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Benchmarking Large Accounting Frameworks: A Generalized Multivariate Model

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  • Reinier Bikker
  • Jacco Daalmans
  • Nino Mushkudiani

Abstract

We present a multivariate benchmarking model for achieving consistency between large quarterly and annual accounting frameworks. The method is based on a quadratic optimization problem, for which many efficient numeric solvers exist. The method combines several features, such as linear constraints, ratio constraints, weights, and inequalities, in one model. Therefore, a wide range of modelling possibilities is supported. This method is especially interesting for national statistical offices, to simplify their processes to achieve consistency between publications.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:ecsysr:v:25:y:2013:i:4:p:390-408
    DOI: 10.1080/09535314.2013.801010
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    References listed on IDEAS

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    1. 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. Nino Mushkudiani & Jacco Daalmans & Reinier Bikker, 2018. "Solving large‐data consistency problems at Statistics Netherlands using macro‐integration techniques," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 553-573, November.
    2. Luboš Marek & Stanislava Hronová & Richard Hindls, 2017. "Option For Predicting The Czech Republic'S Foreign Trade Time Series As Components In Gross Domestic Product," Statistics in Transition New Series, Polish Statistical Association, vol. 18(3), pages 481-500, September.
    3. Geoffrey Brent, 2018. "Maximum likelihood estimation framework for table‐balancing adjustments," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 520-532, November.
    4. 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.
    5. 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.
    6. Umed Temursho, 2018. "Entropy‐based benchmarking methods," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 421-446, November.
    7. Kazemier Brugt & van Veen Michel & IJmker Sander, 2019. "The unobserved economy and the Dutch national accounts after the benchmark revision 2015," Central European Economic Journal, Sciendo, vol. 6(53), pages 1-24, January.
    8. 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.
    9. 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.
    10. Marek Luboš & Hronová Stanislava & Hindis Richard, 2017. "Option for Predicting the Czech Republic’S Foreign Trade Time Series as Components in Gross Domestic Product," Statistics in Transition New Series, Polish Statistical Association, vol. 18(3), pages 481-500, September.
    11. George Athanasopoulos & Rob J Hyndman & Nikolaos Kourentzes & Anastasios Panagiotelis, 2023. "Forecast Reconciliation: A Review," Monash Econometrics and Business Statistics Working Papers 8/23, Monash University, Department of Econometrics and Business Statistics.
    12. 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.

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