IDEAS home Printed from https://ideas.repec.org/p/bis/biswps/1337.html

Introducing BISTRO: a foundational model for unconditional and conditional forecasting of macroeconomic time series

Author

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

Abstract

This article introduces the BIS Time-series Regression Oracle (BISTRO), a general purpose time series model for macroeconomic forecasting. Its edge over traditional econometric approaches lies in its ability to deal with generic unconditional and conditional forecasting tasks, without requiring to adjust the model to the macroe conomic tasks being tackled. Building on the transformer architecture underlying LLMs, BISTRO is fine-tuned on the large repository of macroeconomic data main tained at the BIS. We show that BISTRO provides reliable unconditional forecasts for key macroeconomic aggregates and illustrate how using it for conditional fore casting can help unveiling patterns of nonlinearity in the data.

Suggested Citation

  • 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.
    • Handle: RePEc:bis:biswps:1337
    as

    Download full text from publisher

    File URL: https://www.bis.org/publ/work1337.pdf
    File Function: Full PDF document
    Download Restriction: no
    File URL: https://www.bis.org/publ/work1337.htm
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Zarifhonarvar, Ali, 2026. "Generating inflation expectations with large language models," Journal of Monetary Economics, Elsevier, vol. 157(C).
    3. Xinli Yu & Zheng Chen & Yuan Ling & Shujing Dong & Zongyi Liu & Yanbin Lu, 2023. "Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting," Papers 2306.11025, arXiv.org.
    4. Andrea Carriero & Todd E. Clark & Marcellino Massimiliano, 2020. "Nowcasting Tail Risks to Economic Activity with Many Indicators," Working Papers 20-13R2, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
    5. 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.
    6. 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).
    7. 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.
    8. 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.
    9. 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. "BISTRO: a general purpose oracle for macroeconomic time series," BIS Quarterly Review, Bank for International Settlements, March.
    2. Alexander Eliseev & Sergei Seleznev, 2026. "Fake Date Tests: Can We Trust In-sample Accuracy of LLMs in Macroeconomic Forecasting?," Papers 2601.07992, arXiv.org, revised Mar 2026.
    3. Samuel Chang & Andrew Kennedy & Aaron Leonard & John A. List, 2024. "12 Best Practices for Leveraging Generative AI in Experimental Research," NBER Working Papers 33025, National Bureau of Economic Research, Inc.
    4. Feyzollahi, Maryam & Rafizadeh, Nima, 2025. "The adoption of Large Language Models in economics research," Economics Letters, Elsevier, vol. 250(C).
    5. Giuseppe Matera, 2025. "Corporate Earnings Calls and Analyst Beliefs," Papers 2511.15214, arXiv.org, revised Nov 2025.
    6. 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.
    7. Zhu, Dandan & Wang, Xiangdong & Zhang, Yifan, 2025. "Narrative monetary policy expectation in China," China Economic Review, Elsevier, vol. 94(PB).
    8. Nikoleta Anesti & Edward Hill & Andreas Joseph, 2025. "Inflation Attitudes of Large Language Models," Papers 2512.14306, arXiv.org.
    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. 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).
    11. 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.
    12. Eleonora Granziera & Vegard H. Larsen & Greta Meggiorini & Leonardo Melosi, 2025. "Speaking of Inflation: The Influence of Fed Speeches on Expectations," CESifo Working Paper Series 11992, CESifo.
    13. Iñaki Aldasoro & Ajit Desai, 2025. "Money Talks: AI Agents for Cash Management in Payment Systems," Staff Working Papers 25-35, Bank of Canada.
    14. Felipe A. Csaszar & Harsh Ketkar & Hyunjin Kim, 2024. "Artificial Intelligence and Strategic Decision-Making: Evidence from Entrepreneurs and Investors," Strategy Science, INFORMS, vol. 9(4), pages 322-345, December.
    15. Thierry Warin & Aleksandar Stojkov, 2023. "“Decoding” Policy Perspectives: Structural Topic Modeling of European Central Bankers’ Speeches," JRFM, MDPI, vol. 16(7), pages 1-23, July.
    16. Baumgärtner, Martin & Zahner, Johannes, 2025. "Whatever it takes to understand a central banker — Embedding their words using neural networks," Journal of International Economics, Elsevier, vol. 157(C).
    17. Francesco Venturini, 2025. "Generative AI and Income Growth: Early Evidence on Global Data," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 3, pages 31-46.
    18. Cristina Angelico & Enrico Bernardini, 2026. "Can GenAI fill banks' emissions data gaps?," Questioni di Economia e Finanza (Occasional Papers) 1003, Bank of Italy, Economic Research and International Relations Area.
    19. Renzhi Li & Caihong Chen & Zhijie Han & Yuwei Wang, 2025. "Targeted monetary policy, SMEs’ loan availability, and corporate investment: evidence from China," Small Business Economics, Springer, vol. 65(4), pages 2465-2491, December.
    20. Deborah Miori & Constantin Petrov, 2023. "Narratives from GPT-derived Networks of News, and a link to Financial Markets Dislocations," Papers 2311.14419, arXiv.org.

    More about this item

    Keywords

    ;
    ;
    ;

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:biswps:1337. 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.