The
Productivity, Efficiency and Measurement Analytics (PEMA) research group at the University of Sydney Business School invite you to this professional development workshop showcasing three days on highly practical and modern methods for productivity and efficiency analysis using Python with applications to various economic agents (e.g., departments, firms, industries, regions, countries, public sectors, etc.). The workshop will cover introduction to Python and the key methods in Data Development Analysis (DEA) and Stochastic Frontier Analysis (SFA).
| Date: |
Thursday 7 November - Saturday 9 November 2024 |
| Time: |
9:00am - 5:00pm |
| Venue: |
The University of Sydney Business School CBD Campus, Level 17, |
| |
133 Castlereagh Street, Sydney, NSW 2000 |
| Mode of Delivery: |
Hybrid; remote participation is possible at the same rate; |
| |
zoom link to be provided after registration. |
| Items Required: |
Your own laptop. |
| |
Extension cords, power boards, WiFi will be provided for in-person |
| |
participants, all required Python packages will be provided. |
Day One: Introduction to Python, by
Dr Jessica Leung, Faculty of Business and Economics, Monash University
- First day of the workshop will introduce the participants to one of the most popular programming languages, Python, used for the analysis of productivity and efficiency in the following two days.
- This day is composed of four sessions which will cover:
- Installation of Python and the necessary packages used in DEA and SFA
- Basic operations with functions contained in those packages, such as how to use matplotlib, script.optimise, statsmodels, sklearn.linear_model
- Additional fundamental topics, such as writing functions in Python and understanding data structures etc.
- No assumed knowledge of Python is required for this day.
- The sessions will prepare the participants for seamless entry into the more advanced coding used in the next two days of the workshop.
- By the end of the day you shall be able to write and run codes in Python and have the packages required to run DEA and SFA.
Day Two: Production theory and data envelopment analysis, by
Professor Valentin Zelenyuk, School of Economics, University of Queensland
- This day assumes basic knowledge of economics, statistics, and mathematics and working knowledge of Python equivalent to Day 1.
- The day is composed of four sessions. Three of them will form the conceptual and mathematical basis for DEA:
- The 1st session is focused on economics foundations for productivity & efficiency such as Shephard's axiomatic approach to production theory, with applications to real life scenarios.
- Sessions 2 and 3 will covers the basics and advanced topics in DEA, with real life business productivity applications.
- The 4th session will contain Python-based tutorial helping participant practice the concepts and DEA estimation techniques using real-life data.
- By the end of the day you shall understand the main concepts of the production theory and the related theory of efficiency and productivity analysis, and apply them via different types of (DEA estimators and perform related statistical analysis in Python.
Day Three: Stochastic frontier analysis, by
Professor Artem Prokhorov, Discipline of Business Analytics, University of Sydney
- This day assumes basic knowledge of economics, statistics, and mathematics and working knowledge of Python equivalent to Day 1.
- The day is composed of four sessions. Three of them will form the conceptual and mathematical basis for SFA:
- We start with estimation of cross-sectional stochastic frontier models with multiple inputs.
- Then, we include environmental variables that affect inefficiency scores, allow for time varying technical inefficiency and for unobserved effects and we consider estimation of stochastic frontier models using panel data.
- Finally, we consider estimation of stochastic frontier models when production inputs are endogenous. We cover applications in agriculture, power generation, efficiency of airlines, mine production and banks.
- The 4th session will contain Python based tutorial helping participant to practice the concepts and SFA estimation methods using real-life data.
- By the end of the day, you will be able to estimate stochastic frontier models, test hypotheses about them, interpret the estimates and obtain efficiency scores.
REGISTRATION
You can choose to attend either one, two or three days
in person or via zoom. Fees include course material, codes and data sets, and catering throughout the days. Please note fees for remote participation is the same as for in-person participation.
There are limited places available, register today to secure your spot!Early Bird Registration:Numbers are limited and places are reserved on a first-come first-served basis. Due to limited places, PEMA maintains a no refund policy. Net proceedings from the workshop go to funding PEMA PhD scholarships.