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Methods for Estimating the Gross Regional Product Leading Indicator

Author

Listed:
  • Vladimir Boyko

    (Bank of Russia)

  • Nadezhda Kislyak

    (Bank of Russia)

  • Mikhail Nikitin

    (Bank of Russia)

  • Oleg Oborin

    (Bank of Russia)

Abstract

This paper discusses two methods for estimating the quarterly values of the gross regional product (GRP) leading indicator. The first method is based on Rosstat methodology using the growth rates of indicators that reflect the output for main economic activities in the region. The second method uses temporal disaggregation (disaggregation in time). A distinctive feature of the second method is the possibility of obtaining high-frequency series using not only the indicators specified in Rosstat methodology but also other variables reflecting the dynamics of business activity in regions. The research suggests that temporal disaggregation methods provide more accurate estimates of quarterly values of the physical GRP volume index as compared to methods based on Rosstat methodology. The particular temporal disaggregation model used to forecast GRP for seven federal districts (i.e., all except the North Caucasian District) is chosen based on the performance in forecasting the gross domestic product (GDP) volume, which is close in economic terms to the overall GRP for Russia.

Suggested Citation

  • Vladimir Boyko & Nadezhda Kislyak & Mikhail Nikitin & Oleg Oborin, 2020. "Methods for Estimating the Gross Regional Product Leading Indicator," Russian Journal of Money and Finance, Bank of Russia, vol. 79(3), pages 3-29, September.
    • Handle: RePEc:bkr:journl:v:79:y:2020:i:3:p:3-29
    DOI: 10.31477/rjmf.202003.03
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    References listed on IDEAS

    as
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    2. Sax, Christoph & Steiner, Peter, 2013. "Temporal Disaggregation of Time Series," MPRA Paper 53389, University Library of Munich, Germany.
    3. Heiner Mikosch & Laura Solanko, 2019. "Forecasting Quarterly Russian GDP Growth with Mixed-Frequency Data," Russian Journal of Money and Finance, Bank of Russia, vol. 78(1), pages 19-35, March.
    4. Litterman, Robert B, 1983. "A Random Walk, Markov Model for the Distribution of Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(2), pages 169-173, April.
    5. Wolfang Polasek & Carlos Llano & Richard Sellner, 2010. "Bayesian Methods for Completing Data in Spatial Models," Review of Economic Analysis, Digital Initiatives at the University of Waterloo Library, vol. 2(2), pages 194-214, June.
    6. Porshakov, A. & Ponomarenko, A. & Sinyakov, A., 2016. "Nowcasting and Short-Term Forecasting of Russian GDP with a Dynamic Factor Model," Journal of the New Economic Association, New Economic Association, vol. 30(2), pages 60-76.
    7. James Mitchell & Richard J. Smith & Martin R. Weale & Stephen Wright & Eduardo L. Salazar, 2005. "An Indicator of Monthly GDP and an Early Estimate of Quarterly GDP Growth," Economic Journal, Royal Economic Society, vol. 115(501), pages 108-129, February.
    8. Gulasekaran Rajaguru & Tilak Abeysinghe, 2004. "Quarterly real GDP estimates for China and ASEAN4 with a forecast evaluation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 431-447.
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    Citations

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    Cited by:

    1. Oleg Kryzhanovsky & Alexander Zykov, 2022. "DEMUR: A Regional Semi-Structural Model of the Ural Macroregion," Russian Journal of Money and Finance, Bank of Russia, vol. 81(4), pages 52-85, December.
    2. Alyona Nelyubina, 2022. "Monetary Policy Impact on Income Inequality in the Russian Regions," Russian Journal of Money and Finance, Bank of Russia, vol. 81(2), pages 3-19, June.
    3. Michael Zhemkov, 2022. "Assessment of Monthly GDP Growth Using Temporal Disaggregation Methods," Russian Journal of Money and Finance, Bank of Russia, vol. 81(2), pages 79-104, June.
    4. Sergey V. Arzhenovskiy, 2024. "Forecasting GDP Dynamics Based on the Bank of Russia’s Enterprise Monitoring Data," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 1, pages 31-44, February.

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    More about this item

    Keywords

    leading indicator; gross regional product; temporal disaggregation; Chow-Lin; Litterman; Fernandez;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • E23 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Production
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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