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On the Extrapolation with the Denton Proportional Benchmarking Method

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  • Mr. Marco Marini
  • Mr. Tommaso Di Fonzo

Abstract

Statistical offices have often recourse to benchmarking methods for compiling quarterly national accounts (QNA). Benchmarking methods employ quarterly indicator series (i) to distribute annual, more reliable series of national accounts and (ii) to extrapolate the most recent quarters not yet covered by annual benchmarks. The Proportional First Differences (PFD) benchmarking method proposed by Denton (1971) is a widely used solution for distribution, but in extrapolation it may suffer when the movements in the indicator series do not match consistently the movements in the target annual benchmarks. For this reason, an enhanced formula for extrapolation was recommended by the IMF’s Quarterly National Accounts Manual: Concepts, Data Sources, and Compilation (2001). We discuss the rationale behind this technique, and propose a matrix formulation of it. In addition, we present applications of the enhanced formula to artificial and real-life benchmarking examples showing how the extrapolations for the most recent quarters can be improved.

Suggested Citation

  • Mr. Marco Marini & Mr. Tommaso Di Fonzo, 2012. "On the Extrapolation with the Denton Proportional Benchmarking Method," IMF Working Papers 2012/169, International Monetary Fund.
    • Handle: RePEc:imf:imfwpa:2012/169
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    References listed on IDEAS

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    1. Mr. Marco Marini & Mr. Tommaso Di Fonzo, 2011. "A Newton's Method for Benchmarking Time Series According to a Growth Rates Preservation Principle," IMF Working Papers 2011/179, International Monetary Fund.
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    Cited by:

    1. Wang,Dieter, 2021. "Natural Capital and Sovereign Bonds," Policy Research Working Paper Series 9606, The World Bank.
    2. Mr. Marco Marini & Mr. Robert Dippelsman & Mr. Michael Stanger, 2018. "New Estimates for Direction of Trade Statistics," IMF Working Papers 2018/016, International Monetary Fund.
    3. He, Xi & Lopez, Rigoberto A. & Liu, Yizao, 2015. "Substitution between Online and Offline Advertising: Evidence from the Carbonated Soft Drink Industry," Working Papers 37, University of Connecticut, Department of Agricultural and Resource Economics, Charles J. Zwick Center for Food and Resource Policy.
    4. Elva BOVA & João TOVAR JALLES & Christina KOLERUS, 2018. "Shifting the Beveridge curve: What affects labour market matching?," International Labour Review, International Labour Organization, vol. 157(2), pages 267-306, June.
    5. Sami Ben Naceur & Amr Hosny & Gregory Hadjian, 2019. "How to de-dollarize financial systems in the Caucasus and Central Asia?," Empirical Economics, Springer, vol. 56(6), pages 1979-1999, June.
    6. repec:eur:ejfejr:42 is not listed on IDEAS
    7. Raïmi Aboudou Essessinou & Guy Degla, 2020. "Using a Fast Elitist Non-Dominated Genetic Algorithm on Multiobjective Programming for Quarterly Disaggregation of the Gross Domestic Product," European Journal of Engineering and Formal Sciences Articles, European Center for Science Education and Research, vol. 4, January -.
    8. Jacco Daalmans, 2018. "On the sequential benchmarking of subannual series to annual totals," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 406-420, November.
    9. Guilherme Klein Martins & Fernando Rugitsky, 2021. "The Long Expansion and the Profit Squeeze: Output and Profit Cycles in Brazil (1996–2016)," Review of Radical Political Economics, Union for Radical Political Economics, vol. 53(3), pages 373-397, September.
    10. 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.
    11. Samya Beidas-Strom & Marco Lorusso, 2019. "Macroeconomic Effects of Reforms on Three Diverse Oil Exporters: Russia, Saudi Arabia, and the UK," IMF Working Papers 2019/214, International Monetary Fund.

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    WP; Bi ratio;

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