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BeforeIT.jl: High-Performance Agent-Based Macroeconomics Made Easy

Author

Listed:
  • Aldo Glielmo
  • Mitja Devetak
  • Adriano Meligrana
  • Sebastian Poledna

Abstract

BeforeIT is an open-source software for building and simulating state-of-the-art macroeconomic agent-based models (macro ABMs) based on the recently introduced macro ABM developed in [1] and here referred to as the base model. Written in Julia, it combines extraordinary computational efficiency with user-friendliness and extensibility. We present the main structure of the software, demonstrate its ease of use with illustrative examples, and benchmark its performance. Our benchmarks show that the base model built with BeforeIT is orders of magnitude faster than a Matlab version, and significantly faster than Matlab-generated C code. BeforeIT is designed to facilitate reproducibility, extensibility, and experimentation. As the first open-source, industry-grade software to build macro ABMs of the type of the base model, BeforeIT can significantly foster collaboration and innovation in the field of agent-based macroeconomic modelling. The package, along with its documentation, is freely available at https://github.com/bancaditalia/BeforeIT.jl under the AGPL-3.0.

Suggested Citation

  • Aldo Glielmo & Mitja Devetak & Adriano Meligrana & Sebastian Poledna, 2025. "BeforeIT.jl: High-Performance Agent-Based Macroeconomics Made Easy," Papers 2502.13267, arXiv.org.
  • Handle: RePEc:arx:papers:2502.13267
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    References listed on IDEAS

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    1. Poledna, Sebastian & Miess, Michael Gregor & Hommes, Cars & Rabitsch, Katrin, 2023. "Economic forecasting with an agent-based model," European Economic Review, Elsevier, vol. 151(C).
    2. Herbert Dawid & Domenico Delli Gatti & Luca Eduardo Fierro & Sebastian Poledna, 2024. "Implications of Behavioral Rules in Agent-Based Macroeconomics," CESifo Working Paper Series 11411, CESifo.
    3. Cars Hommes & Sebastian Poledna, 2023. "Analyzing and forecasting economic crises with an agent-based model of the euro area," Tinbergen Institute Discussion Papers 23-013/II, Tinbergen Institute.
    4. Federico D’Ambrosio & Hans L. Bodlaender & Gerard T. Barkema, 2022. "Dynamic sampling from a discrete probability distribution with a known distribution of rates," Computational Statistics, Springer, vol. 37(3), pages 1203-1228, July.
    5. Baptista, Rafa & Farmer, J. Doyne & Hinterschweiger, Marc & Low, Katie & Tang, Daniel & Uluc, Arzu, 2016. "Macroprudential policy in an agent-based model of the UK housing market," Bank of England working papers 619, Bank of England.
    6. Giovanni Dosi & Andrea Roventini, 2019. "More is different ... and complex! the case for agent-based macroeconomics," Journal of Evolutionary Economics, Springer, vol. 29(1), pages 1-37, March.
    7. Samuel Wiese & Jagoda Kaszowska-Mojsa & Joel Dyer & Jose Moran & Marco Pangallo & Francois Lafond & John Muellbauer & Anisoara Calinescu & J. Doyne Farmer, 2024. "Forecasting Macroeconomic Dynamics using a Calibrated Data-Driven Agent-based Model," Papers 2409.18760, arXiv.org.
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