IDEAS home Printed from https://ideas.repec.org/a/bis/bisqtr/2603d.html

BISTRO: a general purpose oracle for macroeconomic time series

Author

Listed:
  • Batuhan Koyuncu
  • Byeungchun Kwon
  • Marco Jacopo Lombardi
  • Fernando Perez-Cruz
  • Hyun Song Shin

Abstract

Predictions of macroeconomic variables are a key input to economic policy, yet traditional econometric approaches have the limitation that the model needs to be tailored to the specific task. The advent of large language models (LLMs) opens up the tantalising prospect that a single general model can tackle a wide variety of tasks. This article introduces the BIS Time-series Regression Oracle (BISTRO), a general purpose time series model for macroeconomic forecasting. Building on the transformer architecture underlying LLMs, BISTRO is fine-tuned on the large repository of macroeconomic data maintained at the BIS. We put the model through its paces by assessing how well it forecasts the 2021 inflation surge. In contrast to standard benchmarks, which mechanically project a reversion to the mean, BISTRO correctly anticipates the persistence of the inflation wave. This highlights its ability to adapt to unfamiliar patterns in the data. Thus, BISTRO holds promise for producing reliable baseline forecasts and for scenario analysis.

Suggested Citation

  • Batuhan Koyuncu & Byeungchun Kwon & Marco Jacopo Lombardi & Fernando Perez-Cruz & Hyun Song Shin, 2026. "BISTRO: a general purpose oracle for macroeconomic time series," BIS Quarterly Review, Bank for International Settlements, March.
  • Handle: RePEc:bis:bisqtr:2603d
    as

    Download full text from publisher

    File URL: https://www.bis.org/publ/qtrpdf/r_qt2603d.pdf
    Download Restriction: no

    File URL: https://www.bis.org/publ/qtrpdf/r_qt2603d.htm
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jens Ludwig & Sendhil Mullainathan, 2024. "Machine Learning as a Tool for Hypothesis Generation," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 139(2), pages 751-827.
    2. Federico Siano, 2025. "The News in Earnings Announcement Disclosures: Capturing Word Context Using LLM Methods," Management Science, INFORMS, vol. 71(11), pages 9831-9855, November.
    3. Anton Korinek, 2023. "Generative AI for Economic Research: Use Cases and Implications for Economists," Journal of Economic Literature, American Economic Association, vol. 61(4), pages 1281-1317, December.
    4. Aldasoro, Inaki & Hördahl, Peter & Schrimpf, Andreas & Zhu, Sonya, 2025. "Predicting Financial Market Stress with Machine Learning," CEPR Discussion Papers 20439, Centre for Economic Policy Research.
    5. Stephen G. Hall & George S. Tavlas & Yongli Wang, 2023. "Forecasting inflation: The use of dynamic factor analysis and nonlinear combinations," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 514-529, April.
    6. Kwon, Byeungchun & Park, Taejin & Rungcharoenkitkul, Phurichai & Smets, Frank, 2025. "Parsing the Pulse: Decomposing Macroeconomic Sentiment with LLMs," CEPR Discussion Papers 20828, Centre for Economic Policy Research.
    7. Yuriy Gorodnichenko & Tho Pham & Oleksandr Talavera, 2023. "The Voice of Monetary Policy," American Economic Review, American Economic Association, vol. 113(2), pages 548-584, February.
    8. Cao, Sean & Jiang, Wei & Wang, Junbo & Yang, Baozhong, 2024. "From Man vs. Machine to Man + Machine: The art and AI of stock analyses," Journal of Financial Economics, Elsevier, vol. 160(C).
    9. Aquilina, Matteo & Araujo, Douglas & Gelos, Gaston & Park, Taejin & Perez-Cruz, Fernando, 2025. "Harnessing Artificial Intelligence for Monitoring Financial Markets," CEPR Discussion Papers 20768, Centre for Economic Policy Research.
    10. Faust, Jon & Wright, Jonathan H., 2013. "Forecasting Inflation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 2-56, Elsevier.
    11. Byeungchun Kwon & Taejin Park & Fernando Perez-Cruz & Phurichai Rungcharoenkitkul, 2024. "Large language models: a primer for economists," BIS Quarterly Review, Bank for International Settlements, December.
    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. Batuhan Koyuncu & Byeungchun Kwon & Marco Jacopo Lombardi & Fernando Perez-Cruz & Hyun Song Shin, 2026. "Introducing BISTRO: a foundational model for unconditional and conditional forecasting of macroeconomic time series," BIS Working Papers 1337, Bank for International Settlements.
    2. John R. Graham & Campbell R. Harvey & Manish Jha, 2026. "CFOs Meet LLMs," Papers 2606.13812, arXiv.org.
    3. Chang, Samuel & Kennedy, Andrew & Leonard, Aaron & List, John A., 2026. "12 best practices for leveraging Generative AI in experimental research," Journal of Economic Behavior & Organization, Elsevier, vol. 246(C).
    4. Firmin Ayivodji & Etienne Briand & Kevin Moran & Dalibor Stevanovic, 2026. "Monetary Policy in the Media Spotlight: Sentiments, Signals, and Economic Impact," Working Papers 26-03, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    5. Feyzollahi, Maryam & Rafizadeh, Nima, 2025. "The adoption of Large Language Models in economics research," Economics Letters, Elsevier, vol. 250(C).
    6. Giuseppe Matera, 2025. "Corporate Earnings Calls and Analyst Beliefs," Papers 2511.15214, arXiv.org, revised Nov 2025.
    7. SEKINE, Toshitaka & WADA, Tetsuro, 2025. "How Did People Tweet against Inflation in Japan?," Discussion paper series HIAS-E-150, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    8. Zhu, Dandan & Wang, Xiangdong & Zhang, Yifan, 2025. "Narrative monetary policy expectation in China," China Economic Review, Elsevier, vol. 94(PB).
    9. Tao Chen & Shuwen Pi & Qing Sophie Wang, 2025. "Artificial Intelligence and Corporate Investment Efficiency: Evidence from Chinese Listed Companies," Working Papers in Economics 25/05, University of Canterbury, Department of Economics and Finance.
    10. Ahmed, M. Iqbal & Cassou, Steven P., 2021. "Asymmetries in the effects of unemployment expectation shocks as monetary policy shifts with economic conditions," Economic Modelling, Elsevier, vol. 100(C).
    11. Stephen G. Hall & George S. Tavlas & Yongli Wang, 2026. "Forecasting Inflation in the Presence of Structural Breaks: A Time‐Varying Parameter Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 45(4), pages 2102-2111, July.
    12. Nason, Guy P. & Palasciano, Henry Antonio, 2026. "Forecasting UK consumer price inflation with RaGNAR: Random generalised network autoregressive processes," International Journal of Forecasting, Elsevier, vol. 42(1), pages 181-202.
    13. Cheng, Qiang & Lin, Pengkai & Zhao, Yue, 2025. "Does generative AI facilitate investor Trading? Early evidence from ChatGPT outages," Journal of Accounting and Economics, Elsevier, vol. 80(2).
    14. Dangxing Chen & Pengzhan Guo, 2026. "Shapley in Context: Explaining Financial Language with Domain Expertise," Papers 2607.00856, arXiv.org.
    15. Joseph, Andreas & Potjagailo, Galina & Chakraborty, Chiranjit & Kapetanios, George, 2024. "Forecasting UK inflation bottom up," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1521-1538.
    16. Dimitrios Kanelis & Pierre L. Siklos, 2025. "The ECB press conference statement: deriving a new sentiment indicator for the euro area," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 30(1), pages 652-664, January.
    17. Granziera, Eleonora & Larsen, Vegard H. & Meggiorini, Greta & Melosi, Leonardo, 2025. "Speaking of Inflation: The Influence of Fed Speeches on Expectations," CEPR Discussion Papers 20038, Centre for Economic Policy Research.
    18. Adriana Cornea‐Madeira & João Madeira, 2022. "Econometric Analysis of Switching Expectations in UK Inflation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(3), pages 651-673, June.
    19. Iñaki Aldasoro & Ajit Desai, 2025. "Money Talks: AI Agents for Cash Management in Payment Systems," Staff Working Papers 25-35, Bank of Canada.
    20. Kevin Bauer & Andreas Grunewald & Florian Hett & Johanna Jagow & Maximilian Speicher, 2026. "Treatment Targeting by Scaled Behavioral Measurement," CESifo Working Paper Series 12772, CESifo.

    More about this item

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

    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:bis:bisqtr:2603d. 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: Martin Fessler (email available below). General contact details of provider: https://edirc.repec.org/data/bisssch.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.