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The Reconciliation of Values, Volumes and Prices in the National Accounts

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  • Martin Weale

Abstract

The problem of reconciling constant and current price national income accounts is considered. It is necessary to take account of the fact that both additive and multiplicative restrictions exist between volume and value estimates and the related deflators. Covariances, implied by the sources from which the data are constructed, must also be considered. The accounts can then be balanced. Although a linear approximation is used to take account of the multiplicative restrictions, this approximation is found to be perfectly adequate.

Suggested Citation

  • Martin Weale, 1988. "The Reconciliation of Values, Volumes and Prices in the National Accounts," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 151(1), pages 211-221, January.
    • Handle: RePEc:bla:jorssa:v:151:y:1988:i:1:p:211-221
    DOI: 10.2307/2982193
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    Cited by:

    1. Daniel Kosiorowski & Dominik Mielczarek & Jerzy P. Rydlewski & Małgorzata Snarska, 2018. "Generalized Exponential Smoothing In Prediction Of Hierarchical Time Series," Statistics in Transition New Series, Polish Statistical Association, vol. 19(2), pages 331-350, June.
    2. Fredrik N G Andersson & Jason Lennard, 2019. "Irish GDP between the Famine and the First World War: estimates based on a dynamic factor model," European Review of Economic History, European Historical Economics Society, vol. 23(1), pages 50-71.
    3. Daniel Kosiorowski & Dominik Mielczarek & Jerzy P. Rydlewski, 2018. "Forecasting of a Hierarchical Functional Time Series on Example of Macromodel for the Day and Night Air Pollution in Silesia Region - A Critical Overview," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 10(1), pages 53-73, March.
    4. Hyndman, Rob J. & Ahmed, Roman A. & Athanasopoulos, George & Shang, Han Lin, 2011. "Optimal combination forecasts for hierarchical time series," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2579-2589, September.
    5. Han Lin Shang, 2017. "Reconciling Forecasts of Infant Mortality Rates at National and Sub-National Levels: Grouped Time-Series Methods," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 36(1), pages 55-84, February.
    6. Patterson, K. D., 2003. "Exploiting information in vintages of time-series data," International Journal of Forecasting, Elsevier, vol. 19(2), pages 177-197.
    7. Kosiorowski Daniel & Mielczarek Dominik & Rydlewski Jerzy P. & Snarska Małgorzata, 2018. "Generalized Exponential Smoothing In Prediction Of Hierarchical Time Series," Statistics in Transition New Series, Polish Statistical Association, vol. 19(2), pages 331-350, June.
    8. Pennings, Clint L.P. & van Dalen, Jan, 2017. "Integrated hierarchical forecasting," European Journal of Operational Research, Elsevier, vol. 263(2), pages 412-418.
    9. Charles H. Feinstein & Mark Thomas, 2001. "A Plea for Errors," Oxford Economic and Social History Working Papers _041, University of Oxford, Department of Economics.
    10. Charles H. Feinstein & Mark Thomas, 2001. "A Plea for Errors," Economics Series Working Papers 2001-W41, University of Oxford, Department of Economics.
    11. Lugovoy, Oleg (Луговой, Олег) & Polbin, Andrey Vladimirovich (Полбин, Андрей Владимирович) & Potashnikоv, Vladimir Yurievich (Поташников, Владимир Юрьевич), 2015. "Bayesian Approach to the Extension of 'Input-Output' Tables [Байесовский Подход К Продлению Таблиц «Затраты–Выпуск»]," Published Papers om31, Russian Presidential Academy of National Economy and Public Administration.
    12. Li, Han & Li, Hong & Lu, Yang & Panagiotelis, Anastasios, 2019. "A forecast reconciliation approach to cause-of-death mortality modeling," Insurance: Mathematics and Economics, Elsevier, vol. 86(C), pages 122-133.
    13. Daniel Kosiorowski & Dominik Mielczarek & Jerzy. P. Rydlewski, 2017. "Forecasting of a Hierarchical Functional Time Series on Example of Macromodel for Day and Night Air Pollution in Silesia Region: A Critical Overview," Papers 1712.03797, arXiv.org.
    14. Leprince, Julien & Madsen, Henrik & Møller, Jan Kloppenborg & Zeiler, Wim, 2023. "Hierarchical learning, forecasting coherent spatio-temporal individual and aggregated building loads," Applied Energy, Elsevier, vol. 348(C).
    15. Daniel Kosiorowski & Dominik Mielczarek & Jerzy P. Rydlewski, 2017. "Aggregated moving functional median in robust prediction of hierarchical functional time series - an application to forecasting web portal users behaviors," Papers 1710.02669, arXiv.org, revised Jul 2018.

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