BISTRO: a general purpose oracle for macroeconomic time series

Batuhan Koyuncu, Byeungchun Kwon, Marco Jacopo Lombardi, Fernando Perez-Cruz and Hyun Song Shin

BIS Quarterly Review, 2026

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.

JEL-codes: C32 C45 C55 C87 (search for similar items in EconPapers)
Date: 2026
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