IDEAS home Printed from https://ideas.repec.org/a/bkr/journl/v83y2024i2p92-111.html

New Approaches to Measuring, Analysing, and Forecasting Prices: A Review of the Bank of Russia, NES, and HSE University Workshop

Author

Listed:
  • Vadim Grishchenko

    (Bank of Russia; HSE University)

  • Ivan Krylov

    (Bank of Russia; HSE University)

Abstract

At the end of March 2024, the Bank of Russia, the New Economic School, and the Bank of Russia's Department at the HSE University held the 11th international workshop titled 'New Approaches to Measuring, Analysing, and Forecasting Prices'. The workshop participants presented their research focused on inflation forecasting aided by machine learning and the use of new data to investigate price changes. The researchers discussed the effectiveness of machine learning algorithms compared with traditional econometrics.

Suggested Citation

  • Vadim Grishchenko & Ivan Krylov, 2024. "New Approaches to Measuring, Analysing, and Forecasting Prices: A Review of the Bank of Russia, NES, and HSE University Workshop," Russian Journal of Money and Finance, Bank of Russia, vol. 83(2), pages 92-111, June.
    • Handle: RePEc:bkr:journl:v:83:y:2024:i:2:p:92-111
    as

    Download full text from publisher

    File URL: https://rjmf.econs.online/upload/iblock/726/itc36r3nugqqltexqui44us2h211mzi8/Measuring-Analysing-Forecasting-Prices.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. A. P. Lerner, 1934. "The Concept of Monopoly and the Measurement of Monopoly Power," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 1(3), pages 157-175.
    2. Stefano DellaVigna & Matthew Gentzkow, 2019. "Uniform Pricing in U.S. Retail Chains," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 134(4), pages 2011-2084.
    3. Vadim Grishchenko & Diana Gasanova & Egor Fomin & Grigory Korenyak, 2023. "Visible prices and their influence on inflation expectations of Russian households," Bank of Russia Working Paper Series wps117, Bank of Russia.
    4. Chiranjit Chakraborty & Andreas Joseph, 2017. "Machine learning at central banks," Bank of England working papers 674, Bank of England.
    5. Olivier Armantier & Wändi Bruine de Bruin & Simon Potter & Giorgio Topa & Wilbert van der Klaauw & Basit Zafar, 2013. "Measuring Inflation Expectations," Annual Review of Economics, Annual Reviews, vol. 5(1), pages 273-301, May.
    6. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    7. 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.
    8. Lenza, Michele & Moutachaker, Inès & Paredes, Joan, 2023. "Forecasting euro area inflation with machine-learning models," Research Bulletin, European Central Bank, vol. 112.
    9. Di, Wenhua & Pattison, Nathaniel, 2023. "Industry Specialization and Small Business Lending," Journal of Banking & Finance, Elsevier, vol. 149(C).
    10. Francesco D’Acunto & Ulrike Malmendier & Juan Ospina & Michael Weber, 2021. "Exposure to Grocery Prices and Inflation Expectations," Journal of Political Economy, University of Chicago Press, vol. 129(5), pages 1615-1639.
    11. Maliar, Lilia & Maliar, Serguei & Winant, Pablo, 2021. "Deep learning for solving dynamic economic models," Journal of Monetary Economics, Elsevier, vol. 122(C), pages 76-101.
    12. Evgeny Pavlov, 2020. "Forecasting Inflation in Russia Using Neural Networks," Russian Journal of Money and Finance, Bank of Russia, vol. 79(1), pages 57-73, March.
    13. Joel Dyer & Patrick Cannon & J. Doyne Farmer & Sebastian Schmon, 2022. "Black-box Bayesian inference for economic agent-based models," Papers 2202.00625, arXiv.org.
    14. Araujo, Gustavo Silva & Gaglianone, Wagner Piazza, 2023. "Machine learning methods for inflation forecasting in Brazil: New contenders versus classical models," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 4(2).
    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. Sengupta, Shovon & Chakraborty, Tanujit & Singh, Sunny Kumar, 2025. "Forecasting CPI inflation under economic policy and geopolitical uncertainties," International Journal of Forecasting, Elsevier, vol. 41(3), pages 953-981.
    2. Helder Ferreira de Mendonça & João Pedro Neves Maia, 2022. "Interest rate expectations based on Taylor rule versus central bank’s survey: which performs better in a large emerging economy?," Applied Economics, Taylor & Francis Journals, vol. 54(39), pages 4532-4544, August.
    3. Rodion Latypov & Elena Akhmedova & Egor Postolit & Marina Mikitchuk, 2024. "Bottom-up Inflation Forecasting Using Machine Learning Methods," Russian Journal of Money and Finance, Bank of Russia, vol. 83(3), pages 23-44, September.
    4. Chua, Chew Lian & Tsiaplias, Sarantis, 2024. "The influence of supermarket prices on consumer inflation expectations," Journal of Economic Behavior & Organization, Elsevier, vol. 219(C), pages 414-433.
    5. Kelly Burns & Imad Moosa, 2017. "Demystifying the Meese–Rogoff puzzle: structural breaks or measures of forecasting accuracy?," Applied Economics, Taylor & Francis Journals, vol. 49(48), pages 4897-4910, October.
    6. Yuchen Zhang & Shigeyuki Hamori, 2020. "The Predictability of the Exchange Rate When Combining Machine Learning and Fundamental Models," JRFM, MDPI, vol. 13(3), pages 1-16, March.
    7. Guglielmo Maria Caporale & Juncal Cuñado & Luis A. Gil-Alana, 2013. "Modelling long-run trends and cycles in financial time series data," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(3), pages 405-421, May.
    8. Carlo Altavilla & Paul De Grauwe, 2010. "Forecasting and combining competing models of exchange rate determination," Applied Economics, Taylor & Francis Journals, vol. 42(27), pages 3455-3480.
    9. Xiaojie Xu, 2017. "The rolling causal structure between the Chinese stock index and futures," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 31(4), pages 491-509, November.
    10. Tallman, Ellis W. & Zaman, Saeed, 2020. "Combining survey long-run forecasts and nowcasts with BVAR forecasts using relative entropy," International Journal of Forecasting, Elsevier, vol. 36(2), pages 373-398.
    11. Daniel Buncic, 2012. "Understanding forecast failure of ESTAR models of real exchange rates," Empirical Economics, Springer, vol. 43(1), pages 399-426, August.
    12. Kevin Curran & Emerson Krasusky, 2025. "Regional Spotlight: Introducing the Price and Inflation Expectations Survey," Regional Spotlight, Federal Reserve Bank of Philadelphia, pages 1-7, December.
    13. Scott Brave & R. Andrew Butters & Alejandro Justiniano, 2016. "Forecasting Economic Activity with Mixed Frequency Bayesian VARs," Working Paper Series WP-2016-5, Federal Reserve Bank of Chicago.
    14. Keen Meng Choy & Hwee Kwan Chow, 2004. "Forecasting the Global Electronics Cycle with Leading Indicators: A VAR Approach," Econometric Society 2004 Australasian Meetings 223, Econometric Society.
    15. Ard H.J. den Reijer, 2005. "Forecasting Dutch GDP using Large Scale Factor Models," DNB Working Papers 028, Netherlands Central Bank, Research Department.
    16. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Realtime nowcasting with a Bayesian mixed frequency model with stochastic volatility," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(4), pages 837-862, October.
    17. Kruse, Robinson & Leschinski, Christian & Will, Michael, 2016. "Comparing Predictive Accuracy under Long Memory - With an Application to Volatility Forecasting," Hannover Economic Papers (HEP) dp-571, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    18. Mogliani, Matteo & Darné, Olivier & Pluyaud, Bertrand, 2017. "The new MIBA model: Real-time nowcasting of French GDP using the Banque de France's monthly business survey," Economic Modelling, Elsevier, vol. 64(C), pages 26-39.
    19. Balcilar, Mehmet & Katzke, Nico & Gupta, Rangan, 2017. "Do precious metal prices help in forecasting South African inflation?," The North American Journal of Economics and Finance, Elsevier, vol. 40(C), pages 63-72.
    20. Michael S. Smith & Shaun P. Vahey, 2016. "Asymmetric Forecast Densities for U.S. Macroeconomic Variables from a Gaussian Copula Model of Cross-Sectional and Serial Dependence," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(3), pages 416-434, July.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E50 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - General
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

    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:92-111. 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.