Efficiency Analysis in Python: A Hands-On Tutorial on Stochastic Frontier Analysis and Data Envelopment Analysis

This session provides an introduction to efficiency and productivity analysis methods for researchers and practitioners. Key topics include the theoretical foundations, assumptions, use cases, and differences between Stochastic Frontier Analysis (SFA) and Data Envelopment Analysis (DEA), as well as hands-on examples in Python covering model specification, estimation, and interpretation.
Published

September 29, 2025

Efficiency Analysis in Python: A Hands-On Tutorial on Stochastic Frontier Analysis and Data Envelopment Analysis

September 29, 01:30 pm

This tutorial introduces participants to two cornerstone methods in efficiency and productivity analysis: Stochastic Frontier Analysis (SFA) and Data Envelopment Analysis (DEA). Aimed at researchers and practitioners in economics, operations research, and analytics, the session combines conceptual grounding with practical implementation in Python.

We will begin with an overview of the theoretical foundations of SFA and DEA, highlighting their assumptions, use cases, and differences. The tutorial then walks through hands-on examples using Python libraries, covering model specification, estimation, and interpretation of results. No prior experience with SFA or DEA is required, but familiarity with basic regression and Python programming will be beneficial.

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Jessica Leung

Dr Jess Leung is a lecturer in Business Analytics at the Department of Econometrics and Business Statistics, specialising in optimisation and decision analytics. She is a Certified Analytics Professional - Expert (CAP-X) and Senior Fellow of the Higher Education Academy (SFHEA), with extensive experience in curriculum design and assessment development.