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Python for Unified Research in Econometrics and Statistics

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  • Roseline Bilina
  • Steve Lawford

Abstract

Python is a powerful high-level open source programming language that is available for multiple platforms. It supports object-oriented programming and has recently become a serious alternative to low-level compiled languages such as C + +. It is easy to learn and use, and is recognized for very fast development times, which makes it suitable for rapid software prototyping as well as teaching purposes. We motivate the use of Python and its free extension modules for high performance stand-alone applications in econometrics and statistics, and as a tool for gluing different applications together. (It is in this sense that Python forms a “unified” environment for statistical research.) We give details on the core language features, which will enable a user to immediately begin work, and then provide practical examples of advanced uses of Python. Finally, we compare the run-time performance of extended Python against a number of commonly-used statistical packages and programming environments. Supplemental materials are available for this article. Go to the publisher's online edition of Econometric Reviews to view the free supplemental file.

Suggested Citation

  • Roseline Bilina & Steve Lawford, 2012. "Python for Unified Research in Econometrics and Statistics," Econometric Reviews, Taylor & Francis Journals, vol. 31(5), pages 558-591, September.
    • Handle: RePEc:taf:emetrv:v:31:y:2012:i:5:p:558-591
    DOI: 10.1080/07474938.2011.553573
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    2. Roger Koenker & Achim Zeileis, 2009. "On reproducible econometric research," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(5), pages 833-847.
    3. Christine Choirat & Raffello Seri, 2009. "Econometrics with Python," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 698-704.
    4. Zeileis, Achim & Koenker, Roger, 2008. "Econometrics in R: Past, Present, and Future," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i01).
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    Cited by:

    1. Valérie Orozco & Christophe Bontemps & Élise Maigné & Virginie Piguet & Annie Hofstetter & Anne Marie Lacroix & Fabrice Levert & Jean-Marc Rousselle, 2017. "How to make a pie? Reproducible Research for Empirical Economics & Econometrics," Post-Print hal-01939942, HAL.
    2. Valérie Orozco & Christophe Bontemps & Elise Maigné & Virginie Piguet & Annie Hofstetter & Anne Lacroix & Fabrice Levert & Jean‐Marc Rousselle, 2020. "How To Make A Pie: Reproducible Research For Empirical Economics And Econometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 34(5), pages 1134-1169, December.

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