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Multiproduct Model Decomposition of Components of Russian GDP

Author

Listed:
  • Nikolay P. Pilnik

    (National Research University Higher School of Economics)

  • Igor Pospelov

    (National Research University Higher School of Economics)

  • Ivan P. Stankevich

    (National Research University Higher School of Economics)

Abstract

This paper proposes a method for a multiproduct model decomposition of GDP components by expenditure which allows the use of several different price indices in the same model. The decomposition does not link the products to imports or exports, therefore, it imposes no restrictions on the behaviour of these series and their deflators. The theoretical reasoning, the estimation methodology and the estimation results for Russian GDP data are presented. A method of the decomposition of changes in inventories is also presented.

Suggested Citation

  • Nikolay P. Pilnik & Igor Pospelov & Ivan P. Stankevich, 2015. "Multiproduct Model Decomposition of Components of Russian GDP," HSE Working papers WP BRP 111/EC/2015, National Research University Higher School of Economics.
    • Handle: RePEc:hig:wpaper:111/ec/2015
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    References listed on IDEAS

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    1. Máximo Camacho & Rafael Doménech, 2012. "MICA-BBVA: a factor model of economic and financial indicators for short-term GDP forecasting," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 3(4), pages 475-497, December.
    2. A. Espasa & E. Senra & R. Albacete, 2002. "Forecasting inflation in the European Monetary Union: A disaggregated approach by countries and by sectors," The European Journal of Finance, Taylor & Francis Journals, vol. 8(4), pages 402-421.
    3. Hendry, David F. & Hubrich, Kirstin, 2011. "Combining Disaggregate Forecasts or Combining Disaggregate Information to Forecast an Aggregate," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(2), pages 216-227.
    4. Blundell, Richard, 1988. "Consumer Behaviour: Theory and Empirical Evidence--a Survey," Economic Journal, Royal Economic Society, vol. 98(389), pages 16-65, March.
    5. Zellner, Arnold & Tobias, Justin, 1998. "A Note on Aggregation, Disaggregation and Forecasting Performance," CUDARE Working Papers 198677, University of California, Berkeley, Department of Agricultural and Resource Economics.
    6. Mandel, Antoine & Landini, Simone & Gallegati, Mauro & Gintis, Herbert, 2015. "Price dynamics, financial fragility and aggregate volatility," Journal of Economic Dynamics and Control, Elsevier, vol. 51(C), pages 257-277.
    7. Keller, Wouter J., 1976. "A nested CES-type utility function and its demand and price-index functions," European Economic Review, Elsevier, vol. 7(2), pages 175-186, February.
    8. Antoine Mandel & Carlo Jaeger & Steffen Fürst & Wiebke Lass & Daniel Lincke & Frank Meissner & Federico Pablo-Marti & Sarah Wolf, 2010. "Agent-based dynamics in disaggregated growth models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00542442, HAL.
    9. Ravazzolo Francesco & Vahey Shaun P., 2014. "Forecast densities for economic aggregates from disaggregate ensembles," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 18(4), pages 1-15, September.
    10. Kim, Kun Ho, 2011. "Density forecasting through disaggregation," International Journal of Forecasting, Elsevier, vol. 27(2), pages 394-412.
    11. Karim Barhoumi & Olivier Darné & Laurent Ferrara & Bertrand Pluyaud, 2012. "Monthly Gdp Forecasting Using Bridge Models: Application For The French Economy," Bulletin of Economic Research, Wiley Blackwell, vol. 64(Supplemen), pages 53-70, December.
    12. repec:bla:buecrs:v:64:y:2012:i::p:s53-s70 is not listed on IDEAS
    13. Kim, Kun Ho, 2011. "Density forecasting through disaggregation," International Journal of Forecasting, Elsevier, vol. 27(2), pages 394-412, April.
    14. Вржещ В. П. & Поспелов И. Г. & Хохлов М. А., 2010. "Модельное Дезагрегирование Макроэкономической Статистики," Higher School of Economics Economic Journal Экономический журнал Высшей школы экономики, CyberLeninka;Федеральное государственное автономное образовательное учреждение высшего образования «Национальный исследовательский университет «Высшая школа экономики», vol. 14(1), pages 88-104.
    15. Blackorby, Charles & Boyce, Richard & Russell, R Robert, 1978. "Estimation of Demand Systems Generated by the Gorman Polar Form: A Generalization of the S-Branch Utility Tree," Econometrica, Econometric Society, vol. 46(2), pages 345-363, March.
    16. Brown, Murray & Heien, Dale M, 1972. "The S-Branch Utility Tree: A Generalization of the Linear Expenditure System," Econometrica, Econometric Society, vol. 40(4), pages 737-747, July.
    17. David L. Edgerton, 1997. "Weak Separability and the Estimation of Elasticities in Multistage Demand Systems," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 79(1), pages 62-79.
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    More about this item

    Keywords

    GDP components by expenditure; decomposition; utility tree;
    All these keywords.

    JEL classification:

    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models

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