IDEAS home Printed from https://ideas.repec.org/a/bkr/journl/v83y2024i2p92-111.html
   My bibliography  Save this article

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. Chiranjit Chakraborty & Andreas Joseph, 2017. "Machine learning at central banks," Bank of England working papers 674, Bank of England.
    2. Lenza, Michele & Moutachaker, Inès & Paredes, Joan, 2023. "Forecasting euro area inflation with machine-learning models," Research Bulletin, European Central Bank, vol. 112.
    3. 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.
    4. Farmer, J. Doyne & Dyer, Joel & Cannon, Patrick & Schmon, Sebastian, 2022. "Black-box Bayesian inference for economic agent-based models," INET Oxford Working Papers 2022-05, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.
    5. 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).
    6. 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.
    7. 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.
    8. Di, Wenhua & Pattison, Nathaniel, 2023. "Industry Specialization and Small Business Lending," Journal of Banking & Finance, Elsevier, vol. 149(C).
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    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. 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.
    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. Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2020. "Forecasting realized oil-price volatility: The role of financial stress and asymmetric loss," Journal of International Money and Finance, Elsevier, vol. 104(C).
    5. Dal Bianco, Marcos & Camacho, Maximo & Perez Quiros, Gabriel, 2012. "Short-run forecasting of the euro-dollar exchange rate with economic fundamentals," Journal of International Money and Finance, Elsevier, vol. 31(2), pages 377-396.
    6. 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.
    7. Goodness C. Aye & Stephen M. Miller & Rangan Gupta & Mehmet Balcilar, 2016. "Forecasting US real private residential fixed investment using a large number of predictors," Empirical Economics, Springer, vol. 51(4), pages 1557-1580, December.
    8. Chuliá, Helena & Garrón, Ignacio & Uribe, Jorge M., 2024. "Daily growth at risk: Financial or real drivers? The answer is not always the same," International Journal of Forecasting, Elsevier, vol. 40(2), pages 762-776.
    9. Mehmet Balcilar & Rangan Gupta & Stephen M. Miller, 2015. "The out-of-sample forecasting performance of nonlinear models of regional housing prices in the US," Applied Economics, Taylor & Francis Journals, vol. 47(22), pages 2259-2277, May.
    10. Jonathan Berrisch & Florian Ziel, 2023. "Multivariate Probabilistic CRPS Learning with an Application to Day-Ahead Electricity Prices," Papers 2303.10019, arXiv.org, revised Feb 2024.
    11. Sucarrat, Genaro, 2009. "Forecast Evaluation of Explanatory Models of Financial Variability," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 3, pages 1-33.
    12. Massimiliano Marzo & Paolo Zagaglia, 2010. "Volatility forecasting for crude oil futures," Applied Economics Letters, Taylor & Francis Journals, vol. 17(16), pages 1587-1599.
    13. Mittnik, Stefan & Robinzonov, Nikolay & Spindler, Martin, 2015. "Stock market volatility: Identifying major drivers and the nature of their impact," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 1-14.
    14. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    15. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87, April.
    16. Naser, Hanan, 2016. "Estimating and forecasting the real prices of crude oil: A data rich model using a dynamic model averaging (DMA) approach," Energy Economics, Elsevier, vol. 56(C), pages 75-87.
    17. Vosen, Simeon & Schmidt, Torsten, 2012. "A monthly consumption indicator for Germany based on Internet search query data," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 19(7), pages 683-687.
    18. Marian Vavra, 2015. "On a Bootstrap Test for Forecast Evaluations," Working and Discussion Papers WP 5/2015, Research Department, National Bank of Slovakia.
    19. Rumler, Fabio & Valderrama, Maria Teresa, 2010. "Comparing the New Keynesian Phillips Curve with time series models to forecast inflation," The North American Journal of Economics and Finance, Elsevier, vol. 21(2), pages 126-144, August.
    20. Giot, Pierre & Petitjean, Mikael, 2007. "The information content of the Bond-Equity Yield Ratio: Better than a random walk?," International Journal of Forecasting, Elsevier, vol. 23(2), pages 289-305.

    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.