Scalable Econometrics on Big Data -- The Logistic Regression on Spark
Computation
2021-06-22 v1 Econometrics
Abstract
Extra-large datasets are becoming increasingly accessible, and computing tools designed to handle huge amount of data efficiently are democratizing rapidly. However, conventional statistical and econometric tools are still lacking fluency when dealing with such large datasets. This paper dives into econometrics on big datasets, specifically focusing on the logistic regression on Spark. We review the robustness of the functions available in Spark to fit logistic regression and introduce a package that we developed in PySpark which returns the statistical summary of the logistic regression, necessary for statistical inference.
Cite
@article{arxiv.2106.10341,
title = {Scalable Econometrics on Big Data -- The Logistic Regression on Spark},
author = {Aurélien Ouattara and Matthieu Bulté and Wan-Ju Lin and Philipp Scholl and Benedikt Veit and Christos Ziakas and Florian Felice and Julien Virlogeux and George Dikos},
journal= {arXiv preprint arXiv:2106.10341},
year = {2021}
}