IDEAS home Printed from https://ideas.repec.org/a/bkr/journl/v83y2024i2p3-25.html
   My bibliography  Save this article

DYFARUS: Dynamic Factor Model to Forecast GDP by Output Using Input-Output Tables

Author

Listed:
  • Anastasia Mogilat

    (Bank of Russia)

  • Oleg Kryzhanovskiy

    (Bank of Russia; Tyumen State University)

  • Zhanna Shuvalova

    (Bank of Russia)

  • Yaroslav Murashov

    (Bank of Russia)

Abstract

DYFARUS is a dynamic factor model used for forecasting GDP. It applies cross-sectoral linkages in the Russian economy using input-output tables. The model is evaluated using an expectation maximisation algorithm and the Kalman filter on Russian and global statistics from January 2011 to December 2022. The model uses quantitative links between economic variables assessed on actual data, which progressively produce forecast values for all sectors reviewed. This study forecasts and evaluates the contributions of each sector to the future GDP path.

Suggested Citation

  • Anastasia Mogilat & Oleg Kryzhanovskiy & Zhanna Shuvalova & Yaroslav Murashov, 2024. "DYFARUS: Dynamic Factor Model to Forecast GDP by Output Using Input-Output Tables," Russian Journal of Money and Finance, Bank of Russia, vol. 83(2), pages 3-25, June.
    • Handle: RePEc:bkr:journl:v:83:y:2024:i:2:p:3-25
    as

    Download full text from publisher

    File URL: https://rjmf.econs.online/upload/iblock/c30/jf07h88m4wwavl1lnz625qx3odydaquq/Dynamic-Factor-Model-Forecast-GDP-Using-Input-Output-Tables.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Andrew T. Foerster & Pierre-Daniel G. Sarte & Mark W. Watson, 2011. "Sectoral versus Aggregate Shocks: A Structural Factor Analysis of Industrial Production," Journal of Political Economy, University of Chicago Press, vol. 119(1), pages 1-38.
    2. 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.
    3. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
    4. Daniel Hopp, 2022. "Benchmarking Econometric and Machine Learning Methodologies in Nowcasting," Papers 2205.03318, arXiv.org.
    5. Dobronravova, E., 2022. "Industry effects of monetary policy in Russia: Econometric analysis," Journal of the New Economic Association, New Economic Association, vol. 55(3), pages 45-60.
    6. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    7. Daron Acemoglu & Vasco M. Carvalho & Asuman Ozdaglar & Alireza Tahbaz‐Salehi, 2012. "The Network Origins of Aggregate Fluctuations," Econometrica, Econometric Society, vol. 80(5), pages 1977-2016, September.
    8. Kuzin, Vladimir & Marcellino, Massimiliano & Schumacher, Christian, 2011. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the euro area," International Journal of Forecasting, Elsevier, vol. 27(2), pages 529-542.
    9. Marie Bessec & Catherine Doz, 2014. "Short-term forecasting of French GDP growth using dynamic factor models," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2013(2), pages 11-50.
    10. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    11. L. Rachel Ngai & Christopher A. Pissarides, 2007. "Structural Change in a Multisector Model of Growth," American Economic Review, American Economic Association, vol. 97(1), pages 429-443, March.
    12. Ernest Liu & Aleh Tsyvinski, 2020. "Dynamical Structure and Spectral Properties of Input-Output Networks," NBER Working Papers 28178, National Bureau of Economic Research, Inc.
    13. Mansor H. Ibrahim, 2005. "Sectoral Effects of Monetary Policy: Evidence from Malaysia," Asian Economic Journal, East Asian Economic Association, vol. 19(1), pages 83-102, March.
    14. Michael Zhemkov, 2021. "Nowcasting Russian GDP using forecast combination approach," International Economics, CEPII research center, issue 168, pages 10-24.
    15. repec:dau:papers:123456789/14465 is not listed on IDEAS
    16. A. Pestova & M. Mamonov, 2016. "A survey of methods for macroeconomic forecasting:looking for perspective directions in russia," Voprosy Ekonomiki, NP Voprosy Ekonomiki, issue 6.
    17. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1.
    18. Schumacher, Christian, 2016. "A comparison of MIDAS and bridge equations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 257-270.
    19. 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.
    20. Joe Ganley & Chris Salmon, 1997. "The Industrial Impact of Monetary Policy Shocks: Some Stylised Facts," Bank of England working papers 68, Bank of England.
    21. M. Y. Gareev & A. V. Polbin, 2022. "Nowcasting Russia’s key macroeconomic variables using machine learning," Voprosy Ekonomiki, NP Voprosy Ekonomiki, issue 8.
    22. Domenico Giannone & Lucrezia Reichlin & David H. Small, 2005. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Finance and Economics Discussion Series 2005-42, Board of Governors of the Federal Reserve System (U.S.).
    23. Cimadomo, Jacopo & Giannone, Domenico & Lenza, Michele & Monti, Francesca & Sokol, Andrej, 2022. "Nowcasting with large Bayesian vector autoregressions," Journal of Econometrics, Elsevier, vol. 231(2), pages 500-519.
    24. Elena Angelini & Marta Banbura & Gerhard Rünstler, 2010. "Estimating and forecasting the euro area monthly national accounts from a dynamic factor model," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2010(1), pages 1-22.
    25. Barış Soybilgen & Ege Yazgan, 2021. "Nowcasting US GDP Using Tree-Based Ensemble Models and Dynamic Factors," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 387-417, January.
    26. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
    27. Katja Heinisch & Rolf Scheufele, 2018. "Bottom-up or direct? Forecasting German GDP in a data-rich environment," Empirical Economics, Springer, vol. 54(2), pages 705-745, March.
    28. A. Pestova & M. Mamonov., 2016. "A survey of methods for macroeconomic forecasting: Looking for perspective directions in Russia," VOPROSY ECONOMIKI, N.P. Redaktsiya zhurnala "Voprosy Economiki", vol. 6.
    29. Marie Bessec & Catherine Doz, 2014. "Short-term forecasting of French GDP growth using dynamic factor models," Post-Print hal-01515602, HAL.
    30. Pestova, Anna (Пестова, Анна) & Mamonov, Mikhail (Мамонов, Михаил), 2016. "Estimating the Influence of Different Shocks on Macroeconomic Indicators and Developing Conditional Forecasts on the Basis of BVAR Model for the Russian Economy [Оценка Влияния Различных Шоков На Д," Ekonomicheskaya Politika / Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 4, pages 56-92, August.
    31. Aleksey Ponomarenko & Svetlana Popova & Andrey Sinyakov & Natalia Turdyeva & Dmitry Chernyadyev, 2020. "Assessing the Consequences of the Pandemic for the Russian Economy Through an Input-Output Model," Russian Journal of Money and Finance, Bank of Russia, vol. 79(4), pages 3-17, December.
    32. Henkel, Lukas, 2020. "Sectoral output effects of monetary policy: do sticky prices matter?," Working Paper Series 2473, European Central Bank.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Danilo Cascaldi-Garcia & Matteo Luciani & Michele Modugno, 2023. "Lessons from Nowcasting GDP across the World," International Finance Discussion Papers 1385, Board of Governors of the Federal Reserve System (U.S.).
    2. Hanan Naser, 2015. "Estimating and forecasting Bahrain quarterly GDP growth using simple regression and factor-based methods," Empirical Economics, Springer, vol. 49(2), pages 449-479, September.
    3. Anastasiia Pankratova, 2024. "Forecasting Key Macroeconomic Indicators Using DMA and DMS Methods," Russian Journal of Money and Finance, Bank of Russia, vol. 83(1), pages 32-52, March.
    4. Aastveit, Knut Are & Trovik, Tørres, 2014. "Estimating the output gap in real time: A factor model approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(2), pages 180-193.
    5. Baumeister, Christiane & Guérin, Pierre, 2021. "A comparison of monthly global indicators for forecasting growth," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1276-1295.
    6. Antipa, Pamfili & Barhoumi, Karim & Brunhes-Lesage, Véronique & Darné, Olivier, 2012. "Nowcasting German GDP: A comparison of bridge and factor models," Journal of Policy Modeling, Elsevier, vol. 34(6), pages 864-878.
    7. Stankevich, Ivan, 2023. "Application of Markov-Switching MIDAS models to nowcasting of GDP and its components," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 70, pages 122-143.
    8. Cascaldi-Garcia, Danilo & Ferreira, Thiago R.T. & Giannone, Domenico & Modugno, Michele, 2024. "Back to the present: Learning about the euro area through a now-casting model," International Journal of Forecasting, Elsevier, vol. 40(2), pages 661-686.
    9. Jansen, W. Jos & Jin, Xiaowen & de Winter, Jasper M., 2016. "Forecasting and nowcasting real GDP: Comparing statistical models and subjective forecasts," International Journal of Forecasting, Elsevier, vol. 32(2), pages 411-436.
    10. Chudik, Alexander & Grossman, Valerie & Pesaran, M. Hashem, 2016. "A multi-country approach to forecasting output growth using PMIs," Journal of Econometrics, Elsevier, vol. 192(2), pages 349-365.
    11. Jos Jansen & Jasper de Winter, 2016. "Improving model-based near-term GDP forecasts by subjective forecasts: A real-time exercise for the G7 countries," DNB Working Papers 507, Netherlands Central Bank, Research Department.
    12. Mahmut Gunay, 2020. "Nowcasting Turkish GDP with MIDAS: Role of Functional Form of the Lag Polynomial," Working Papers 2002, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    13. Michael Zhemkov, 2021. "Nowcasting Russian GDP using forecast combination approach," International Economics, CEPII research center, issue 168, pages 10-24.
    14. Schumacher Christian, 2011. "Forecasting with Factor Models Estimated on Large Datasets: A Review of the Recent Literature and Evidence for German GDP," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 28-49, February.
    15. Chernis, Tony & Cheung, Calista & Velasco, Gabriella, 2020. "A three-frequency dynamic factor model for nowcasting Canadian provincial GDP growth," International Journal of Forecasting, Elsevier, vol. 36(3), pages 851-872.
    16. Smith Paul, 2016. "Nowcasting UK GDP during the depression," Working Papers 1606, University of Strathclyde Business School, Department of Economics.
    17. Marina Diakonova & Luis Molina & Hannes Mueller & Javier J. Pérez & Cristopher Rauh, 2022. "The information content of conflict, social unrest and policy uncertainty measures for macroeconomic forecasting," Working Papers 2232, Banco de España.
    18. Dennis Kant & Andreas Pick & Jasper de Winter, 2022. "Nowcasting GDP using machine learning methods," Working Papers 754, DNB.
    19. Hindrayanto, Irma & Koopman, Siem Jan & de Winter, Jasper, 2016. "Forecasting and nowcasting economic growth in the euro area using factor models," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1284-1305.
    20. Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013. "Now-Casting and the Real-Time Data Flow," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 195-237, Elsevier.

    More about this item

    Keywords

    GDP; sectoral analysis; dynamic factor model; Kalman filter; input-output tables;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • C67 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Input-Output Models

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bkr:journl:v:83:y:2024:i:2:p:3-25. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Olga Kuvshinova (email available below). General contact details of provider: https://edirc.repec.org/data/cbrgvru.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.