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Scalable Econometrics on Big Data -- The Logistic Regression on Spark

Author

Listed:
  • Aur'elien Ouattara
  • Matthieu Bult'e
  • Wan-Ju Lin
  • Philipp Scholl
  • Benedikt Veit
  • Christos Ziakas
  • Florian Felice
  • Julien Virlogeux
  • George Dikos

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.

Suggested Citation

  • Aur'elien Ouattara & Matthieu Bult'e & Wan-Ju Lin & Philipp Scholl & Benedikt Veit & Christos Ziakas & Florian Felice & Julien Virlogeux & George Dikos, 2021. "Scalable Econometrics on Big Data -- The Logistic Regression on Spark," Papers 2106.10341, arXiv.org.
    • Handle: RePEc:arx:papers:2106.10341
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    References listed on IDEAS

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    4. Hal Varian, 2018. "Artificial Intelligence, Economics, and Industrial Organization," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 399-419, National Bureau of Economic Research, Inc.
    5. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    6. Fernández-Villaverde, Jesús & Zarruk Valencia , David, 2018. "A Practical Guide to Parallelization in Economics," CEPR Discussion Papers 12890, C.E.P.R. Discussion Papers.
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