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gingado: a machine learning library focused on economics and finance

Author

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  • Douglas Kiarelly Godoy de Araujo

Abstract

gingado is an open source Python library that offers a variety of convenience functions and objects to support usage of machine learning in economics research. It is designed to be compatible with widely used machine learning libraries. gingado facilitates augmenting user datasets with relevant data directly obtained from official sources by leveraging the SDMX data and metadata sharing protocol. The library also offers a benchmarking object that creates a random forest with a reasonably good performance out-of-the-box and, if provided with candidate models, retains the one with the best performance. gingado also includes methods to help with machine learning model documentation, including ethical considerations. Further, gingado provides a flexible simulatation of panel datasets with a variety of non-linear causal treatment effects, to support causal model prototyping and benchmarking. The library is under active development and new functionalities are periodically added or improved.

Suggested Citation

  • Douglas Kiarelly Godoy de Araujo, 2023. "gingado: a machine learning library focused on economics and finance," BIS Working Papers 1122, Bank for International Settlements.
    • Handle: RePEc:bis:biswps:1122
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Douglas Kiarelly Godoy de Araujo, 2024. "Synthetic controls with machine learning: application on the effect of labour deregulation on worker productivity in Brazil," BIS Working Papers 1181, Bank for International Settlements.
    2. Zheng, Yuqi & Lucey, Brian, 2025. "Talking Trump and tanking markets," Finance Research Letters, Elsevier, vol. 85(PA).
    3. Bilyana Bogdanova & Brian Buffett & Bianca Ligani & Ismail Mustafi & Stratos Nikoloutsos & Rafael Schmidt & Bruno Tissot, 2025. "SDMX adoption and use of open source tools," IFC Reports 17, Bank for International Settlements.
    4. Douglas Araujo & Brian Buffett, 2026. "sdmx.io: where tools and learning resources come together to address official statistics use cases," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Data science in central banking, volume 67, Bank for International Settlements.
    5. Rahaman, Shafeeq Ur & Abdul, Mahe Jabeen, 2025. "Quantifying uncertainty in economics policy predictions: A Bayesian & Monte Carlo based data-driven approach," International Review of Financial Analysis, Elsevier, vol. 102(C).
    6. Douglas Kiarelly Godoy de Araujo & Carlos Cantú & Allan Chinchilla & Cecilia Franco & Jon Frost & Andrea Oconitrillo, 2024. "Fast payments and banking: Costa Rica's SINPE Móvil," BIS Papers chapters, in: Bank for International Settlements (ed.), Faster digital payments: global and regional perspectives, volume 127, pages 45-60, Bank for International Settlements.
    7. 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.
    8. Zheng, Yuqi & Lucey, Brian, 2025. "Central bankers’ political discourse as a driver of clean energy markets," Research in International Business and Finance, Elsevier, vol. 80(C).
    9. Douglas Kiarelly Godoy de Araujo, 2025. "Open-sourced central bank macroeconomic models," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Data science in central banking: enhancing the access to and sharing of data, volume 64, Bank for International Settlements.

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    JEL classification:

    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access

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