| Unit name | Econometrics with Python |
|---|---|
| Unit code | ECONM0014 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
| Unit director | Dr. Khatoon |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
None |
| Units you must take alongside this one (co-requisite units) |
Economic Analytics, Large Scale Data Engineering |
| Units you may not take alongside this one |
None |
| School/department | School of Economics |
| Faculty | Faculty of Social Sciences and Law |
Why is this unit important?
Econometrics is a discipline that sits at the intersection of economics, statistics and mathematics. It is the study of statistical methods that allow us to make predictions of economic quantities of interest, evaluate economic policies or firm strategies, and give empirical content to economic theory using real world data.
In a world where data availability increases dramatically every day, econometric reasoning paired with the computational power that Python can offer, becomes a very powerful tool to understand the world around us.
In this unit you will learn how to think about data and use it, programming skills in python to handle data and perform analysis.
The goal of the unit is to empower you, through knowledge of econometrics and Python programming, to become a sophisticated and critical consumer of data, better equipped to comprehend a nuanced and complex world.
How does this unit fit into your programme of study
This unit will harmonize students’ backgrounds in Econometrics and Python programming to meet a standard for following units in the program. It introduces the students to the fundamental techniques in statistical and causal analysis that will be thoroughly used in the TB2 units. It also familiarizes students with Python by linking each theoretical concept to a computational exercise. The Python programming basics will help to connect with another compulsory unit in TB1, Large Scale Data Engineering, and one in TB2, Machine Learning for Economics, and will be invaluable for the students to help with the data handling of the industry project dissertations.
An overview of content
This course integrates standard econometrics techniques with their implementation in the Python programming language. In the first part, students will learn programming skills in python to handle data and perform analysis. In the second part, students will learn prediction analysis and causal inference with experimental and observational data. The key topics include identification, OLS, omitted variable bias and instrumental variables techniques.
• Use of Python programming in structuring data: Branching and looping, functional programming, object oriented programming, Scientific Libraries-pandas, numpy, matplotlib
• Version control: Github setup and exploratory data analysis
• Prediction analysis via regression models (including penalization and endogeneity)
• Causal analysis via regression models
How will students, personally, be different as a result of the unit
Students will have an in-depth understanding of the link between statistical thinking and causality as it is used in economics. They will be confident in employing computational tools and programming in Python to apply the main econometric methods in practice.
Learning Outcomes
At the end of this unit you should be able to:
The unit will be taught using a combination of
Tasks which help you learn and prepare you for summative tasks (formative):
Lab sessions will be geared towards developing the same problem-solving techniques, programming and statistical and causal reasoning that will be needed for the Homework assignments.
Tasks which count towards your unit mark (summative):
When assessment does not go to plan
Reassessment will be through a single examination covering all ILOs.
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. ECONM0014).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
