ECONOMETRICS AND MACHINE LEARNING IN BUSINESS AND ECONOMICS EDUCATION: FACTS AND A GUIDELINE ON TEACHING PRACTICES
Abstract
Econometrics, and related courses, are often thought of as the most challenging courses for many undergraduate economics, business, and management students. Using a large international dataset of business and economics syllabi, I show an upward trajectory in including machine learning topics within business syllabi, with a discernible shift of emphasis from econometrics topics. With the growing number of undergraduate students from diverse backgrounds, there is a growing need to improve the teaching of econometrics and make it more inclusive and applicable. I discuss and formalize actionable guidelines for practices and interventions that can improve econometrics teaching and make it accessible and relevant to increasingly diverse students in economics, business, and management schools.
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