EF308 - Econometrics and Forecasting
Module Assignment
This assessment comprises 100% of your assessment for the Econometrics and Forecasting module. It consists of two parts:
- Group work equal to 40% of the module grade
- Individual report equal to 60% of the module grade
For this assignment you are asked to imagine a new financial advisory product aimed at students and produce the data analytics suitable for supporting the launch of the product.
Deadlines:
Group Work: due April 11th, 7pm.
Individual Report: due April 11th, 7pm.
The product: Your proposed product can be anything at all that is new, but ‘new’ doesn’t mean that it is completely new, it might just be an iteration or improvement of an existing service that is already offered. You won’t really be judged explicitly on the quality of the idea, but it does speak to the overall interest of your project. Illustrate your proposed product with relevant screenshots and visuals, as you see fit, in order to build a story here. Build a launch story about the product - essentially, breathe a bit of life into your proposed product so that it feels real.
The data: You could either mock up data using Generative AI or use kaggle.com to identify a suitable pre-existing dataset. The dataset doesn’t need to be perfectly aligned, as usually that isn’t possible. But more that it is reasonably aligned to your topic. For example, if you propose an app to allow students to save for holidays, you might use a holiday booking dataset. If you propose to sell credit cards to students, you might use a credit card default-rate dataset. Don’t worry if the dataset doesn’t perfectly match - if it is not too close, your presentation might simply say something along the lines of ‘while we are still trying to identify useful data, here is an analysis of some related data …’. Thats all thats needed.
The data task: The data task is the core learning and assessment point of the work. Broadly speaking you are asked to use a combination of skills learned across the classes. This includes: data cleaning (what we do in the first two classes, and bits of all the other classes); regression analysis (building a model that explains how the data works); and, perhaps, forecasting (incorporating some aspect of forecasting, or looking into the future in your analysis).
What to submit:
1. For the group project (40%): Submit a ten-minute video pitching your product idea. An approximate structure for this might look like: about 5 slides outlining the problem you are solving, how your product idea solves it, and what data investigations you carried out to develop the product idea. You might also want to include a working Streamlit file of your product, in order to ‘demo’ it.
2. For the individual assignment (60%): A well-formatted report on the proposed investment of 1,500 words length, along with a link to a Google Colab coding file. It is acceptable that there is shared content between the group members - especially in terms of coding and the overall idea - but there should be an identifiable element of the content that is your own work. The report should look professional: give good attention to visuals etc and look at professional reports e.g. from consulting houses like McKinsey to get an idea on how to structure. The coding file simply needs to outline the steps you took to analyse the data from start to finish. It doesn’t need a story or text, I can follow the format. The same coding file can be submitted by all group members, if you wish.
Use of ChatGPT and similar models: Feel free to use any amount of Generative AI assistance as you wish.