Gingado
gingado, is an easy-to-use open source ML library organized by BIS with five main contributions:
- First, it helps users automatically acquire hundreds of time series from a number of official statistical sources.
- Second, it offers automatic benchmark models based on random forests that perform well off the shelf in a variety of cases but can be easily customised by users.
- Third, gingado lets users flexibly simulate large and multidimensional panel data with linear and non-linear causal relationships in the data-generating processes.
- Fourth, it facilitates model documentation based on ML best practices, including ethical considerations about model usage when applicable.
- Fifth, it features auxiliary utilities for time series data analysis.
gingado’s instructions, documentation, practical examples and source code are available at https://dkgaraujo.github.io/gingado/.
Paper: gingado: a machine learning library focused on economics and finance
Author: Douglas K G Araujo
From: BIS
From the 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.
