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Teaching Reproducibility and Replicability While Teaching Econometrics in the Classroom

Author

Listed:
  • Anson T. Y. Ho

    (Toronto Metropolitan University, Ted Rogers School of Management)

  • Kim P. Huynh

    (Department of Economics, Indiana University)

  • David T. Jacho-Chávez

    (Emory University, Department of Economics)

  • Katie Leinenbach

    (Emory University, Department of Economics)

  • Carson H. Rea

    (Bates White, LLC)

Abstract

This research discusses how reproducibility and replicability can be taught to economists and social scientists while learning econometrics. Instructors can use standard tools from data science and machine learning to teach classical undergraduate Econometrics curriculum. This paper emphasizes the usage of self-contained computing environments for students to complete and submit their econometric practice exercises using open-source software. Demonstrations highlight how instructors can create computer-based assignments that can be distributed electronically to students, and how researchers can easily replicate and reproduce research using the same tools. For students, assignments are accompanied by code that automatically deploys a computing environment in the cloud where the assignment can be completed without the need for further software installation or a hardware upgrade. This teaches students how to prepare their work to be reproducible and replicable.

Suggested Citation

  • Anson T. Y. Ho & Kim P. Huynh & David T. Jacho-Chávez & Katie Leinenbach & Carson H. Rea, 2026. "Teaching Reproducibility and Replicability While Teaching Econometrics in the Classroom," Advanced Studies in Theoretical and Applied Econometrics,, Springer.
    • Handle: RePEc:spr:adschp:978-3-031-97942-2_7
    DOI: 10.1007/978-3-031-97942-2_7
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