This module develops your ability to solve economics problems and to analyse economic data using computational techniques. It will teach you to apply numerical optimisation methods to a range of economics and econometrics problems, develop an understanding of numerical and computational methods through their practical applications, and develop an ability to assess the strengths and weaknesses of different methods for different applications. The module builds upon the Level 4 modules Introduction to Object Orientated Programming (CO320), and Programming for Artificial Intelligence (Python programming) (CO359) and will further develop students' understanding of programming languages commonly used in economic analysis, including at least one of Python, R and/or Julia.
Total contact hours: 24
Private study hours: 126
Total study hours: 150
Compulsory to the following courses:
• BSc Economics with Data Science
Optional to the following courses:
• all single honours degree programmes in Economics
Method of assessment
Main Assessment Methods:
• Coding exercises, 4x 15% (60% total)
• Group project of 3000 words (30% total)
• Group project presentation (15 minutes) (10%)
Reassessment: 100% project
The most up to date reading list for each module can be found on the university's reading list pages (https://kent.rl.talis.com/index.html).
• Doing Economics (https://www.core-econ.org/project/doing-economics/)
• Quantecon (https://quantecon.org/) Quantitative Economics Undergraduate Course
• Quantitative Economics with Python (https://python.quantecon.org/)
• Quantitative Economics with Julia (https://julia.quantecon.org/)
• Additional documentation and readings based on specific topics and software to be published annually.
Subject specific learning outcomes.
On successfully completing the module you will be able to:
1. Apply numerical optimization methods to a range of economics and econometrics problems
2. Understand foundational methods in economic modelling and computational economics
3. Understand foundational methods in coding for economic analysis, standard methods for analysing large data sets
4. Formulate, solve and critically analyse problems in economics using a range of computational methods
5. Identify and develop understanding of programming languages commonly used in economics such as Python, R, and Julia
6. Develop and apply economic modelling skills for industry and policy analysis using industry platforms
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