Accessible Unlicensed Requires Authentication Published by De Gruyter October 16, 2020

Econometrics Pedagogy and Cloud Computing: Training the Next Generation of Economists and Data Scientists

Danielle V. Handel, Anson T. Y. Ho, Kim P. Huynh, David T. Jacho-Chávez and Carson H. Rea

Abstract

This paper describes how cloud computing tools widely used in the instruction of data scientists can be introduced and taught to economics students as part of their curriculum. The demonstration centers around a workflow where the instructor creates a virtual server and the students only need Internet access and a web browser to complete in-class tutorials, assignments, or exams. Given how prevalent cloud computing platforms are becoming for data science, introducing these techniques into students’ econometrics training would prepare them to be more competitive when job hunting, while making instructors and administrators re-think what a computer laboratory means on campus.

JEL Classification: A11; A22; A23; C87; C88

Corresponding author: Kim P. Huynh, Bank of Canada, 234 Wellington Ave., Ottawa, ON, K1A 0G9, Canada, E-mail:

Funding source: Emory University

Acknowledgments

We thank the editor and an anonymous referee for helpful comments that improve the readability and exposition of the paper. We also thank Amazon Web Services (AWS) Educate and Stata Corporation for providing us with a cloud classroom with credits and temporary Stata lab licenses for testing, respectively. AWS is Emory University’s preferred and recommended cloud service for faculty-led computational needs. Handel, Jacho-Chávez, and Rea acknowledge financial support from the Department of Economics at Emory University. The views expressed in this article are those of the authors. No responsibility for them should be attributed to the Bank of Canada. All remaining errors are the responsibility of the authors.

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Received: 2020-07-24
Accepted: 2020-09-22
Published Online: 2020-10-16

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