant when basic data are scarce, which is the case not only in many developing countries, but also in developed countries when annual accounts are compiled for recent periods. The (basic) data, indicator ratios and identities together are used in a Bayesian approach to estimate the values of national accounts variables and analytical indicator ratios based thereon. The amendment of the current practices consists in introducing reliability intervals of basic data and indicator ratios, which allows for the use of a much larger number of indicator ratios in the compilation and checking of national accounts data. The Bayesian compilation approach makes it possible–in contrast to current practices–to use indicator ratios both as priors and as analytical indicators.

Suggested Citation

  • Jan R. Magnus & Jan W. van Tongeren & Aart F. de Vos, 2000. "National Accounts Estimation Using Indicator Ratios," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 46(3), pages 329-350, September.
    • Handle: RePEc:bla:revinw:v:46:y:2000:i:3:p:329-350
    DOI: 10.1111/j.1475-4991.2000.tb00846.x
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    Cited by:

    1. Van Tongeren, J.W. & Magnus, J.R., 2011. "Bayesian Integration of Large Scale SNA Data Frameworks with an Application to Guatemala," Other publications TiSEM 7a0ed98e-134b-4fa4-a97c-4, Tilburg University, School of Economics and Management.
    2. Van Tongeren, J.W. & Magnus, J.R., 2011. "Bayesian Integration of Large Scale SNA Data Frameworks with an Application to Guatemala," Discussion Paper 2011-022, Tilburg University, Center for Economic Research.
    3. Ton de Waal & Arnout van Delden & Sander Scholtus, 2020. "Multi‐source Statistics: Basic Situations and Methods," International Statistical Review, International Statistical Institute, vol. 88(1), pages 203-228, April.
    4. Danilov, Dmitry & Magnus, Jan R., 2008. "On the estimation of a large sparse Bayesian system: The Snaer program," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4203-4224, May.
    5. Bos, Frits, 2007. "Compiling the national accounts demystified," MPRA Paper 3736, University Library of Munich, Germany.
    6. Van Tongeren, J.W., 2011. "From national accounting to the design, compilation, and use of bayesian policy and analysis frameworks," Other publications TiSEM e2d6399b-fdf5-4147-b414-3, Tilburg University, School of Economics and Management.
    7. van Tongeren, Jan W. & Bruil, Arjan, 2022. "Projections to 2025 of the household sector within the Dutch economy," The Journal of the Economics of Ageing, Elsevier, vol. 23(C).
    8. Umed Temurshoev, 2015. "Uncertainty treatment in input-output analysis," Working Papers 2015-004, Universidad Loyola Andalucía, Department of Economics.

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