Template-Type: ReDIF-Paper 1.0
Author-Name: Douglas Kiarelly Godoy de Araujo
Author-X-Name-First: Douglas Kiarelly Godoy
Author-X-Name-Last: de Araujo
Title: gingado: a machine learning library focused on economics and finance
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.
Creation-Date: 2023-09
File-URL: https://www.bis.org/publ/work1122.pdf
File-Format: Application/pdf
File-Function: Full PDF document
File-URL: https://www.bis.org/publ/work1122.htm
File-Format: text/html
Number: 1122
Keywords: machine learning, open source, data access, documentation
Classification-JEL: C87, C14, C82
Handle: RePEc:bis:biswps:1122